PCDOE Implementing service monitoring strategies Practice Question
Your organization runs a critical e-commerce platform on Google Kubernetes Engine (GKE). The platform uses Cloud Service Mesh (Anthos Service Mesh) for traffic management and Cloud Monitoring for observability. Recently, after a new release, you observe that the p99 latency of the checkout service has increased from 200ms to 2s. The service's CPU and memory metrics appear normal, and there are no error logs. The release included a change to the Istio VirtualService configuration that added a retry policy: 3 retries with a 500ms timeout per retry. You suspect that the retries are contributing to the latency increase. You want to use Cloud Monitoring to confirm this hypothesis. Which approach should you take?
⚠ Common exam trap
Google Cloud often tests the distinction between metrics (which aggregate over time) and traces (which show individual request paths), leading candidates to choose Cloud Trace (Option A) when they should use Metrics Explorer with retry-specific metrics to confirm a latency hypothesis.
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
✓
Use Metrics Explorer to query the istio.io/service/server/request_count metric, filtered by response_code_class and destination_service, and include the istio.io/service/server/request_retries metric to see retry counts alongside latency
It directly correlates retry attempts with latency by querying the `istio.io/service/server/request_retries` metric alongside the `istio.io/service/server/request_count` metric in Metrics Explorer. This allows you to visualize the retry count per destination service (checkout) and compare it with the p99 latency increase, confirming whether the retry policy is causing the observed latency spike. The retry policy (3 retries with 500ms timeout) can add up to 1.5s of additional latency per request, which aligns with the increase from 200ms to 2s.
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 Cloud Trace to analyze distributed traces for the checkout service and look for retry spans
Why it's wrong here
Trace analysis can confirm retries but is more time-consuming than using metrics.
- ✗
Check the 'Services' dashboard in Cloud Monitoring, which shows a pre-built latency chart for all services
Why it's wrong here
The default dashboard may not include retry metrics, so it won't confirm the hypothesis.
- ✓
Use Metrics Explorer to query the istio.io/service/server/request_count metric, filtered by response_code_class and destination_service, and include the istio.io/service/server/request_retries metric to see retry counts alongside latency
Why this is correct
This directly shows the correlation between retries and latency.
- ✗
Use Logs Explorer to search for logs containing 'retry' in the checkout service namespace
Why it's wrong here
Retry logs may not be generated by default; they require explicit Istio logging configuration.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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