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This page has 10 Troubleshooting Scenario Questions scenario questions for the PCD exam, each with detailed explanations and wrong-answer analysis.

How should I approach PCD scenario questions?

Read the full scenario before looking at the answer options. Identify the constraint or requirement in the scenario, then eliminate options that are generally true but wrong for this specific case. Scenario questions reward careful reading over pattern matching.

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Scenario-based practice

Troubleshooting Scenario Questions

Practise Google Professional Cloud Developer practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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Scenario guide

How to approach troubleshooting scenario questions

These questions describe a network symptom and ask you to identify the root cause or the correct fix. They appear across all certification exams and reward systematic thinking over memorisation. The best candidates follow a consistent troubleshooting framework even under time pressure.

Quick answer

Troubleshooting Scenario Questions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Practice scenarios

Question 1hardmultiple choice

Full question →

Refer to the exhibit. A developer runs the above command to deploy a Cloud Function triggered by Pub/Sub. The function fails to execute when a message is published. The logs show: "Function execution took 60001 ms, finished with status: 'timeout'". What should the developer do?

Network Topology

A
Change the trigger to HTTP
Why wrong: Changing the trigger type changes the event source but doesn't fix the timeout issue.
B
Reduce the number of function instances
Why wrong: Instance count affects concurrency, not execution time per request.
C
Check the function code for long-running operations
The timeout indicates the function is taking too long; the proper fix is to optimize the code to complete within the allowed time.
D
Increase the function timeout to 9 minutes
Why wrong: Increasing timeout is a temporary fix; the root cause is the slow code that should be optimized.

Full breakdown with real-world context →

Question 2mediummultiple choice

Full question →

Refer to the exhibit. A Cloud Run service is unable to connect to a Cloud SQL instance. The log entry shows the following. What is the most likely cause?

Exhibit

{
  "resource.type": "cloud_run_revision",
  "severity": "ERROR",
  "textPayload": "Error: [project:us-central1:myinstance] cannot connect: connection refused"
}

A
The Cloud Run service account lacks the Cloud SQL Client role.
Without the cloudsql.client role, the VPC connector cannot authorize the connection, leading to a connection refused error.
B
The database user credentials are incorrect.
Why wrong: Incorrect credentials result in an authentication error, not a connection refused at the network level.
C
The Cloud SQL instance is in a different region than the Cloud Run service.
Why wrong: Cross-region connections are possible; the error would typically be a timeout, not a connection refused.
D
The Cloud SQL instance has a public IP assigned.
Why wrong: A public IP would not cause a connection refused by itself; the service would still try to connect, and if allowed, it would succeed.

Full breakdown with real-world context →

Question 3easymultiple choice

Full question →

Refer to the exhibit. A developer writes the above Dockerfile for a Cloud Run service. The service fails to start. The logs indicate that the container exited immediately. What is the most likely cause?

Network Topology

A
The WORKDIR is set to a directory that doesn't exist
Why wrong: WORKDIR creates the directory if it doesn't exist; this is not the cause.
B
The server.js file is missing from the build context
If server.js is not copied into the image (e.g., it's in a different directory or excluded by .dockerignore), the container has no entrypoint and exits immediately.
C
The CMD instruction is incorrectly formatted
Why wrong: The CMD is correctly written as JSON array; this is not the issue.
D
The EXPOSE 8080 instruction is unnecessary and may cause conflicts
Why wrong: EXPOSE is just documentation; it does not affect container startup.

Full breakdown with real-world context →

Question 4easymultiple choice

Full question →

A developer deploys the above app.yaml to App Engine standard environment. The deployment succeeds, but the application fails to connect to the database. What is the most likely reason?

Exhibit

Refer to the exhibit.
```
# app.yaml for App Engine standard environment
runtime: python39
entrypoint: gunicorn -b :$PORT main:app
env_variables:
  DATABASE_URL: "postgresql://user:pass@localhost/mydb"
```

A
The runtime 'python39' is not supported in App Engine standard environment.
Why wrong: Python 3.9 is a supported runtime.
B
The $PORT environment variable is not set in App Engine standard environment.
Why wrong: App Engine sets the PORT environment variable.
C
The application is trying to connect to a local database on localhost, which is not available in the App Engine sandbox.
App Engine standard does not allow connections to localhost; use Cloud SQL.
D
The entrypoint command is incorrect because gunicorn is not allowed.
Why wrong: gunicorn is allowed as an entrypoint.

