Academy of Computer Science & Management in Bielsko Biata Entrance Exam Set

The core of this question lies in understanding the fundamental principles of data integrity and the role of hashing in ensuring it, particularly in the context of distributed systems and version control, which are critical areas for students entering the Academy of Computer Science & Management in Bielsko Biata. Consider a scenario where a file’s content is modified. A cryptographic hash function, like SHA-256, takes the entire content of the file as input and produces a fixed-size output, the hash value. If even a single bit of the file is altered, the resulting hash value will be drastically different due to the avalanche effect inherent in good hash functions. This makes the hash a unique digital fingerprint of the file’s content at a specific point in time. When a system needs to verify the integrity of a file, it recalculates the hash of the current file and compares it to a previously stored, trusted hash value. If the hashes match, it indicates that the file has not been tampered with or corrupted. If they don’t match, it signals that the file has been altered. The question asks about the most effective method to detect unauthorized modifications to a digital asset within a system that prioritizes data integrity. Option A, using a checksum derived from a robust cryptographic hash function (like SHA-256), directly addresses this by providing a unique and sensitive fingerprint that changes significantly with even minor alterations. This aligns with the Academy’s emphasis on secure computing and data management. Option B, while involving a form of data comparison, is less effective for detecting subtle modifications. Simple byte-by-byte comparison only works if the exact original file is available for direct comparison, which isn’t always the case when verifying integrity against a known state. It also doesn’t inherently provide a compact, verifiable signature. Option C, relying on file modification timestamps, is highly unreliable for integrity checks. Timestamps can be easily manipulated or may not reflect actual content changes, especially in distributed or synchronized file systems. They are metadata, not a direct measure of content integrity. Option D, using file size as an indicator, is only useful for detecting additions or deletions of data, not for verifying the integrity of the existing content. A file could have the same size but entirely different data within it, rendering this method insufficient for detecting unauthorized modifications. Therefore, the most robust and conceptually sound method for ensuring data integrity against unauthorized modifications, as would be taught and valued at the Academy of Computer Science & Management in Bielsko Biata, is the use of cryptographic hash functions.

The scenario describes a system where a client sends a request to a server, which then processes it and sends back a response. The core of the question lies in understanding how the server manages multiple concurrent requests to maintain responsiveness and efficiency, a fundamental concept in distributed systems and network programming, both crucial areas at the Academy of Computer Science & Management in Bielsko Biata. When a server receives multiple requests simultaneously, it needs a strategy to handle them without blocking or causing significant delays. One common approach is to use a thread pool. A thread pool is a collection of pre-created threads that are ready to execute tasks. When a new request arrives, the server assigns it to an available thread from the pool. If all threads are busy, the request might be placed in a queue. This prevents the server from creating a new thread for every single request, which is resource-intensive and can lead to performance degradation or even denial-of-service conditions. Another strategy is asynchronous I/O, where the server initiates an operation (like reading data from a client) and then continues to process other tasks without waiting for the operation to complete. When the operation finishes, a callback or event signals the server to handle the result. This allows a single thread to manage many concurrent operations efficiently. Considering the options: 1. **Thread pooling with a fixed-size pool:** This is a robust and widely used method for managing concurrent requests. The fixed size ensures that the server doesn’t exhaust its resources by creating an excessive number of threads. It balances concurrency with resource management. This aligns with the need for efficient resource utilization and predictable performance, key considerations in the academic programs at the Academy of Computer Science & Management in Bielsko Biata. 2. **Creating a new thread for each incoming request:** While simple to implement, this is highly inefficient. Creating and destroying threads has significant overhead, and a large number of threads can consume excessive memory and CPU, leading to context-switching penalties and potential system instability. This is generally considered a poor practice for high-concurrency servers. 3. **Serial processing of all requests:** This would mean handling requests one after another. While it guarantees no resource contention from multiple threads, it completely sacrifices concurrency and would lead to extremely long response times for subsequent requests, making the server unresponsive. This is not suitable for modern web services. 4. **Using a single, infinitely looping thread:** This is fundamentally flawed. A single thread can only execute one task at a time. If that thread is busy processing a request, it cannot handle any other incoming requests, effectively making the server behave serially, similar to option 3, but with the added risk of a single point of failure if that thread crashes. Therefore, the most effective and commonly adopted strategy for a server handling numerous concurrent requests in a way that balances responsiveness and resource efficiency is thread pooling with a fixed-size pool. This approach is a cornerstone of scalable server design, a topic frequently explored in courses related to operating systems, distributed systems, and network programming at the Academy of Computer Science & Management in Bielsko Biata.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and its management within the Scrum framework. Technical debt refers to the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. In Scrum, this debt can accumulate if the team consistently prioritizes delivering new features over refactoring or improving the codebase. When a Scrum team faces a backlog filled with user stories that are becoming increasingly complex and difficult to implement due to the accumulation of technical debt, the most effective approach to address this is to proactively allocate capacity for debt reduction. This involves treating the reduction of technical debt as a form of “work” that needs to be planned and executed. Consider a scenario where the Product Owner wants to maximize the delivery of new features, but the Development Team identifies that the current codebase is hindering progress. The team estimates that without addressing the debt, future sprints will see a significant decrease in velocity. The Development Team, in collaboration with the Product Owner, should negotiate a balance. This balance is achieved by dedicating a portion of each sprint’s capacity to address technical debt. This could manifest as creating specific backlog items (e.g., “Refactor authentication module,” “Improve database indexing”) or by agreeing to a percentage of each sprint’s effort that will be allocated to such tasks. The calculation, though conceptual, involves understanding that if a sprint has a capacity of 100 units of work, and the team decides to allocate 20% to technical debt reduction, then 20 units of that capacity are dedicated to improving the codebase. This proactive approach prevents the debt from becoming unmanageable and ensures long-term sustainability and agility, aligning with the Academy of Computer Science & Management in Bielsko Biata’s emphasis on robust and maintainable software engineering practices. This strategy directly addresses the underlying principle of continuous improvement inherent in agile methodologies.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and its management within the iterative cycles of Scrum. Technical debt, in essence, represents the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. In the context of the Academy of Computer Science & Management in Bielsko Biata’s curriculum, which emphasizes practical application and efficient project management, recognizing and mitigating technical debt is crucial for long-term project sustainability and code quality. Consider a Scrum team working on a complex project for the Academy of Computer Science & Management in Bielsko Biata. During Sprint 3, the team decides to implement a new feature, but to meet the sprint deadline, they opt for a less robust database indexing strategy, creating technical debt. In Sprint 4, they are tasked with refactoring existing code to improve performance. The Product Owner, prioritizing new feature delivery, argues against allocating significant time to address the indexing issue, suggesting it can be deferred. However, the Development Team recognizes that the suboptimal indexing is now impacting the performance of multiple features developed in previous sprints, leading to slower response times during user testing sessions conducted by Academy students. The Scrum Guide emphasizes that the Development Team is responsible for the quality of the increment. While the Product Owner prioritizes the product backlog, the Development Team has the autonomy to decide how to build the increment. Addressing technical debt is not merely a task; it’s an integral part of maintaining a healthy codebase and ensuring the long-term viability of the product. If the team consistently defers addressing technical debt, it can lead to a significant slowdown in future development, increased bug rates, and a demoralized team. In this scenario, the most appropriate action for the Development Team, aligned with Scrum principles and the Academy’s focus on quality and efficiency, is to negotiate with the Product Owner. They should explain the impact of the technical debt on the current sprint’s goals and future development velocity. They can propose a balanced approach, perhaps dedicating a portion of the Sprint 4 capacity to refactor the indexing, or suggesting that the Product Owner prioritize a dedicated “technical improvement” sprint or backlog item in the near future. The key is proactive management and communication. The question asks about the *most effective* approach to manage this situation. Option a) correctly identifies that the Development Team should collaborate with the Product Owner to prioritize addressing the technical debt within the upcoming sprints, recognizing it as a critical factor for maintaining product quality and development velocity, which aligns with the Academy’s emphasis on robust software engineering practices. Option b) is incorrect because simply documenting the debt without actively planning to address it in the near future will likely exacerbate the problem, contrary to the Academy’s goal of producing high-quality software. Option c) is incorrect because while the Product Owner prioritizes the backlog, the Development Team’s responsibility for the increment’s quality means they cannot unilaterally ignore technical debt that impacts performance. Option d) is incorrect because deferring the issue indefinitely without a concrete plan to address it is a direct path to significant project degradation, which would be counterproductive to the educational objectives of the Academy of Computer Science & Management in Bielsko Biata.

