PCD Managing application performance monitoring — Timed 50 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

Question 11

Which THREE are valid uses of Cloud Trace? (Choose three.)

Accepted Answer

Identifying latency bottlenecks in a distributed application. Cloud Trace is a distributed tracing system that captures latency data from applications, allowing you to identify performance bottlenecks across services. Option A is correct because Cloud Trace provides detailed traces that show the time spent in each component of a distributed application, enabling you to pinpoint where delays occur.

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Monitoring CPU usage of a Compute Engine instance

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Exporting traces to Prometheus for long-term storage

Question 12

Which TWO are correct ways to reduce logging costs in Google Cloud? (Choose two.)

Accepted Answer

Set log bucket retention to a shorter period. Option A is correct because reducing the retention period for log buckets directly decreases the amount of log data stored, which lowers storage costs in Cloud Logging. Logs are billed based on volume ingested and stored; shorter retention means older logs are deleted sooner, reducing the total storage footprint and associated charges.

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Disable all audit logs to reduce volume

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Export all logs to BigQuery for analysis

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Increase the retention period from 30 days to 365 days

Question 13

Your company runs a multi-tier web application on Google Kubernetes Engine (GKE). The application consists of a frontend service, a backend API service, and a PostgreSQL database managed by Cloud SQL. Recently, users have been reporting intermittent slow response times during peak hours (10 AM - 12 PM). You have set up Cloud Monitoring dashboards and alerts. Cloud Trace shows that the backend API service has high latency, but only for certain requests. You notice that the backend service's CPU utilization is around 60% during peak hours, and memory usage is normal. The Cloud SQL instance's CPU utilization is at 90% and the query latency is high. You have also observed that the backend service makes multiple database queries per request, some of which are repeated. What is the most effective course of action to reduce latency?

Accepted Answer

Increase the CPU and memory of the Cloud SQL instance to handle the load. The primary bottleneck is the Cloud SQL instance, which is running at 90% CPU with high query latency. Since the backend service's CPU is only at 60% and memory is normal, scaling the database directly addresses the root cause. Increasing the Cloud SQL instance's CPU and memory provides more processing power and connection capacity to handle the peak load, reducing query latency and overall response times.

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Scale up the backend API service by increasing the number of replicas

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Scale up the frontend service by increasing the number of replicas

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Implement a caching layer using Memorystore for Redis to cache database query results

Question 14

Your team manages a serverless application deployed on Cloud Run. The application processes image uploads and stores metadata in Firestore. You have set up a Cloud Monitoring alert based on the 'request_count' metric for the Cloud Run service. The alert triggers when the request count exceeds 1000 requests per minute. Recently, the alert has been firing frequently, but the team notices that the application is performing well and there are no errors. The team is concerned about alert fatigue. You review the metric and notice that the request count metric is based on all HTTP requests, including health checks from the Cloud Run system. The health check requests account for about 30% of the total requests. What should you do to reduce unnecessary alerts while still monitoring real user traffic?

Accepted Answer

Create a new log-based metric that filters out health check requests, and use that in the alert. Option B is correct because creating a new log-based metric that filters out health check requests allows you to monitor only real user traffic. Cloud Run's system health checks (e.g., from the Cloud Run infrastructure) are included in the default 'request_count' metric, inflating the count. By using a log-based metric with a filter that excludes these health check requests, you can set an accurate alert threshold based on actual user demand, reducing alert fatigue without losing visibility into real issues.

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Increase the alert threshold to 1500 requests per minute

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Disable health checks on the Cloud Run service

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Configure the existing metric to exclude health check logs

Question 15

Your application running on Google Kubernetes Engine (GKE) is experiencing intermittent latency spikes. You have enabled Cloud Monitoring and Cloud Logging. Which approach would be MOST effective to identify the root cause?

Accepted Answer

Use Cloud Trace to analyze distributed tracing data for slow requests.. Cloud Trace is the most effective tool for identifying intermittent latency spikes because it provides end-to-end distributed tracing, allowing you to pinpoint which specific service or request path is causing the delay. Unlike aggregate metrics or logs, Cloud Trace captures individual request spans and can reveal high-latency operations, such as slow database queries or external API calls, that occur only under certain conditions.