Full breakdown with real-world context →

Question 5hardmulti select

Full question →

A DevOps team is troubleshooting high latency in a Cloud Bigtable instance. They notice that the row keys are lexicographically sorted timestamps. Which TWO actions will MOST improve performance? (Select 2 answers)

A
Add more Bigtable nodes
Why wrong: Helps throughput but does not fix hotspotting from row key design.
B
Increase the number of column families
Why wrong: Does not address row key hotspotting.
C
Enable compression on column families
Why wrong: Reduces storage, not latency from hotspotting.
D
Use salt (hash prefix) in the row key
Randomizes row key distribution, reducing hotspots.
E
Redesign the row key to include a field promotion (e.g., user ID) before the timestamp
Distributes writes across tablets.

Full breakdown with real-world context →

Question 6hardmultiple choice

Full question →

You are a developer for an e-commerce platform running on Google Kubernetes Engine (GKE) with a Cloud SQL backend. The application uses Cloud Memorystore for Redis for session caching. During a flash sale, you notice that the application latency spikes and some users are unable to complete checkout. You suspect the Redis instance is overwhelmed. The Redis instance is currently a Standard tier instance with 5 GB of memory. You need to increase throughput without significant architectural changes. You have the following options: A) Use client-side caching to reduce load on the Redis instance. B) Switch to a Memorystore Standard tier instance with a higher capacity and enable scaling. C) Migrate to a Memorystore Basic tier instance with a larger memory size. D) Enable for Redis clustering on the existing instance to distribute load across shards. Which option should you choose?

A
Use client-side caching to reduce load on the Redis instance.
Why wrong: Migrating to a Basic tier instance with larger memory does not increase throughput significantly; Basic tier has lower performance and lacks scaling features.
B
Switch to a Memorystore Standard tier instance with a higher capacity and enable scaling.
Enabling clustering on an existing instance is not possible without creating a new clustered instance; this requires significant architectural changes.
C
Migrate to a Memorystore Basic tier instance with a larger memory size.
Why wrong: Switching to a Standard tier instance with higher capacity and enabling scaling increases throughput directly without architectural changes.
D
Enable for Redis clustering on the existing instance to distribute load across shards.
Why wrong: Client-side caching reduces load but does not increase Redis throughput; it requires application changes and is not a direct solution for Redis overload.

Full breakdown with real-world context →

Question 7mediummultiple choice

Read the full network assurance explanation →

A company has a legacy monolithic application running on Compute Engine that is being migrated to microservices on GKE. During the migration, they need to maintain performance monitoring across both environments. The legacy application uses Stackdriver Logging and Monitoring agents (now Ops Agent) and exports logs to Cloud Logging. The new microservices are instrumented with OpenTelemetry for traces and metrics. The team wants a unified view of performance across both environments, including distributed traces from the new services and log-based metrics from the legacy app. They also want to correlate logs and traces for troubleshooting. Which solution should they implement?

A
Keep monitoring separate and use separate dashboards for legacy and new.
Why wrong: Separate dashboards do not provide a unified view and hinder correlation.
B
Use a third-party APM tool that supports both environments.
Why wrong: While possible, it's not leveraging native Google Cloud tools and may add complexity.
C
Use Cloud Monitoring dashboards and ingest OpenTelemetry metrics into Cloud Monitoring, while using Cloud Logging log-based metrics from legacy app.
This approach unifies metrics and logs from both environments, enabling correlation.
D
Rewrite the legacy app to use OpenTelemetry.
Why wrong: Rewriting is costly and time-consuming, not necessary.