The core of this question lies in understanding the principles of agile software development, specifically the concept of Minimum Viable Product (MVP) and its role in iterative development. An MVP is the version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort. It’s not about delivering a fully featured product, but rather a functional core that addresses the primary user need. In the context of the Academy of Computer Science & Management in Bielsko Biata’s curriculum, which emphasizes practical application and efficient resource management, understanding how to prioritize features for an MVP is crucial. The scenario describes a project aiming to develop a novel educational platform. The key is to identify which feature set would best satisfy the fundamental requirement of enabling student-program interaction while minimizing initial development complexity and allowing for rapid feedback. Let’s analyze the options: * **Option 1 (Correct):** A basic student profile creation, course enrollment, and a simple Q&A forum. This directly addresses the core need for student-program interaction. Student profiles are essential for individual tracking, course enrollment is the primary action, and a Q&A forum facilitates communication, which is a fundamental aspect of any educational platform. This set represents a lean yet functional core, allowing for early user testing and validation of the platform’s concept. * **Option 2 (Incorrect):** Advanced analytics dashboards for student performance, personalized learning path recommendations, and integration with external academic databases. While valuable, these are typically enhancements built upon a stable core. They represent features that would be developed *after* the fundamental interaction model is proven. Including them in an MVP would significantly increase complexity and delay the release of a testable product. * **Option 3 (Incorrect):** A fully integrated virtual classroom with live video streaming, real-time collaborative document editing, and gamified progress tracking. This represents a feature-rich, complex system. Such a comprehensive offering would require substantial development time and resources, contradicting the agile principle of delivering a minimal, testable product to gather feedback early. It’s a significant leap beyond a basic MVP. * **Option 4 (Incorrect):** A robust administrative portal for faculty, automated grading systems, and a comprehensive resource library with multimedia content. This focuses heavily on the administrative and content delivery side, neglecting the primary user interaction aspect for students. While important for the overall platform, it doesn’t represent the most essential initial functionality for validating the student-program engagement model. Therefore, the most appropriate MVP for the Academy of Computer Science & Management in Bielsko Biata’s new educational platform would be the one that enables the most fundamental student-program interaction with the least complexity, allowing for swift iteration based on user feedback.

The core of this question lies in understanding the principles of agile software development methodologies, specifically how they address evolving requirements and stakeholder collaboration, which are central to the Academy of Computer Science & Management in Bielsko Biata’s curriculum. The scenario describes a project where initial user feedback, gathered through iterative demonstrations, leads to significant changes in the product’s core functionality. This necessitates a flexible approach to planning and execution. In agile, the Product Backlog is a dynamic, prioritized list of features, user stories, and tasks. When new requirements emerge or existing ones are refined based on feedback, these changes are incorporated into the Product Backlog. The Product Owner is responsible for managing this backlog, ensuring it reflects the current understanding of what the product should be. The question asks about the most appropriate action when a critical feature, initially deemed low priority, is now essential due to user feedback. This directly relates to the adaptive planning characteristic of agile. 1. **Re-prioritization of the Product Backlog:** The Product Owner, in consultation with stakeholders and the development team, would immediately re-evaluate the priority of the newly critical feature. This might involve moving it up the backlog, potentially displacing lower-priority items. 2. **Sprint Planning Adjustment:** If the current sprint is already underway, the feature might be too large to incorporate without disrupting the sprint goal. In such cases, it would be added to the Product Backlog for consideration in the *next* sprint planning session. If the sprint is not yet started, it could be included in the upcoming sprint planning. 3. **Communication:** Open communication between the Product Owner, development team, and stakeholders is paramount. Everyone needs to understand the change in priority and its implications. Considering the options: * Option A (Re-prioritizing the Product Backlog and discussing its inclusion in the next sprint planning) directly addresses the agile principle of adapting to change and managing the backlog effectively. It acknowledges that the current sprint might be too far along, but ensures the new requirement is addressed promptly in the next iteration. This aligns with the iterative and incremental nature of agile development. * Option B suggests immediately halting the current sprint. This is generally discouraged in agile unless the sprint goal becomes completely unachievable or irrelevant, which isn’t stated here. It’s disruptive and goes against the commitment to a sprint. * Option C proposes ignoring the feedback until the next major release cycle. This is antithetical to agile’s emphasis on continuous feedback and adaptation. * Option D suggests creating a completely new project. This is inefficient and ignores the existing project’s framework and the potential to integrate the feedback within it. Therefore, the most aligned and effective agile response is to re-prioritize the backlog and plan for its inclusion in the subsequent sprint. This demonstrates an understanding of how agile frameworks like Scrum handle emergent requirements and maintain flexibility while respecting the iterative process.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and how it is managed within iterative development cycles. Technical debt, in essence, represents the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. In the context of the Academy of Computer Science & Management in Bielsko Biata’s curriculum, which emphasizes efficient and maintainable software engineering, recognizing how to mitigate this debt is crucial. Consider a scenario where a development team at the Academy of Computer Science & Management in Bielsko Biata is working on a project using Scrum. They are in their third sprint. During the first sprint, to meet a tight deadline for a proof-of-concept, they implemented a feature using a less-than-optimal database schema, knowing it would require refactoring later. This decision introduced technical debt. Now, in the third sprint, they are tasked with adding a new feature that heavily relies on the data structure implemented in the first sprint. To address the technical debt effectively, the team needs to balance delivering new functionality with improving the existing codebase. Simply ignoring the debt will lead to slower development in future sprints and increased bug rates, directly impacting project velocity and quality – key metrics often discussed in software project management courses at the Academy. The most effective approach for managing this technical debt, aligning with agile principles and the Academy’s focus on robust software engineering, is to allocate a portion of each sprint’s capacity to address it. This is often referred to as “paying down” technical debt. This could involve refactoring the database schema, improving code clarity, or enhancing test coverage for the problematic module. Let’s analyze the options: * **Option a) Allocate a dedicated portion of each sprint’s capacity to refactor the problematic database schema and associated code.** This directly addresses the debt by actively working to improve the codebase. It aligns with agile principles of continuous improvement and managing technical debt proactively. This is the most sustainable and effective approach for long-term project health, a key consideration in the Academy’s emphasis on software quality. * **Option b) Defer all refactoring until after the current feature set is complete.** This is a common but often detrimental approach. It allows technical debt to accumulate, making future development increasingly difficult and costly. It contradicts the agile principle of addressing issues as they arise. * **Option c) Focus solely on delivering the new feature, assuming the debt will be resolved by a future, separate “maintenance sprint.”** This is also a risky strategy. It relies on the assumption that a dedicated maintenance sprint will materialize and have sufficient resources, which is not guaranteed in agile environments. It prioritizes short-term delivery over long-term maintainability. * **Option d) Document the technical debt and continue adding new features without any immediate plans for remediation.** This is the least effective approach. It acknowledges the problem but does nothing to solve it, leading to a worsening situation and potential project failure due to unmanageable complexity and bugs. Therefore, the most appropriate strategy, reflecting the principles taught at the Academy of Computer Science & Management in Bielsko Biata regarding sustainable software development, is to actively manage and reduce technical debt within ongoing sprints.