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Increase the number of replicas or switch to a larger machine type.

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Examine CPU and memory utilization metrics in Cloud Monitoring for the GKE cluster.

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Review recent Cloud Logging entries for error messages.

Question 16

A development team is using Cloud Monitoring to set up an alerting policy for a Compute Engine instance. They want to be notified when the instance's CPU utilization exceeds 80% for at least 5 minutes. Which alerting policy configuration should they use?

Accepted Answer

Condition type: Metric Threshold, Trigger: For 5 minutes, Threshold: 80%. Option A is correct because Cloud Monitoring alerting policies use a Metric Threshold condition type to evaluate a metric against a static threshold. Setting the trigger to 'For 5 minutes' ensures the condition is met only when the CPU utilization exceeds 80% consistently over the specified duration, preventing false alarms from transient spikes.

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Condition type: Metric Threshold, Trigger: For most recent value, Threshold: 80%

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Condition type: Change Rate, Trigger: For 5 minutes, Threshold: 80%

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Condition type: Metric Absence, Duration: 5 minutes

Question 17

Based on the Cloud Trace exhibit, which service is the primary contributor to the overall request latency?

Accepted Answer

The auth service. The Cloud Trace exhibit shows that the auth service accounts for the largest segment of the overall request latency, as indicated by the longest span duration in the trace waterfall. In Google Cloud Trace, each span represents a service's contribution to the total latency, and the service with the highest cumulative span time is the primary contributor. Since the auth service span is the longest, it is the correct answer.

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The productcatalog service

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The frontend service itself

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The recommendations service

Question 18

You are configuring a Cloud Monitoring alerting policy for a Cloud Run service. The service has a maximum of 10 concurrent requests per instance. You want to be alerted when the average number of concurrent requests per instance exceeds 8 for at least 1 minute. Which metric and condition type should you use?

Accepted Answer

Metric: run.googleapis.com/request_count, Condition type: Metric Threshold, Threshold: >8. Option A is correct because the `run.googleapis.com/request_count` metric tracks the number of concurrent requests per instance, which directly matches the requirement. A Metric Threshold condition with a threshold of >8 triggers an alert when the average exceeds 8 for at least 1 minute, aligning with the specified criteria.

Answer

Metric: run.googleapis.com/request_count, Condition type: Metric Absence, Duration: 1 min

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Metric: resource/container/cpu/utilization, Condition type: Metric Threshold, Threshold: >80%

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Metric: run.googleapis.com/request_count, Condition type: Change Rate, Threshold: >0.5

Question 19

You are designing a monitoring strategy for a microservices architecture running on GKE. Each service emits custom business metrics (e.g., order processing time). You want to create a dashboard that shows the 99th percentile latency for each service over the last 7 days. Which approach should you take?

Accepted Answer

Write custom metrics to Cloud Monitoring and create a dashboard with the 99th percentile aligner.. Option B is correct because Cloud Monitoring supports custom metrics and provides built-in aligners, including a 99th percentile aligner, which can be applied directly in a dashboard chart. This allows you to compute the 99th percentile latency for each service over the last 7 days without manual calculation or exporting logs. Custom metrics are the appropriate mechanism for business metrics like order processing time, as they are designed for numeric time-series data.

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Export logs to Cloud Logging and use Log Analytics to compute percentiles.

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Use Metrics Explorer to view the metrics and manually compute percentiles.

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Use Prometheus monitoring built into GKE and query the avg() function.

Question 20

Which TWO actions are best practices for managing application performance monitoring in Google Cloud?

Accepted Answer

Set appropriate sampling rates for Cloud Trace to balance cost and insight.. Option A is correct because Cloud Trace charges based on the number of spans ingested and retained. Setting an appropriate sampling rate (e.g., 1 in 10 requests for high-traffic services) reduces cost while still providing statistically significant latency data for performance analysis. This balances the need for actionable insights against budget constraints.

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Enable debug logging in production to capture detailed errors.

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Log all application activity at INFO level for complete visibility.

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Configure alerts to send emails to the entire development team.