Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
You need to monitor the performance of a production Cloud Run service and set an alert when the p99 latency exceeds 500 ms over a 5-minute window. Which combination of Cloud Monitoring resources should you use?
⚠ Common exam trap
PCA often tests the misconception that log-based metrics or uptime checks can substitute for native latency metrics — the exam expects you to know that request_latencies with a percentile aggregator is the correct primitive for p99 alerting.
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
✓
Define an alerting policy using the metric 'run.googleapis.com/request_latencies' with a percentile aggregator and threshold condition
Cloud Run exposes request latency as the built-in metric run.googleapis.com/request_latencies, which can be aggregated with a percentile aligner (e.g., 99th percentile) over a 5-minute window and used in a Cloud Monitoring alerting policy with a threshold of 500 ms. This is the native, lowest-latency path for p99 latency alerting on Cloud Run.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define an alerting policy using the metric 'run.googleapis.com/request_latencies' with a percentile aggregator and threshold condition
Why this is correct
Cloud Run exports request latency as a distribution metric, so a percentile aggregator is required to derive p99 rather than an average. Pairing that aggregator with a 500 ms threshold over a five-minute alignment window satisfies the stem's latency alerting condition.
- ✗
Create a log-based metric for latency and an alerting policy with a condition on the count of logs
Why it's wrong here
Log-based metrics count log entries, which carry no numeric latency distribution, so a p99 threshold cannot be computed from them. Cloud Run already exports request latency as a distribution metric; the alerting policy should query that histogram. Log-based metrics suit counting discrete events such as error occurrences.
- ✗
Use Cloud Logging to export logs to BigQuery and run a scheduled query to check latency
Why it's wrong here
Exporting logs to BigQuery and querying on a schedule introduces batch delay, so the alert cannot fire promptly on a five-minute window. It tempts because BigQuery suits long-term log analytics, but latency alerting needs Cloud Monitoring metrics with an alerting policy.
- ✗
Create an uptime check and set an alert on the check response time
Why it's wrong here
An uptime check measures availability and response time from external probes, not the service's p99 request latency. It tempts because uptime checks do alert on response time, but they sample synthetic requests rather than aggregating real request latency percentiles.
Go deeper
Related to this question
Learn chapter
Resource Monitoring and Logging with Cloud Operations
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
Key term
Cloud Monitoring
Cloud monitoring is the process of observing, measuring, and managing an organization's cloud infrastructure and applications to ensure performance, availability, and security.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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