The scenario describes a distributed system where a consensus algorithm is being implemented to ensure agreement among nodes. The core challenge is to maintain consistency in the face of potential network partitions or node failures. The question probes the understanding of how different consensus mechanisms handle such disruptions. In a distributed system aiming for fault tolerance, particularly in the context of the Academy of Computer Science & Management in Bielsko Biata’s focus on robust software engineering and distributed systems, understanding consensus protocols is paramount. The scenario presents a situation where a network partition occurs, isolating a subset of nodes. Let’s analyze the options in relation to common consensus algorithms: * **Paxos and Raft:** These are widely used consensus algorithms. They are designed to tolerate a certain number of failures (typically \(f\) failures in a system of \(2f+1\) nodes). When a partition occurs, a majority of nodes must still be able to communicate to make progress. If the partition splits the network such that neither partition has a majority, then neither partition can reach consensus on new state. However, if one partition contains a majority, it can continue to operate. The key is that a partition that *lacks* a majority cannot unilaterally make progress and potentially diverge from the majority partition. * **Byzantine Fault Tolerance (BFT) algorithms (e.g., PBFT):** These algorithms are designed to tolerate more malicious or arbitrary failures, including Byzantine faults. They typically require a larger supermajority (e.g., \(2f+1\) out of \(3f+1\) nodes) to reach consensus. While they are more resilient, a partition can still pose challenges. If a partition isolates fewer than the required number of nodes to form a majority (or supermajority depending on the specific BFT variant), those isolated nodes cannot make progress. The critical aspect is that the protocol must prevent the minority partition from committing invalid states. * **Two-Phase Commit (2PC):** This is a distributed transaction protocol, not a general consensus algorithm for state replication. While it involves coordination, it is known for its blocking nature. If the coordinator fails or a partition prevents communication between the coordinator and participants, the transaction can be left in an indeterminate state, blocking all participants. It does not inherently provide a mechanism for a majority partition to continue operating independently during a partition. * **Gossip Protocols:** These are typically used for information dissemination and do not guarantee strict consensus on a single state. While they can be resilient to partitions by eventually propagating information, they are not designed for the deterministic agreement required for state machine replication or distributed databases. Considering the scenario of a network partition where a subset of nodes is isolated, the most appropriate response, reflecting the principles of robust distributed systems taught at the Academy of Computer Science & Management in Bielsko Biata, is that the protocol must ensure that the isolated minority partition cannot unilaterally commit new states that would violate the consistency guarantees of the system. This is a fundamental property of fault-tolerant consensus algorithms like Paxos and Raft, and also a consideration in BFT systems. The system’s integrity relies on preventing divergence. Therefore, the core principle is that the isolated partition, lacking a majority (or the necessary quorum), must not be able to finalize operations that would lead to an inconsistent global state. The system must be designed such that only the partition containing the majority can continue to make progress, and any state committed by the minority partition before the partition would be invalidated or reconciled upon network recovery. The correct answer is that the protocol must prevent the isolated minority partition from committing new states.

The core of this question lies in understanding the ethical implications of data handling and the principles of responsible AI development, which are paramount at the Academy of Computer Science & Management in Bielsko Biata. When a machine learning model, trained on a dataset containing historical biases, is deployed to make decisions impacting individuals, it risks perpetuating and even amplifying those biases. For instance, if a dataset used to train a loan application assessment model disproportionately features rejections for certain demographic groups due to past discriminatory practices, the model will learn and replicate this pattern. The ethical imperative is to identify and mitigate these biases before deployment. This involves not just technical solutions like bias detection algorithms and re-sampling techniques, but also a deep understanding of the societal context and the potential harm caused by unfair algorithmic outcomes. The Academy emphasizes a holistic approach, integrating technical proficiency with a strong ethical framework. Therefore, the most critical step is to proactively identify and address potential biases within the training data and the model’s architecture itself, ensuring fairness and equity in its decision-making processes. This proactive stance is fundamental to responsible innovation in computer science and management, aligning with the Academy’s commitment to developing technology that benefits society ethically.

The scenario describes a software development project at the Academy of Computer Science & Management in Bielsko Biata, where a team is tasked with creating a new learning management system (LMS). The project manager is considering different methodologies. Agile methodologies, such as Scrum or Kanban, emphasize iterative development, flexibility, and continuous feedback, which are highly beneficial for projects where requirements might evolve or are not fully defined at the outset. This approach allows for rapid prototyping and adaptation to user needs, crucial for an educational platform. Waterfall, on the other hand, is a linear, sequential approach where each phase must be completed before the next begins. While it offers structure, it is less adaptable to changing requirements and can lead to delays if issues are discovered late in the development cycle. Lean principles focus on eliminating waste and maximizing value, which can be integrated into Agile frameworks. DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the systems development life cycle and provide continuous delivery with high software quality. While valuable for deployment and operations, it doesn’t solely dictate the core development methodology for initial system design and iteration. Given the need for a responsive and evolving LMS that caters to diverse student and faculty needs at the Academy of Computer Science & Management in Bielsko Biata, an Agile approach, specifically Scrum due to its structured iterations (sprints) and defined roles, would be the most suitable for managing the development lifecycle and ensuring the final product aligns with the institution’s educational goals and technological advancements.

