- A
Use Cloud Trace to analyze distributed traces for the checkout service and look for retry spans
Why wrong: Trace analysis can confirm retries but is more time-consuming than using metrics.
- B
Check the 'Services' dashboard in Cloud Monitoring, which shows a pre-built latency chart for all services
Why wrong: The default dashboard may not include retry metrics, so it won't confirm the hypothesis.
- C
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
This directly shows the correlation between retries and latency.
- D
Use Logs Explorer to search for logs containing 'retry' in the checkout service namespace
Why wrong: Retry logs may not be generated by default; they require explicit Istio logging configuration.
Quick Answer
The answer is to use Metrics Explorer to query the `istio.io/service/server/request_count` metric filtered by `response_code_class` and `destination_service`, alongside the `istio.io/service/server/request_retries` metric. This approach is correct because it directly correlates retry attempts with latency by visualizing retry counts per destination service—such as the checkout service—alongside request counts, allowing you to confirm whether the added retry policy (3 retries with a 500ms timeout) is causing the observed p99 spike from 200ms to 2s. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your ability to diagnose latency increases after a retry policy change in Istio on GKE, a common trap where candidates focus on CPU or memory metrics instead of retry-specific telemetry. The key insight is that retries multiply latency: each retry adds its timeout, so 3 retries at 500ms can add up to 1.5s, aligning with your observed increase. Memory tip: “Retries multiply latency—count the retries, not the CPU.”
PCDOE Implementing service monitoring strategies Practice Question
This PCDOE practice question tests your understanding of implementing service monitoring strategies. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, Istio's retry policy is implemented at the Envoy proxy sidecar, which intercepts requests and retries them based on the VirtualService configuration. The `istio.io/service/server/request_retries` metric counts retry attempts, and when combined with the `istio.io/service/server/request_count` metric filtered by `response_code_class` (e.g., 5xx or timeout), you can calculate the retry rate and its impact on latency. In real-world scenarios, a retry policy with a 500ms per-retry timeout can cause cascading latency if the upstream service is slow, as each retry adds to the total response time, and the p99 latency can spike dramatically even if CPU/memory are normal.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCDOE question test?
Implementing service monitoring strategies — This question tests Implementing service monitoring strategies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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 — Option C is correct because 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.
What should I do if I get this PCDOE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 30, 2026
This PCDOE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDOE exam.
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