How Do You Mitigate Azure Cache for Redis Maintenance Performance Issues?
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To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. When you are ready to answer a question, click the Question button to return to the question. Background - Fourth Coffee is a global coffeehouse chain and coffee company recognized as one of the world’s most influential coffee brands. The company is renowned for its specialty coffee beverages, including a wide range of espresso-based drinks, teas, and other beverages. Fourth Coffee operates thousands of stores worldwide. Current environment - The company is developing cloud-native applications hosted in Azure. Corporate website - The company hosts a public website located at http://www.fourthcoffee.com/. The website is used to place orders as well as view and update inventory items. Inventory items - In addition to its core coffee offerings, Fourth Coffee recently expanded its menu to include inventory items such as lunch items, snacks, and merchandise. Corporate team members constantly update inventory. Users can customize items. Corporate team members configure inventory items and associated images on the website. Orders - Associates in the store serve customized beverages and items to customers. Orders are placed on the website for pickup. The application components process data as follows: 1. Azure Traffic Manager routes a user order request to the corporate website hosted in Azure App Service. 2. Azure Content Delivery Network serves static images and content to the user. 3. The user signs in to the application through a Microsoft Entra ID for customers tenant. 4. Users search for items and place an order on the website as item images are pulled from Azure Blob Storage. 5. Item customizations are placed in an Azure Service Bus queue message. 6. Azure Functions processes item customizations and saves the customized items to Azure Cosmos DB. 7. The website saves order details to Azure SQL Database. 8. SQL Database query results are cached in Azure Cache for Redis to improve performance. The application consists of the following Azure services: Requirements - The application components must meet the following requirements: • Azure Cosmos DB development must use a native API that receives the latest updates and stores data in a document format. • Costs must be minimized for all Azure services. • Developers must test Azure Blob Storage integrations locally before deployment to Azure. Testing must support the latest versions of the Azure Storage APIs. Corporate website - • User authentication and authorization must allow one-time passcode sign-in methods and social identity providers (Google or Facebook). • Static web content must be stored closest to end users to reduce network latency. Inventory items - • Customized items read from Azure Cosmos DB must maximize throughput while ensuring data is accurate for the current user on the website. • Processing of inventory item updates must automatically scale and enable updates across an entire Azure Cosmos DB container. • Inventory items must be processed in the order they were placed in the queue. • Inventory item images must be stored as JPEG files in their native format to include exchangeable image file format (data) stored with the blob data upon upload of the image file. • The Inventory Items API must securely access the Azure Cosmos DB data. Orders - • Orders must receive inventory item changes automatically after inventory items are updated or saved. Issues - • Developers are storing the Azure Cosmos DB credentials in an insecure clear text manner within the Inventory Items API code. • Production Azure Cache for Redis maintenance has negatively affected application performance. You need to mitigate the Azure Cache for Redis issue. What are two possible ways to achieve this goal? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. - 
Community Votes
40% of anonymous learners picked answer AB. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests how to restore Azure Cache for Redis performance after a maintenance-induced failover; the trap is choosing memory-tuning or eviction options that never address the connection spike on the surviving node.
Azure Cache for Redis maintenance forces a failover that moves every client connection onto the surviving node and spikes server load. This page establishes that the correct AZ-204 answer is to recreate and redistribute client connections and to configure client retries with exponential backoff.
The most common wrong picks are C (allkeys-lfu eviction) and D (raising maxmemory-reserved / maxfragmentationmemory-reserved), because both sound like cache performance tuning; neither reduces the server load created when a failed-over node's connections all land on the remaining node.
Community Discussion (5 comments)
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Expert Analysis
Why the Answer Is Correct
Azure Cache for Redis maintenance forces a failover: every client connection held by the node that goes down is transferred to the node still running, and the surviving node's server load spikes. Microsoft's documented mitigation is to recreate those connections so they are redistributed across both nodes — exactly the remediation captured by option A — and to make the client resilient so commands are not lost during the blip, which is option B's retry-with-exponential-backoff pattern. Option A is also framed as validating the fix in the test environment, which is the safe way to prove the connection-recreation approach before touching production. Together, A and B address both halves of the maintenance impact: connection redistribution and transient command failure.Why the Other Options Are Wrong
Option C changes the maxmemory policy to allkeys-lfu, which evicts least-frequently-used keys; the maintenance incident is a momentary connection and load spike, not a memory-pressure problem, and evicting keys would additionally shrink the cached SQL Database result set. Option D raises maxmemory-reserved and maxfragmentationmemory-reserved, which set aside memory for non-cache operations and fragmentation — useful when memory is fragmented, but irrelevant to a failover-driven connection surge. Option E purges the cache in the test environment, which destroys the cached query results the workload depends on and proves nothing about production resilience. None of C, D or E recreates and redistributes the connections that moved during the failover.Community Comment Notes
smetr pointed straight at Microsoft's server-maintenance guidance, quoting that "all the client connections get recreated and redistributed among the two nodes", which is the core of option A. Vichu_1607 and fivestarfrog both selected the same A/B pairing, with fivestarfrog calling out that smetr's explanation was on point. Zezere disagreed, rating BD and insisting that "Eviction is not pertinent since all keys are correct at this point in time" and that rebooting a test instance cannot fix production — a fair objection, but option A is about recreating client connections rather than restarting the cache nodes. Mattt backed B and argued for C to retain the most relevant data, yet that reasoning targets memory pressure rather than the maintenance-induced connection spike that B actually remedies.Official Reference
Exam Strategy
In case-study items, anchor the answer to the exact symptom in the Issues list — here, 'maintenance has negatively affected performance' — and match it to the documented resilience guidance for that service, not to generic tuning knobs. Remember that questions saying 'Each correct answer presents part of the solution' require two selections that cover different mechanisms, so one connection fix plus one retry fix is a strong signal.
Frequently Asked Questions
Why is raising maxmemory-reserved and maxfragmentationmemory-reserved wrong here?
Those settings reserve memory for non-cache operations and fragmentation; they do not reduce the connection spike and server load caused when a Redis node fails over during maintenance.
Why does rebooting the app help after Azure Cache for Redis maintenance?
A failover moves all client connections onto the surviving node and server load spikes; recreating the connections redistributes them across both nodes.