The scenario describes a distributed system where nodes communicate using a message-passing paradigm. The core issue is ensuring that a consensus is reached among a majority of nodes regarding the state of a shared resource, even in the presence of network partitions or node failures. The question probes the understanding of fault tolerance and distributed consensus mechanisms. In a system with \(N\) nodes, a majority is typically defined as \(\lceil \frac{N}{2} \rceil + 1\) nodes. If \(N=5\), a majority requires \(\lceil \frac{5}{2} \rceil + 1 = \lceil 2.5 \rceil + 1 = 3 + 1 = 4\) nodes. To guarantee that a quorum can always be formed even if some nodes are unavailable, the number of nodes that must be available is the minimum number required for a majority. If \(f\) nodes can fail, then \(N – f\) nodes must be available to form a quorum. In this case, if up to 2 nodes can fail, then \(5 – 2 = 3\) nodes are still operational. However, to *guarantee* a majority of 4, we need at least 4 nodes to be operational. The question asks for the minimum number of nodes that *must be available* to ensure a majority can be formed. If 3 nodes are available, a majority of 4 cannot be guaranteed. If 4 nodes are available, a majority of 4 can be formed. Therefore, 4 nodes must be available. This relates to the Paxos or Raft consensus algorithms, where a majority is crucial for committing operations and maintaining consistency in a distributed environment, a fundamental concept taught at the Academy of Computer Science & Management in Bielsko Biata. Understanding these quorum requirements is vital for designing robust and reliable distributed applications, a key area of study within computer science.

The core concept tested here is the understanding of software development methodologies and their suitability for projects with evolving requirements and a need for rapid feedback, which is a key consideration in modern computer science and management programs like those at the Academy of Computer Science & Management in Bielsko Biata. The scenario describes a project at the Academy of Computer Science & Management in Bielsko Biata that involves developing a novel interactive learning platform. The key characteristics are: 1. **Uncertainty in initial requirements:** The exact features and user experience are not fully defined at the outset. 2. **Need for frequent user feedback:** Continuous input from students and faculty is crucial for shaping the platform. 3. **Iterative development:** The platform will be built and refined in stages. 4. **Agile principles:** The project aims for flexibility and responsiveness to change. Let’s analyze why the other options are less suitable in this context: * **Waterfall Model:** This is a linear, sequential approach where each phase (requirements, design, implementation, testing, deployment, maintenance) must be completed before the next begins. It is highly unsuitable for projects with evolving requirements and a need for continuous feedback, as changes late in the cycle are very costly and disruptive. The Academy’s project explicitly requires adaptability. * **Spiral Model:** While this model incorporates risk analysis and iterative development, it is often more complex and resource-intensive than necessary for a project primarily focused on user-centric feature development and rapid iteration. It’s typically used for large, high-risk projects. The Academy’s project, while innovative, doesn’t inherently suggest the extreme risk profile that would necessitate the full Spiral model’s overhead. * **V-Model:** This is an extension of the Waterfall model, emphasizing verification and validation at each stage. Similar to Waterfall, it is less flexible for projects with fluid requirements and a strong emphasis on early and continuous user interaction. It’s more suited for projects where rigorous testing at each phase is paramount and requirements are stable. Therefore, an **Agile methodology**, such as Scrum or Kanban, is the most appropriate choice. Agile methodologies are designed to handle evolving requirements, embrace change, and deliver working software in short, iterative cycles, incorporating user feedback at each step. This aligns perfectly with the described needs of the Academy’s interactive learning platform project, fostering a collaborative and adaptive development process that is highly valued in contemporary software engineering education and practice.

The core of this question lies in understanding the principles of agile software development, specifically the concept of iterative development and feedback loops, as applied within the context of a university project at the Academy of Computer Science & Management in Bielsko Biata. The scenario describes a team working on a complex system, encountering unforeseen challenges that necessitate adaptation. Option A, “Prioritizing the development of a core functional prototype that can be demonstrated and iterated upon based on early stakeholder feedback,” directly aligns with agile methodologies. This approach emphasizes delivering working software incrementally, allowing for continuous validation and course correction. By focusing on a functional prototype, the team can quickly gather insights from their instructors and peers, which is crucial for navigating the inherent uncertainties of a novel project. This iterative feedback mechanism is a cornerstone of agile, enabling the team to pivot effectively without committing to a fully realized, potentially flawed, design. The explanation of why this is correct involves discussing the benefits of early and frequent delivery of value, the importance of adapting to change, and how this contrasts with more rigid, plan-driven approaches that might lead to significant rework if initial assumptions are incorrect. This aligns with the Academy’s emphasis on practical application and adaptive problem-solving in computer science and management.

The scenario describes a distributed system where nodes communicate using a publish-subscribe messaging pattern. The core challenge is ensuring that a critical update message, intended for all active subscribers, is delivered reliably even in the presence of transient network partitions or node failures. The system aims for eventual consistency, meaning all nodes will eventually receive the message and reach a consistent state, but not necessarily simultaneously. Consider a scenario where a central authority publishes a configuration update to a topic. Several client applications, running on different machines within the Academy of Computer Science & Management in Bielsko Biata network, subscribe to this topic. If a network switch temporarily fails, isolating a subset of subscribers, the publish-subscribe broker must buffer the message for the disconnected nodes. Upon restoration of connectivity, the broker should resume delivering the buffered message. This process highlights the importance of message queuing and durable subscriptions. Durable subscriptions ensure that even if a subscriber is offline when a message is published, it will receive the message once it reconnects. The broker’s ability to maintain message state and deliver it to reconnected subscribers is paramount. The question probes the understanding of how distributed systems manage message delivery in unreliable environments, a fundamental concept in modern software engineering and crucial for the robust operation of services at the Academy of Computer Science & Management in Bielsko Biata. The correct approach involves mechanisms that guarantee message persistence and delivery to dormant subscribers.

