PCD Managing application performance monitoring — Timed 10 Questions

Question 1

A company deploys a microservices application on Google Kubernetes Engine (GKE). The operations team needs to monitor API latency between services. Which Google Cloud service should they use to trace requests across services?

Accepted Answer

Cloud Trace. Cloud Trace is the correct choice because it is a distributed tracing system designed to capture latency data as requests propagate through microservices. It provides end-to-end visibility by collecting trace spans from each service, allowing the operations team to identify bottlenecks and measure API latency between services in a GKE environment.

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Error Reporting

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Cloud Logging

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Cloud Monitoring

Question 2

A developer notices that a Cloud Function is timing out after 60 seconds. The function makes an external API call that occasionally takes longer than the timeout. What is the best practice to handle this?

Accepted Answer

Increase the timeout for the specific Cloud Function to a higher value. Option C is correct because Cloud Functions have a configurable timeout per function (up to 540 seconds for HTTP functions). Increasing the timeout for the specific function that makes the slow external API call directly addresses the timeout issue without affecting other functions or introducing unnecessary retry overhead. This is the most targeted and efficient solution.

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Implement retry logic without changing the timeout

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Increase the timeout for all Cloud Functions in the project

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Decrease the timeout to fail fast and implement retry logic

Question 3

A company uses Cloud Monitoring to set up an alerting policy for CPU utilization on Compute Engine instances. They want to be notified when average CPU usage exceeds 80% for 5 minutes. Which threshold type should they use?

Accepted Answer

Threshold. Option C is correct because a Threshold alerting policy in Cloud Monitoring triggers when a metric's value crosses a defined static boundary. For this use case, setting a threshold of 80% with a duration of 5 minutes directly matches the requirement to alert when average CPU usage exceeds 80% for that period.

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Forecast

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Change rate

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Metric absence

Question 4

An application running on GKE is experiencing high latency. The team uses Cloud Trace to identify the bottleneck. They notice that a particular service spends most of its time waiting on a database query. How can they optimize performance?

Accepted Answer

Optimize the database query and add appropriate indexes. Option C is correct because the bottleneck is identified as a database query causing high latency. Optimizing the query and adding appropriate indexes directly reduces the time spent waiting on the database, which is the root cause. Cloud Trace shows the service is waiting on the database, so improving database performance is the most effective solution.

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Decrease the number of pods to reduce load

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Use Cloud CDN to cache database results

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Increase the number of replicas for the service

Question 5

A company uses Cloud Run for a serverless application. They notice that cold starts are causing high latency for some requests. What is the best strategy to reduce cold starts?

Accepted Answer

Set a minimum number of instances to keep containers always warm. Option B is correct because setting a minimum number of instances ensures that Cloud Run keeps a baseline of container instances always warm and ready to serve requests. This eliminates cold starts for the first requests that hit those pre-warmed instances, directly addressing the latency issue. Cloud Run automatically scales to zero when idle, but a minimum instance setting overrides that behavior for the specified number of containers.

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Increase the max instances setting

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Migrate the application to Cloud Functions

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Reduce the container concurrency setting

Question 6

A team wants to monitor custom application metrics from a Compute Engine instance. They use the Cloud Monitoring agent. Which metric type should they use to report a gauge measurement like current memory usage?

Accepted Answer

gauge. Option D is correct because a gauge metric type is specifically designed to report a value that can arbitrarily increase or decrease over time, such as current memory usage. The Cloud Monitoring agent supports gauge metrics for point-in-time measurements, and they are reported as a single data point without any aggregation window, making them ideal for snapshot-like observations.

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histogram

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delta

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cumulative

Question 7

A company uses Cloud Monitoring to create an uptime check for their external HTTP endpoint. The check fails periodically even though the service is healthy. What is the most likely cause?

Accepted Answer

The firewall is blocking the uptime check IP ranges. The most likely cause is that the firewall is blocking the uptime check IP ranges. Cloud Monitoring uses specific source IP ranges for its uptime checks, and if these are not explicitly allowed through the firewall, the checks will fail even though the service itself is healthy. This is a common configuration issue where the firewall rules are not updated to include the monitoring system's probe IPs.

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The service is down during those periods

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The SSL certificate is expired

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The endpoint has high latency

Question 8

An application running on GKE uses a custom metric to track order processing time. The metric is exported via Prometheus and ingested by Cloud Monitoring using the Managed Service for Prometheus. The team wants to create an alert when the 95th percentile latency exceeds 2 seconds over a 5-minute window. Which PromQL query should be used?

Accepted Answer

histogram_quantile(0.95, rate(order_processing_duration_seconds_bucket[5m])). Option D is correct because `histogram_quantile(0.95, rate(order_processing_duration_seconds_bucket[5m]))` computes the 95th percentile latency over a 5-minute window using Prometheus histogram buckets. The `rate()` function calculates the per-second increase of each bucket, which is required for accurate quantile estimation from cumulative histograms, and the result directly gives the latency threshold below which 95% of requests fall.

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avg(rate(order_processing_duration_seconds_sum[5m])) / avg(rate(order_processing_duration_seconds_count[5m]))

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histogram_quantile(0.95, sum(rate(order_processing_duration_seconds_bucket[5m])))

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histogram_quantile(0.95, order_processing_duration_seconds_bucket)

Question 9

A company uses Cloud Logging to centralize logs from multiple projects. They want to create a log-based metric for tracking 404 errors. However, the metric shows zero data even though 404 errors are occurring. What is the most likely reason?

Accepted Answer

The logs are being excluded by an exclusion filter before the metric is applied. Option D is correct because log-based metrics are computed from logs that have passed through all exclusion filters. If an exclusion filter is configured to discard logs matching certain criteria (e.g., all HTTP 4xx responses), those logs are never evaluated by the metric filter, causing the metric to show zero data even though 404 errors are occurring. Exclusion filters are applied before log-based metric evaluation in Cloud Logging.

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The metric filter uses the wrong resource type

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The metric is sampled and not all logs are considered

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The logs are not being exported to Cloud Logging

Question 10

Which TWO are best practices for setting up alerting policies in Cloud Monitoring? (Choose two.)

Accepted Answer

Include documentation in the alert policy to guide responders. Option A is correct because including documentation in alert policies provides responders with immediate context, runbooks, and troubleshooting steps directly within the alert notification. This reduces mean time to resolution (MTTR) by ensuring responders have the necessary information without needing to search external sources. Cloud Monitoring supports embedding Markdown documentation in alert policies, which is a best practice for operational efficiency.

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Create separate alerts for each condition rather than combining them

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Avoid using notification channels to reduce noise

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Set all alerts to high severity to ensure visibility