AZ-204 Practice Question: Monitor, troubleshoot, and optimize Azure solutions
You are optimizing an Azure API Management instance that handles 10,000 requests per second. You notice that caching is not effective. The cache hit ratio is below 10%. You need to increase the cache hit ratio. What should you do?
⚠ Common exam trap
Watch out — candidates often assume a low cache hit ratio is due to insufficient cache size or backend performance, when the real issue is overly granular cache keys that prevent reuse.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure cache key to include only relevant query parameters
The low cache hit ratio indicates that cache keys are too specific, causing each request to miss the cache. By configuring the cache key to include only relevant query parameters, you group similar requests under the same cache key, increasing the likelihood of cache hits. This directly addresses the root cause of poor cache utilization in Azure API Management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use external Azure Cache for Redis
Why it's wrong here
While Azure Cache for Redis offers a robust and scalable external caching solution, simply relocating the cache storage externally does not inherently resolve a low cache hit ratio if the underlying cache key design is flawed. If the cache keys are too granular or include irrelevant parameters, the system will still generate unique keys for logically identical requests, leading to frequent cache misses regardless of where the cache is stored. The problem lies in *what* is being cached and *how* it's identified, not *where* it's stored, making this an ineffective solution for improving hit ratio.
- ✓
Configure cache key to include only relevant query parameters
Why this is correct
Optimizing the cache key to include only essential query parameters significantly improves the cache hit ratio by ensuring that logically identical requests map to the same cached response. By explicitly excluding irrelevant parameters (e.g., tracking IDs, timestamps, or optional filters that do not alter the core response content), multiple requests for the same resource will share a single cache entry. This consolidation ensures that the API Management instance can serve more requests directly from the cache, thereby reducing backend load, improving API response times, and maximizing caching efficiency.
- ✗
Disable caching for low-traffic APIs
Why it's wrong here
Disabling caching for any API, regardless of its traffic volume, will prevent any requests to that API from being served from the cache, thereby reducing the overall cache hit ratio for the entire API Management instance. While low-traffic APIs might not benefit as much from caching as high-traffic ones in terms of absolute saved backend calls, disabling it entirely eliminates any potential for cache hits for those endpoints. This action is counterproductive to an optimization goal focused on increasing the cache hit ratio across the API Management instance.
- ✗
Increase the cache size to 5 GB
Why it's wrong here
Increasing the cache size, even to a substantial 5 GB, primarily addresses scenarios where the cache is frequently evicting valid, frequently accessed items due to capacity constraints. However, if the cache hit ratio is low due to a poorly designed cache key that generates too many unique entries for logically identical data, a larger cache will simply store more unique, rarely re-hit items for a longer duration. It will not improve the efficiency of identifying and serving repeated requests for the same logical resource, as the fundamental issue of key uniqueness remains unaddressed.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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