The scenario describes a distributed system where nodes communicate using a message-passing paradigm. The core challenge is to ensure that all nodes agree on a specific value, even in the presence of potential network partitions or node failures. This is a classic problem in distributed computing, often referred to as the consensus problem. The Byzantine Generals Problem is a well-known theoretical framework that models this challenge, where some generals (nodes) might be traitors (faulty) and send conflicting information. To achieve consensus in such an environment, a specific number of reliable nodes must be present. The general result for achieving consensus in a system with \(n\) total nodes, where up to \(f\) nodes can be faulty (including Byzantine faults), requires that \(n > 2f\). In this specific case, the system has 10 nodes and can tolerate up to 3 faulty nodes. Therefore, \(n = 10\) and \(f = 3\). The condition \(n > 2f\) becomes \(10 > 2 \times 3\), which simplifies to \(10 > 6\). This condition is met, indicating that consensus is possible. The minimum number of nodes required to guarantee consensus when up to \(f\) nodes can be faulty is \(2f + 1\). With \(f=3\), the minimum number of nodes is \(2 \times 3 + 1 = 7\). Since the system has 10 nodes, which is greater than the minimum required 7 nodes, consensus can be achieved. The question asks about the fundamental requirement for achieving consensus in a distributed system with a known maximum number of faulty nodes. The ability to reach agreement on a single value, despite potential malicious behavior or failures, hinges on having a sufficient majority of honest participants. If the number of faulty nodes approaches or exceeds half the total number of nodes, it becomes impossible to distinguish between genuine and fabricated messages, thus preventing reliable consensus. The threshold \(n > 2f\) ensures that even in the worst-case scenario, where all faulty nodes conspire against the system, the honest nodes can still outvote the faulty ones and establish a common understanding. This principle is foundational for many distributed algorithms and systems, including distributed databases, blockchain technologies, and fault-tolerant computing, all of which are relevant to the advanced studies at the Academy of Computer Science & Management in Bielsko Biata.

The scenario describes a project management challenge where a software development team at the Academy of Computer Science & Management in Bielsko Biata Entrance Exam is tasked with creating a new learning analytics platform. The team is using an Agile methodology, specifically Scrum. The core issue is the delay in delivering a critical feature due to unforeseen technical complexities and a lack of clear requirements for a specific integration point. The question asks for the most appropriate action to address this situation within the Scrum framework. In Scrum, the Product Owner is responsible for maximizing the value of the product resulting from the work of the Development Team. This includes managing the Product Backlog, which is a prioritized list of features, bug fixes, and other work. When a feature’s complexity or requirements become unclear, leading to delays, the Product Owner must collaborate with the Development Team to refine the backlog item. This refinement process, often called backlog grooming or backlog refinement, involves breaking down large items into smaller, more manageable ones, clarifying acceptance criteria, and estimating effort. Option A suggests the Product Owner should immediately remove the feature from the current sprint. While removing a feature might be a last resort, it’s not the primary or most effective first step. It bypasses the opportunity to clarify and potentially salvage the work. Option B proposes that the Development Team should continue working on the feature without further clarification, hoping to resolve the issues through trial and error. This is contrary to Agile principles, which emphasize iterative development with clear goals and feedback loops. It risks wasted effort and further delays. Option C advocates for the Product Owner to work with the Development Team to break down the complex feature into smaller, more manageable user stories and to clarify the specific integration requirements. This aligns perfectly with the Product Owner’s role in backlog management and the Scrum principle of iterative refinement. By clarifying requirements and reducing the scope of individual tasks, the team can gain better visibility, estimate more accurately, and make progress even on complex features. This approach also facilitates early feedback and adaptation. Option D suggests that the Scrum Master should solely handle the technical complexities. While the Scrum Master facilitates the process and removes impediments, they are not typically responsible for defining product requirements or resolving technical implementation details. That responsibility lies with the Development Team and the Product Owner. Therefore, the most effective and Scrum-aligned action is for the Product Owner to collaborate with the Development Team to refine the Product Backlog item, breaking it down and clarifying requirements.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and its management within the Scrum framework. Technical debt refers to the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. In Scrum, this debt can accumulate if the team consistently prioritizes delivering features over refactoring or addressing architectural weaknesses. The scenario describes a team at the Academy of Computer Science & Management in Bielsko Biata Entrance Exam that has been under pressure to deliver new features rapidly, leading to shortcuts in code quality and design. This has resulted in a backlog of issues that are now slowing down future development. The question asks for the most appropriate Scrum practice to address this situation. Option a) represents the correct approach. “Sprint Retrospectives” are specifically designed for the team to inspect itself and create a plan for improvements to be enacted during the next Sprint. This is the ideal forum to discuss the impact of technical debt, identify its root causes, and collectively decide on strategies to mitigate it, such as allocating a portion of each Sprint’s capacity to refactoring or addressing architectural issues. Option b) is incorrect because “Daily Scrums” are for synchronization and planning for the next 24 hours, not for deep dives into long-term technical issues. While technical debt might be mentioned, it’s not the primary venue for strategic resolution. Option c) is incorrect. “Sprint Reviews” are for demonstrating the increment and gathering feedback from stakeholders. While the impact of technical debt might be visible in the product, the review is not the place to plan its remediation. Option d) is incorrect. “Product Backlog Refinement” is about clarifying and estimating backlog items. While technical debt items can be added to the Product Backlog, the refinement session itself doesn’t inherently solve the problem; it’s the retrospective that drives the *decision* to address it. Therefore, the Sprint Retrospective is the most fitting Scrum event for proactively managing and reducing accumulated technical debt, aligning with the Academy’s emphasis on sustainable development practices.

The scenario describes a distributed system where nodes communicate using a publish-subscribe model. Node A publishes a message to topic ‘sensor_data’. Node B and Node C are subscribed to this topic. Node B also publishes to topic ‘control_commands’, and Node D is subscribed to this topic. The question asks about the immediate consequence of Node A publishing to ‘sensor_data’. In a typical publish-subscribe system, a message published to a topic is delivered to all currently subscribed consumers of that topic. Therefore, Node B and Node C, being subscribed to ‘sensor_data’, will receive the message. Node D’s subscription to ‘control_commands’ is irrelevant to this specific publication event. The core concept being tested is the direct, immediate impact of a publish operation on subscribed entities in a decoupled messaging architecture, a fundamental principle in many distributed computing and microservices paradigms relevant to the Academy of Computer Science & Management in Bielsko Biata’s curriculum. The explanation focuses on the direct causal link between publication and subscription, emphasizing the decoupling inherent in this messaging pattern.

The core concept here revolves around the ethical implications of data privacy in the context of machine learning model development, a crucial area for students at the Academy of Computer Science & Management in Bielsko Biata. The scenario describes a situation where a predictive model, trained on sensitive user data, exhibits biased outcomes. The question probes the most appropriate ethical response. The model’s bias, manifesting as disproportionately negative predictions for a specific demographic group (individuals from rural areas), points to a potential issue in the training data or the algorithm’s learning process. This is a common challenge in AI development, where real-world data often reflects societal biases. Option A, advocating for a thorough audit of the training dataset for representational imbalances and implementing bias mitigation techniques during model retraining, directly addresses the root cause of the problem. This involves scrutinizing the data collection methods, identifying underrepresented or overrepresented groups, and applying techniques like re-sampling, re-weighting, or adversarial debiasing. This approach aligns with the Academy’s emphasis on responsible AI development and ethical data handling. Option B, suggesting the immediate deployment of the model with a disclaimer about potential biases, is ethically problematic. While transparency is important, deploying a known biased system without attempting to rectify it can lead to discriminatory outcomes and harm individuals. This would contradict the Academy’s commitment to creating beneficial and fair technological solutions. Option C, proposing to simply remove the sensitive demographic feature from the dataset, is a superficial fix. While it might mask the bias in the current model, it doesn’t address the underlying societal biases that might still be implicitly encoded in other features. Furthermore, it could lead to a less accurate or less useful model if that feature, when used ethically, could have contributed to accurate predictions for the general population. This approach fails to tackle the systemic issues. Option D, recommending the abandonment of the project due to the inherent difficulties in achieving fairness, is an overly cautious and potentially defeatist response. While fairness in AI is challenging, it is not insurmountable. The Academy encourages innovation and problem-solving, and abandoning a project due to ethical challenges, rather than seeking solutions, would be counterproductive to the spirit of advanced computer science and management. Therefore, the most ethically sound and technically appropriate response, reflecting the principles of responsible AI taught at the Academy of Computer Science & Management in Bielsko Biata, is to investigate and rectify the bias at its source.

