BenchMark Quizzes by MDFT Pro

Resolve Azure Cache for Redis Overload

You work as a Cache Infrastructure Engineer for MDFT Pro, a well-known training agency that delivers certification courses to students worldwide. Claire, the Performance Optimization Lead, has reported that the Azure Cache for Redis instance used by the Student Learning Portal is experiencing frequent overload conditions. The cache stores session data, course progress information, quiz results, and frequently accessed course materials to reduce database load. During peak enrollment periods and exam seasons, when thousands of students access the portal simultaneously, the Redis cache becomes overloaded—causing timeouts, connection failures, and degraded application performance. Students experience slow page loads and failed login attempts. You need to implement solutions that both prevent the cache from becoming overloaded and make the application more resilient when temporary overload situations occur.

What are two ways to resolve the Redis cache overload issue?

Choose all correct answers from the options below.

Explanations for each answer:

Test application code by rebooting all nodes in the test environment is incorrect. Rebooting nodes is disruptive and doesn't address the root causes of Redis overload. While it might temporarily clear the issue, it doesn't implement any preventive measures like retry logic or memory management improvements. Rebooting is a last resort, not a proactive solution.
Modify the maxmemory policy to evict the least frequently used keys out of all keys is incorrect. Changing the eviction policy (like using allkeys-lfu for least frequently used) affects which keys are removed when memory is full, but doesn't prevent overload or handle connection issues. This is about memory management strategy, not resolving current overload conditions or improving resilience.
Configure client connections to retry commands with exponential backoff is correct. Implementing retry logic with exponential backoff helps clients gracefully handle transient failures and connection issues during overload scenarios. This prevents cascading failures, reduces request storms, and allows the Redis cache time to recover from temporary spikes in load.
Test application code by purging the cache in the test environment is incorrect. Purging the cache in test doesn't address production overload issues and could actually make things worse by causing a cache stampede when all keys need to be reloaded. This is a testing technique, not a solution for preventing or resolving overload conditions.
Increase the maxmemory-reserved and maxfragmentationmemory-reserved values is correct. Reserving more memory for Redis operations prevents memory pressure that causes performance degradation and connection issues. These settings ensure Redis has buffer memory for background operations like persistence, replication, and defragmentation, reducing the likelihood of overload conditions.

Learn more about Redis Memory Management:

Redis Best Practices

Practice Exam

Resolve Azure Cache for Redis Overload

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