Troubleshooting practice questions

You are troubleshooting a web application deployed on Compute Engine instances behind a target pool. Users report intermittent timeouts when accessing the application via the forwarding rule's IP address. Based on the exhibit, what is the most likely cause of the issue?

Network Topology

Trap 1: The forwarding rule is missing a backend service.

Target pools do not use backend services.

Trap 2: The port range is set to 80-80, which restricts traffic to port 80…

Port 80 is correct for HTTP; this would not cause timeouts.

Trap 3: The forwarding rule should use a backend service instead of a…

Target pools can handle HTTP traffic, but health checks are still needed.

A
The forwarding rule is missing a backend service.
Why wrong: Target pools do not use backend services.
B
The target pool lacks health checks, causing traffic to be sent to unhealthy instances.
Target pools rely on health checks to stop routing to unhealthy instances; without them, traffic may be routed to failed instances.
C
The port range is set to 80-80, which restricts traffic to port 80 only.
Why wrong: Port 80 is correct for HTTP; this would not cause timeouts.
D
The forwarding rule should use a backend service instead of a target pool for HTTP traffic.
Why wrong: Target pools can handle HTTP traffic, but health checks are still needed.

Question 2easymulti select

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

Trap 1: Create separate alerts for each condition rather than combining them

Combining conditions reduces alert fatigue.

Trap 2: Avoid using notification channels to reduce noise

Notification channels are necessary to alert responders.

Trap 3: Set all alerts to high severity to ensure visibility

Using high severity for all alerts reduces their effectiveness.

A
Include documentation in the alert policy to guide responders
Documentation helps responders take correct action.
B
Create separate alerts for each condition rather than combining them
Why wrong: Combining conditions reduces alert fatigue.
C
Define clear notification channels for different severity levels
Proper routing ensures the right people are notified.
D
Avoid using notification channels to reduce noise
Why wrong: Notification channels are necessary to alert responders.
E
Set all alerts to high severity to ensure visibility
Why wrong: Using high severity for all alerts reduces their effectiveness.

Question 3mediumdrag order

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

Question 4mediummultiple choice

A developer is troubleshooting a deployment on Cloud Run. The service fails with 'Container failed to start' error. The container image is built from a Dockerfile that uses CMD ['npm', 'start']. What is the most likely cause?

Trap 1: The Dockerfile uses CMD instead of ENTRYPOINT.

Both are acceptable; CMD is fine when used with ENTRYPOINT or alone.

Trap 2: The container image is too large and exceeds the memory limit.

This would cause a different error or timeout, not a container start failure.

Trap 3: The Cloud Run service does not have permission to pull the image…

That would cause an image pull error, not a container start failure.

A
The Dockerfile uses CMD instead of ENTRYPOINT.
Why wrong: Both are acceptable; CMD is fine when used with ENTRYPOINT or alone.
B
The container image is too large and exceeds the memory limit.
Why wrong: This would cause a different error or timeout, not a container start failure.
C
The application does not listen on the port specified by the PORT environment variable.
Cloud Run expects the container to listen on the port defined by the PORT env var (default 8080). If the app listens on another port, it fails.
D
The Cloud Run service does not have permission to pull the image from Container Registry.
Why wrong: That would cause an image pull error, not a container start failure.

Question 5mediummulti select

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?

Trap 1: Error Reporting

Error Reporting aggregates errors but does not trace performance latency across services.

Trap 2: Cloud Profiler

Cloud Profiler profiles code-level CPU and memory usage, not request traces across services.

Trap 3: Cloud Logging

Cloud Logging provides individual log entries but does not directly trace a request's full path across services.

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.

Question 6hardmulti select

Which THREE of the following are valid reasons to use Cloud Deploy instead of manually applying kubectl commands in a CI/CD pipeline?

Trap 1: Cloud Deploy automatically containerizes applications.

Cloud Deploy expects already containerized apps.

Trap 2: Cloud Deploy enforces IAM roles on Kubernetes clusters.

IAM can be used with kubectl as well.

A
Cloud Deploy automatically containerizes applications.
Why wrong: Cloud Deploy expects already containerized apps.
B
Cloud Deploy maintains a deployment history for auditing.
Audit trail is built-in.
C
Cloud Deploy enforces IAM roles on Kubernetes clusters.
Why wrong: IAM can be used with kubectl as well.
D
Cloud Deploy provides automatic rollbacks on deployment failure.
Automated rollback is a key feature.
E
Cloud Deploy supports canary and blue-green deployments out of the box.
Advanced deployment strategies are supported.

Question 7mediummultiple choice

A company has a monolithic application that needs to be migrated to Cloud Run. The application currently writes logs to a local file. What is the best practice for handling logs in Cloud Run?

Trap 1: Use a third-party logging agent installed in the container image.

Not necessary and adds complexity; built-in logging suffices.

Trap 2: Use a sidecar container to ship logs to Stackdriver.

Sidecar is not needed; Cloud Run natively captures stdout/stderr.

Trap 3: Continue writing to a local file; Cloud Run will persist it.

Filesystem is ephemeral and not persisted across requests.

A
Use a third-party logging agent installed in the container image.
Why wrong: Not necessary and adds complexity; built-in logging suffices.
B
Write logs to stdout and stderr; Cloud Run automatically sends them to Cloud Logging.
Cloud Run collects stdout and stderr and sends them to Cloud Logging.
C
Use a sidecar container to ship logs to Stackdriver.
Why wrong: Sidecar is not needed; Cloud Run natively captures stdout/stderr.
D
Continue writing to a local file; Cloud Run will persist it.
Why wrong: Filesystem is ephemeral and not persisted across requests.

Read the full network assurance explanation →

Question 8mediummultiple choice

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?

Trap 1: Keep monitoring separate and use separate dashboards for legacy and…

Separate dashboards do not provide a unified view and hinder correlation.

Trap 2: Use a third-party APM tool that supports both environments.

While possible, it's not leveraging native Google Cloud tools and may add complexity.

Trap 3: Rewrite the legacy app to use OpenTelemetry.

Rewriting is costly and time-consuming, not necessary.

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.