The core of this question lies in understanding the principles of agile software development, specifically the concept of Minimum Viable Product (MVP) and its role in iterative development, a key tenet emphasized in modern computer science and management programs like those at the Academy of Computer Science & Management in Bielsko Biata. An MVP is the version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort. It is not simply a “minimum” product, but rather a product that can be released to early adopters to test hypotheses and gather feedback. In the given scenario, the development team at the Academy of Computer Science & Management in Bielsko Biata is tasked with creating a novel data visualization platform. They have identified core functionalities: interactive charting, real-time data updates, and user-defined dashboard layouts. To adhere to agile principles and deliver value quickly while mitigating risk, the team should prioritize building a version that encompasses these essential features, allowing for immediate user interaction and feedback. This initial release, the MVP, will serve as a foundation for subsequent iterations, incorporating more advanced features based on validated learning. Option A, focusing on delivering a fully featured, polished product with all planned functionalities, contradicts the iterative and feedback-driven nature of agile development. This approach would delay market entry, increase the risk of building features that users do not desire, and miss opportunities for early validation. Option B, which suggests releasing only the interactive charting component without real-time updates or customizable layouts, would be too limited. While it’s a component, it doesn’t represent a viable product that can effectively test the core value proposition of a dynamic data visualization platform. It might not provide enough utility for users to offer meaningful feedback on the overall concept. Option D, concentrating solely on the user-defined dashboard layouts, neglects the critical aspect of data visualization itself. Without interactive charting and data updates, the dashboard would be an empty shell, failing to demonstrate the platform’s primary purpose and thus yielding little valuable learning. Therefore, the most effective approach, aligning with agile methodologies and the educational focus on practical, iterative development at the Academy of Computer Science & Management in Bielsko Biata, is to build and release a version that includes interactive charting, real-time data updates, and user-defined dashboard layouts. This constitutes the Minimum Viable Product, enabling the team to gather crucial user feedback and adapt their development strategy efficiently.

The scenario describes a software development project at the Academy of Computer Science & Management in Bielsko Biata where a team is using an Agile methodology. The core of Agile is iterative development and continuous feedback. When a critical bug is discovered late in the development cycle, the team must decide how to proceed. Option A, “Prioritize the bug fix in the next sprint and communicate the delay to stakeholders,” aligns with Agile principles. Agile embraces change and allows for reprioritization of backlog items. Addressing a critical bug is a valid reason to adjust the sprint backlog. Communicating transparently with stakeholders about potential delays is also a hallmark of effective Agile project management, fostering trust and managing expectations. Option B, “Continue with the planned features, deferring the bug fix to a post-release patch,” is risky. A critical bug can severely impact user experience and product stability, making a post-release fix potentially too late. Option C, “Immediately halt all development to fix the bug, regardless of sprint commitments,” is disruptive and can lead to significant scope creep and team burnout, deviating from the planned iterative approach. Option D, “Blame the QA team for not finding the bug earlier and demand an immediate fix without process adjustments,” is counterproductive and goes against the collaborative and continuous improvement ethos of Agile. The focus should be on problem-solving, not assigning blame. Therefore, the most appropriate and Agile-aligned response is to integrate the fix into the ongoing development process with clear communication.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and how it is managed within iterative development cycles. Technical debt, in essence, refers to the implied cost of additional rework caused by choosing an easy (but limited) solution now instead of using a better approach that would take longer. In the context of the Academy of Computer Science & Management in Bielsko Biata Entrance Exam, this relates to the practical application of software engineering principles in real-world project management. When a development team prioritizes rapid feature delivery over robust design or thorough testing, they incur technical debt. This debt manifests as code that is harder to maintain, more prone to bugs, and slower to modify in the future. The scenario describes a situation where a team has consistently deferred refactoring and code optimization to meet aggressive release schedules. This accumulation of technical debt directly impacts the team’s velocity and the overall quality of the software product. The question asks to identify the most appropriate strategic response to a significant increase in technical debt that is hindering progress. Let’s analyze the options: * **Option a) Allocate a dedicated portion of each sprint to address accumulated technical debt, prioritizing refactoring and code quality improvements.** This approach directly tackles the root cause by integrating debt reduction into the regular development workflow. By dedicating a percentage of capacity each sprint, the team prevents further accumulation and systematically reduces existing debt, aligning with agile principles of continuous improvement and sustainable development. This is a proactive and balanced strategy. * **Option b) Immediately halt all new feature development until all existing technical debt is completely eliminated.** This is an extreme and often impractical approach. While thorough, it can lead to significant delays in delivering value to stakeholders and may not be feasible given business pressures. It also ignores the fact that some technical debt is inevitable and can be managed. * **Option c) Increase the team’s velocity by working overtime to compensate for the slower progress caused by technical debt.** This is a short-term fix that exacerbates the problem. Overtime often leads to burnout, increased errors, and further accumulation of technical debt as developers rush through tasks. It does not address the underlying structural issues. * **Option d) Document the technical debt and plan to address it in a future, separate “debt reduction” release.** This approach postpones the problem, allowing it to grow and potentially become unmanageable. While documentation is important, deferring action indefinitely is detrimental to long-term project health and team morale. Therefore, the most effective and sustainable strategy, reflecting best practices in software engineering and project management as taught at institutions like the Academy of Computer Science & Management in Bielsko Biata, is to integrate debt management into the regular development cycles.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and how it is managed within iterative development cycles. Technical debt, akin to financial debt, accrues when shortcuts are taken in code quality or design to meet immediate deadlines. This debt needs to be “paid down” through refactoring and improving the codebase. In an agile context, this is typically addressed during sprint planning and execution. Consider a scenario where a development team at the Academy of Computer Science & Management in Bielsko-Biała is working on a new feature for their internal project management system. They are in the third sprint of a six-sprint project. During the first two sprints, to ensure timely delivery of a Minimum Viable Product (MVP), the team consciously deferred certain code optimizations and documentation updates, accumulating a moderate level of technical debt. Now, in the third sprint, the team is planning the upcoming work. They have identified several new features requested by stakeholders and also recognize the need to address some of the accumulated technical debt. The question asks about the most effective approach to balance these competing priorities. Option A suggests dedicating a fixed percentage of each sprint’s capacity to addressing technical debt. This is a common and effective agile practice. For instance, if the team estimates they can complete 100 story points of work in a sprint, they might allocate 10-20% (10-20 story points) specifically to tackling technical debt items. This ensures consistent progress in improving code quality without completely halting new feature development. This proactive approach prevents technical debt from becoming unmanageable, which aligns with the Academy’s emphasis on robust software engineering practices. Option B, focusing solely on new features until all are complete before addressing debt, is a risky strategy that can lead to overwhelming technical debt and hinder future development. Option C, addressing debt only when it directly impacts new feature delivery, is reactive and can lead to significant delays and increased effort. Option D, leaving debt resolution entirely to the end of the project, is highly problematic as it can make the codebase unmaintainable and jeopardize the entire project’s success, a concept antithetical to the structured learning environment at the Academy. Therefore, the most sound strategy, reflecting agile principles and promoting sustainable development, is to proactively manage technical debt by allocating a portion of each sprint’s effort to it.

