A deadlock occurs in multithreading when two or more threads are unable to proceed because each is waiting for the other to release a resource, resulting in a standstill where no progress can be made. This can lead to system hangs or crashes.

The CAP theorem is crucial in understanding the limitations of distributed systems. It states that in the presence of a network partition, a distributed system can only guarantee either Consistency or Availability, not both. NoSQL databases often sacrifice Consistency (CP) for better Availability and Partition tolerance (AP).

Mutex and semaphore are both synchronization mechanisms, but they serve different purposes. A mutex ensures mutual exclusion, allowing only one thread to access a resource at a time, while a semaphore can allow multiple threads to access multiple resources concurrently. Mutexes typically use binary values (0 and 1) to signal resource availability, while semaphores can have a count greater than 1, allowing for resource allocation based on available counts.

Dynamic imports enable code splitting by loading components only when needed, reducing the initial bundle size and improving load times. This approach is more efficient than bundling all components together or using preloading techniques, as it minimizes the initial download size.

In Agile methodologies, estimating the effort required for each user story or task is commonly referred to as "Story Pointing." Story Pointing involves assigning relative values (such as story points) to different tasks or user stories, based on their complexity, risk, and effort required. This practice helps Agile teams plan and prioritize their work effectively during sprint planning sessions. Sprint planning involves determining which user stories and tasks will be tackled in the upcoming sprint, whereas backlog refinement is the ongoing process of reviewing and updating the product backlog. Velocity tracking, on the other hand, is a measure of how much work a team can complete in a sprint, often calculated based on past performance. Therefore, the most appropriate option for estimating effort in Agile is Story Pointing.

Code coverage measures the percentage of code executed by your test suite. To determine this, you can use tools that analyze test results and report on the lines of code covered during testing. This metric helps assess the thoroughness of your testing efforts and identifies areas of the codebase that may require additional test cases for better coverage and quality assurance.

The key difference between deadlock prevention and deadlock avoidance lies in their approaches to handling deadlock situations in operating systems. Deadlock prevention focuses on proactively avoiding deadlock by implementing resource allocation strategies and system design techniques that eliminate the possibility of deadlock occurrence. On the other hand, deadlock avoidance aims to reactively resolve deadlock situations after they occur by detecting them and taking appropriate actions, such as preempting resources or terminating processes to break the deadlock. While prevention aims to stop deadlock from happening in the first place, avoidance deals with managing deadlocks if they occur, making them complementary strategies in ensuring system stability and resource utilization efficiency.

Binary Search is the optimal approach for searching in sorted arrays due to its logarithmic time complexity O(log n). Linear Search is inefficient for large datasets, Hashing may not be suitable for sorted arrays, and Indexing might introduce additional overhead in a distributed system.

IPv6 uses 128-bit addresses, which is a significant increase from IPv4's 32-bit addresses. This expansion allows for a much larger number of possible unique addresses, addressing the issue of IPv4 address exhaustion. Therefore, "128" is the correct option.

Normalization involves breaking down data into multiple tables and using relationships like foreign keys to link them together. This ensures data is not duplicated, reduces redundancy, and allows for efficient querying and data retrieval. Storing all information in one table would lead to data redundancy and inefficiency. Using multiple databases or denormalization would not adhere to normalization principles.