Full breakdown with real-world context →

Question 8mediumdrag order

Full question →

Drag and drop the steps to troubleshoot a failed Cloud Build in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

A
Check Cloud Build logs first, then examine build configuration, then fix identified issues, then re-run the build.
This is the correct order because logs provide the immediate error details, then configuration review helps identify misconfigurations, fixing addresses the root cause, and re-running validates the solution.
B
Re-run the build first, then check logs, then examine configuration, then fix issues.
Why wrong: This is incorrect because re-running without investigation is unlikely to resolve the issue and wastes time; logs should be checked first to understand the failure.
C
Examine build configuration first, then re-run the build, then check logs, then fix issues.
Why wrong: This is incorrect because examining configuration without logs may miss runtime errors; logs provide the specific error pointing to what went wrong.
D
Fix issues first, then check logs, then examine configuration, then re-run.
Why wrong: This is incorrect because fixing issues without understanding the failure (logs/configuration) is guesswork; you must diagnose before attempting a fix.

Full breakdown with real-world context →

Question 9mediummulti select

Full question →

You are troubleshooting a performance issue in a microservices application. Which TWO tools from Google Cloud's operations suite would you use to trace a request across services and identify the slowest component?

A
Cloud Monitoring
Cloud Monitoring can display latency heatmaps and service graphs that help visualize the slowest component in a distributed trace.
B
Error Reporting
Why wrong: Error Reporting aggregates errors but does not trace performance latency across services.
C
Cloud Profiler
Why wrong: Cloud Profiler profiles code-level CPU and memory usage, not request traces across services.
D
Cloud Logging
Why wrong: Cloud Logging provides individual log entries but does not directly trace a request's full path across services.
E
Cloud Trace
Cloud Trace is the primary tool for distributed tracing, capturing end-to-end latency for each request and showing spans for each service call.

Full breakdown with real-world context →

Question 10mediummultiple choice

Full question →

A developer deploying a new version of a microservice sees a sudden increase in error logs in Cloud Logging. The errors are 500 responses from the service. What is the most efficient way to investigate the root cause?

A
Use Cloud Trace to view the trace of failed requests
Cloud Trace records traces for each request, including errors, allowing you to see the exact step that failed.
B
Revert to the previous version immediately
Why wrong: Rollback may restore service but fails to identify the root cause, risking recurrence.
C
Check the CPU and memory metrics in Cloud Monitoring
Why wrong: Resource metrics may show saturation but do not directly explain the logic errors causing 500 responses; they are less efficient for this symptom.
D
Analyze the error logs using Log Analytics and create a log-based metric
Why wrong: While log analysis can reveal error patterns, it is slower than tracing and does not provide the full request context.

Full breakdown with real-world context →

These PCD practice questions are part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style PCD questions with detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics.

Troubleshooting Scenario Questions

How to approach troubleshooting scenario questions

Quick answer

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Practice scenarios

Refer to the exhibit. A developer runs the above command to deploy a Cloud Function triggered by Pub/Sub. The function fails to execute when a message is published. The logs show: "Function execution took 60001 ms, finished with status: 'timeout'". What should the developer do?

Refer to the exhibit. A Cloud Run service is unable to connect to a Cloud SQL instance. The log entry shows the following. What is the most likely cause?

Exhibit

Refer to the exhibit. A developer writes the above Dockerfile for a Cloud Run service. The service fails to start. The logs indicate that the container exited immediately. What is the most likely cause?

A developer deploys the above app.yaml to App Engine standard environment. The deployment succeeds, but the application fails to connect to the database. What is the most likely reason?

Exhibit

A DevOps team is troubleshooting high latency in a Cloud Bigtable instance. They notice that the row keys are lexicographically sorted timestamps. Which TWO actions will MOST improve performance? (Select 2 answers)

Drag and drop the steps to troubleshoot a failed Cloud Build in the correct order.

You are troubleshooting a performance issue in a microservices application. Which TWO tools from Google Cloud's operations suite would you use to trace a request across services and identify the slowest component?

A developer deploying a new version of a microservice sees a sudden increase in error logs in Cloud Logging. The errors are 500 responses from the service. What is the most efficient way to investigate the root cause?