The scenario describes a distributed system where nodes communicate using a publish-subscribe model. Node A publishes a message to topic ‘X’. Node B and Node C are subscribed to topic ‘X’. Node D is subscribed to topic ‘Y’. Node E is subscribed to both topic ‘X’ and topic ‘Y’. In a typical publish-subscribe system: 1. A publisher sends a message to a specific topic. 2. A broker or messaging system receives the message. 3. The broker then forwards the message to all subscribers that have registered interest in that particular topic. Applying this to the scenario: * Node A publishes to topic ‘X’. * Node B is subscribed to ‘X’, so it will receive the message. * Node C is subscribed to ‘X’, so it will receive the message. * Node D is subscribed to ‘Y’, so it will *not* receive the message published to ‘X’. * Node E is subscribed to ‘X’, so it will receive the message. Therefore, the nodes that will receive the message published by Node A are Node B, Node C, and Node E. The question asks which of the following statements accurately reflects the message distribution. The correct statement must identify all nodes that receive the message and exclude those that do not. The core concept being tested is the fundamental mechanism of the publish-subscribe pattern in distributed systems, a key area of study in computer science, particularly relevant to network programming, microservices architecture, and event-driven systems, which are integral to the curriculum at the Academy of Computer Science & Management in Bielsko Biata. Understanding how messages are routed based on topic subscriptions is crucial for designing scalable and efficient distributed applications. This pattern decouples publishers from subscribers, allowing for flexible system design. The Academy emphasizes such foundational principles for building robust software solutions.

The scenario describes a project management challenge within a software development context, relevant to the Academy of Computer Science & Management in Bielsko Biata’s curriculum. The core issue is the potential for scope creep and its impact on project timelines and resource allocation. The project manager needs to balance client satisfaction with maintaining project integrity. The client’s request for “additional features that enhance user engagement” without a formal change request process or impact assessment directly leads to scope creep. Scope creep is the uncontrolled expansion of project requirements after the project has begun. This often happens when new features or functionalities are added without proper evaluation of their impact on the original budget, schedule, and resources. In this situation, the project manager’s primary responsibility is to manage the project’s scope effectively. This involves understanding the client’s evolving needs while adhering to the agreed-upon project plan. A formal change control process is crucial for managing such requests. This process typically involves: 1. **Receiving the request:** Documenting the proposed change. 2. **Analyzing the impact:** Assessing how the change affects scope, schedule, cost, resources, and quality. 3. **Seeking approval:** Presenting the analysis to stakeholders (including the client) for a decision. 4. **Implementing the change (if approved):** Updating project plans and documentation accordingly. Failing to follow this process, as implied by the client’s direct communication of new feature ideas, can lead to significant problems. The project manager must proactively address this by initiating the change control process. This ensures that all stakeholders are aware of the implications of any proposed changes and that decisions are made based on a clear understanding of the trade-offs. Therefore, the most appropriate action for the project manager is to formally document the client’s requests, assess their impact on the project’s existing constraints, and present these findings for a decision. This upholds good project management practices, which are fundamental to successful software development and business management, areas of focus at the Academy of Computer Science & Management in Bielsko Biata.

The scenario describes a software development project at the Academy of Computer Science & Management in Bielsko Biata where a team is using an Agile methodology, specifically Scrum. The core of the problem lies in the team’s inability to consistently deliver a potentially shippable increment of work at the end of each Sprint. This indicates a breakdown in the Scrum framework’s core principles. Let’s analyze the potential causes and their impact on the Scrum process: 1. **Lack of a well-defined Sprint Goal:** A Sprint Goal provides focus and direction for the Sprint. Without it, the team might be working on disparate tasks without a unifying objective, leading to an incomplete or unintegrated increment. 2. **Unrealistic Sprint Planning:** If the team overcommits during Sprint Planning, they are likely to fail to complete the planned work, resulting in an incomplete increment. This could stem from poor estimation, scope creep, or external dependencies not being managed. 3. **Insufficient Definition of Done (DoD):** The DoD is a crucial artifact that defines the quality standards for the work. If the DoD is vague or not rigorously applied, the team might consider work “done” when it still requires significant integration or testing, leading to an un-shippable increment. 4. **Poor Backlog Refinement:** A well-refined Product Backlog ensures that Product Backlog Items (PBIs) are clear, understood, and estimated before Sprint Planning. If PBIs are not adequately prepared, the team might struggle to understand the requirements during the Sprint, hindering progress. 5. **Ineffective Daily Scrums:** Daily Scrums are meant to synchronize activities and create a plan for the next 24 hours. If these meetings are not focused on progress towards the Sprint Goal or identifying impediments, they won’t help the team overcome obstacles to delivering the increment. 6. **Lack of Collaboration and Self-Organization:** Agile thrives on collaboration and self-organization. If team members are not working together effectively, or if there’s a lack of ownership, it can impede the delivery of a cohesive increment. Considering the problem statement, the most fundamental issue that directly impacts the ability to deliver a *potentially shippable increment* is the **lack of a clear and achievable Sprint Goal**. While other factors contribute to delivery issues, the Sprint Goal acts as the primary driver for the team’s focus and collective effort towards a specific, integrated outcome. Without a clear target, the team’s work can become fragmented, making it difficult to achieve a cohesive, shippable product increment, regardless of how well other Scrum events are conducted or how refined the backlog is. The Sprint Goal ensures that the team is working towards a common, valuable objective, which is essential for producing a unified and potentially releasable product increment.

The scenario describes a distributed system where a consensus algorithm is being implemented. The core challenge is to ensure that all participating nodes agree on a single value despite potential network delays and node failures. The question probes the understanding of how different consensus mechanisms handle these challenges, specifically in the context of achieving fault tolerance and liveness. In a distributed system, achieving consensus is paramount for maintaining data consistency and enabling coordinated actions. Various algorithms exist, each with its own trade-offs. Paxos, for instance, is known for its correctness but can be complex to implement and may suffer from liveness issues under certain conditions (e.g., repeated conflicts). Raft, on the other hand, was designed with understandability and practical implementation in mind, aiming to provide stronger liveness guarantees. Byzantine Fault Tolerance (BFT) algorithms, such as PBFT, are designed to handle a more adversarial environment where nodes can exhibit arbitrary malicious behavior, not just failures. The Academy of Computer Science & Management in Bielsko Biata Entrance Exam often emphasizes understanding the foundational principles of distributed systems and their practical implications. A candidate’s ability to differentiate between algorithms based on their fault tolerance models and liveness properties is crucial. The question requires evaluating which algorithm is best suited for a scenario where a significant portion of nodes might be unreliable or even malicious, and where continuous operation (liveness) is a critical requirement. Considering the need for resilience against potentially malicious behavior and the importance of continuous operation, Byzantine Fault Tolerance algorithms are the most appropriate. While Paxos and Raft are robust for crash failures, they do not inherently protect against nodes actively trying to disrupt the consensus process. Therefore, an algorithm designed for Byzantine failures would be the most suitable choice for the described scenario at the Academy of Computer Science & Management in Bielsko Biata.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt.” Technical debt refers to the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. In the context of the Academy of Computer Science & Management in Bielsko Biata’s emphasis on robust software engineering practices, recognizing and managing technical debt is crucial for long-term project health and maintainability. Consider a scenario where a development team at the Academy of Computer Science & Management in Bielsko Biata is working on a new feature for a student portal. They are under pressure to deliver quickly. Instead of implementing a fully scalable database schema that would require more upfront design and coding, they opt for a simpler, denormalized structure that can be built faster. This decision, while expediting the initial delivery, introduces technical debt. If the portal’s user base grows significantly, this denormalized structure will become inefficient, leading to slower query times and increased maintenance overhead. The team will then have to refactor the database, which will consume valuable development time and resources that could have been used for new features. This refactoring effort is the “repayment” of the technical debt. The question probes the understanding of how such a decision impacts the project’s future development velocity and the overall quality of the software product, aligning with the Academy’s focus on producing well-engineered solutions. The correct answer identifies the direct consequence of prioritizing short-term gains over long-term architectural soundness.

The core of this question lies in understanding the principles of agile software development, specifically the concept of “technical debt” and its management within a continuous integration and continuous delivery (CI/CD) pipeline. Technical debt, in essence, represents the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. In the context of the Academy of Computer Science & Management in Bielsko Biata Entrance Exam, understanding how to balance rapid feature delivery with maintaining code quality is crucial for students aiming to work in modern software engineering environments. Consider a scenario where a development team at the Academy of Computer Science & Management in Bielsko Biata is striving to meet aggressive release targets for a new module. They are employing a CI/CD pipeline. To expedite the initial release, they decide to defer the refactoring of a complex legacy component, effectively incurring technical debt. This decision allows them to deliver the core functionality faster. However, as subsequent features are built upon this component, the cost of development increases due to the difficulty in understanding and modifying the debt-ridden code. The team’s CI/CD pipeline, while efficient for deployment, doesn’t inherently address the accumulation of this debt. To manage this, the team needs a strategy that integrates debt reduction into their workflow. This involves allocating specific time or resources for refactoring and code improvement. Without this, the technical debt will continue to grow, slowing down future development and increasing the risk of bugs. The most effective approach for a team committed to long-term maintainability and agility, as emphasized in the curriculum at the Academy of Computer Science & Management in Bielsko Biata, is to proactively schedule and execute refactoring tasks. This could involve dedicating a percentage of each sprint to debt reduction, implementing stricter code review processes, or using automated tools to identify and prioritize areas for improvement. The goal is to prevent the debt from becoming unmanageable, which would ultimately hinder their ability to deliver value efficiently. Therefore, the correct approach is to actively schedule and integrate refactoring efforts into the development lifecycle, rather than solely relying on the deployment automation of the CI/CD pipeline.

The scenario describes a distributed system where nodes communicate using a gossip protocol. The goal is to determine the minimum number of rounds required for a specific node, let’s call it Node X, to receive information from all other \(N-1\) nodes, given that each node can broadcast to \(k\) other nodes per round. In a gossip protocol, information spreads from node to node. If a node has information, it shares it with \(k\) randomly chosen neighbors. The question implies a worst-case scenario for Node X to receive information, meaning we want to find the minimum number of rounds for Node X to be *guaranteed* to have received information from everyone. However, the phrasing “receive information from all other \(N-1\) nodes” suggests a direct or indirect path for information to reach Node X. Let’s consider the spread of information *to* Node X. In each round, Node X can receive information from up to \(k\) distinct nodes that it directly communicates with. If Node X can initiate communication, it can reach \(k\) nodes. If other nodes can reach Node X, it also receives information. The problem is about Node X *receiving* information. Consider the total number of nodes \(N\). Node X needs to receive information from \(N-1\) other nodes. In round 1, Node X can directly contact \(k\) nodes. Let’s assume these are the first \(k\) nodes it learns from. In round 2, Node X can contact another \(k\) nodes. If these are new nodes, it now has information from \(2k\) nodes. This continues. The number of nodes Node X has received information from after \(r\) rounds, assuming it contacts \(k\) new nodes each round, would be \(k \times r\). To receive information from all \(N-1\) nodes, we need \(k \times r \ge N-1\). Therefore, the minimum number of rounds \(r\) is \(\lceil \frac{N-1}{k} \rceil\). In this specific problem, \(N = 100\) and \(k = 5\). So, the minimum number of rounds is \(\lceil \frac{100-1}{5} \rceil = \lceil \frac{99}{5} \rceil = \lceil 19.8 \rceil = 20\). This calculation assumes that Node X is actively reaching out and that the nodes it reaches are distinct in each round. A more nuanced understanding of gossip protocols involves the spread of information *through* the network, not just directly to Node X. However, the question is phrased from Node X’s perspective of *receiving* information. If Node X can initiate communication with \(k\) nodes, and these are distinct each round, then after \(r\) rounds, it has directly communicated with \(k \times r\) nodes. To ensure it has received information from all \(N-1\) other nodes, it needs to have communicated with them. The ceiling function accounts for the fact that you can’t have a fraction of a round. This problem tests understanding of network propagation and resource allocation in a distributed system, relevant to the robust and efficient communication strategies taught at the Academy of Computer Science & Management in Bielsko-Biała. The ability to model and analyze such processes is crucial for developing scalable and resilient distributed applications, a core competency for graduates. The concept of rounds and the ceiling function highlight the discrete nature of such processes and the need for careful planning to achieve full network coverage or information dissemination.