PCD Practice Test 5 — 10 Questions

Question 1

A company is designing a microservices application. They want to ensure that if one service fails, it does not cascade to other services. Which pattern should they implement?

Accepted Answer

Circuit Breaker. The Circuit Breaker pattern is the correct choice because it prevents cascading failures by monitoring service calls and opening the circuit when failures exceed a threshold, allowing the system to fail fast and avoid resource exhaustion. This pattern directly addresses the requirement to isolate failures between microservices, ensuring that a failure in one service does not propagate to others.

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Auto-scaling

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Retry with exponential backoff

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Load shedding

Question 2

Match each Cloud Storage class to its typical use case.

Accepted Answer

Standard: Frequently accessed data. Cloud Storage classes optimize cost based on access frequency. Standard is for hot data, Nearline for monthly, Coldline for quarterly, and Archive for yearly access. Common mistakes involve confusing the frequency thresholds.

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Standard: Data accessed less than once a year

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Nearline: Data accessed less than once a quarter

Question 3

Which THREE are best practices for building applications on GKE? (Choose three.)

Accepted Answer

Set resource requests and limits for CPU and memory. Option A is correct because setting CPU and memory requests lets the scheduler place pods on nodes with sufficient capacity, while limits prevent a single container from starving others and enable proper QoS classes and autoscaling decisions. Option C is correct because readiness probes ensure traffic only reaches containers that are ready to serve, and liveness probes let kubelet restart containers that are stuck or deadlocked, improving availability and reliability. Option E is correct because Google-managed SSL certificates integrate with GKE Ingress to automatically provision and renew TLS certificates for HTTPS, reducing operational overhead and avoiding expired-certificate outages. Option B is not a best practice because pinning pods to specific node instances via nodeSelector reduces scheduling flexibility and resilience, and performance consistency is better achieved with node affinity, taints/tolerations, or dedicated node pools. Option D is not a best practice because StatefulSets are intended for stateful workloads needing stable identities and persistent storage, while stateless applications should generally use Deployments for simpler scaling and rolling updates.

Answer

Use nodeSelector to pin pods to specific node instances for performance consistency

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Use StatefulSets for all applications to preserve state across restarts

Question 4

A developer is writing a Cloud Function in Node.js that is triggered by HTTP. They want to test the function locally by sending an HTTP request. Which tool should they use?

Accepted Answer

The Functions Framework for Node.js.. The Functions Framework for Node.js allows developers to run HTTP-triggered Cloud Functions locally. It starts a local server that listens for HTTP requests and invokes the function, mimicking the production environment. This enables quick iteration and testing without deployment. Other options reference non-existent emulators or commands that require deployment, so they are not suitable for local testing.

Answer

The gcloud functions invoke command.

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The Cloud Run emulator.

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The Cloud Functions emulator.

Question 5

A developer is building a containerized application that will be deployed to Google Kubernetes Engine (GKE). They want to run unit tests for a Go package that interacts with the Kubernetes API. Which approach should they use to test the code without a running cluster?

Accepted Answer

Use the client-go library's fake clientset to simulate Kubernetes API interactions, and write tests that use the fake clientset to verify behavior.. The client-go library includes a fake clientset that implements the Kubernetes API interfaces. It allows you to simulate API operations, such as creating, updating, and deleting resources, without a real cluster. Tests can use the fake clientset to verify that the code interacts with the API correctly, including error handling. This approach is fast, reliable, and does not require external dependencies.

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Use the kubectl proxy command to create a local proxy to a remote cluster, and point the tests to the proxy.

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Start a local Kubernetes cluster using Minikube and run the tests against it, then tear it down after the tests complete.

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Mock the HTTP calls to the Kubernetes API server using a library like httptest, and return predefined JSON responses.

Question 6

A company is developing a microservices application on Google Cloud. Each service is deployed as a Docker container on Cloud Run. The development team wants to ensure that inter-service communication is encrypted and authenticated. What is the best approach?

Accepted Answer

Use Cloud Run's built-in IAM-based authentication and automatic TLS for internal requests.. Cloud Run automatically provisions TLS certificates for all incoming requests and supports IAM-based authentication for internal requests between services in the same Google Cloud project. This means inter-service communication is encrypted by default via HTTPS and can be authenticated by configuring the receiving service to require a valid IAM token from the caller, without any additional infrastructure or sidecar proxies.

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Configure mutual TLS (mTLS) between services using Cloud Endpoints.

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Deploy a sidecar proxy on each Cloud Run service to handle TLS termination.

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Assign a service account to each service and use its private key to sign requests.

Question 7

You are managing a microservices application deployed on Google Kubernetes Engine (GKE) that uses Cloud Monitoring and Cloud Logging. Recently, users have reported intermittent slow response times, especially during peak hours. You have enabled the Ops Agent on GKE nodes and configured custom metrics for your services. The application consists of a frontend service, a backend API service, and a database service. The frontend calls the backend, which in turn queries the database. You notice that when the response time spikes, the frontend service's CPU utilization remains low, but the backend service's CPU utilization increases. The database service shows normal latency and no errors. You have examined the logs and found no application errors. The GKE cluster has three node pools: one for each service, with autoscaling enabled. The backend service is configured with a HorizontalPodAutoscaler (HPA) based on CPU utilization, but the HPA does not seem to scale up quickly enough during traffic spikes. You want to identify the root cause of the performance degradation. Which course of action should you take first?

Accepted Answer

Analyze the backend service's request latency distribution using Cloud Monitoring metrics to identify whether the issue is due to increased request volume or slow request processing.. The intermittent slow response times during peak hours, combined with low frontend CPU but high backend CPU and normal database latency, strongly suggest the backend service is struggling to process requests quickly under load. Analyzing the backend's request latency distribution using Cloud Monitoring metrics (e.g., 99th percentile latency) will reveal whether the issue stems from increased request volume (which would show a shift in latency distribution) or from individual requests taking longer to process (e.g., due to inefficient code or resource contention). This diagnostic step directly addresses the symptom without making assumptions about scaling or network issues.

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Check the network latency between the frontend and backend services using Cloud Monitoring's network metrics.

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Configure the backend service's HPA to use custom metrics based on request latency instead of CPU utilization.

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Increase the minimum number of replicas for the backend service to handle peak traffic.

Question 8

Match each Kubernetes resource to its function.

Accepted Answer

Pod: The smallest and simplest Kubernetes object. It represents a single instance of a running process in your cluster.. These are fundamental Kubernetes objects for running containerized applications.

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ConfigMap: Stores sensitive data such as passwords, OAuth tokens, and ssh keys.

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Secret: Manages external access to the services in a cluster, typically HTTP.

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Ingress: Allows you to decouple configuration artifacts from image content to keep containerized applications portable.

Question 9

A company uses Cloud Bigtable for AdTech clickstream data. They notice that some tables have many deleted rows and the storage cost is increasing. What should they configure to automatically remove deleted data and reduce storage?

Accepted Answer

Configure a garbage collection policy based on time or number of versions. Cloud Bigtable automatically compacts data in the background, but deleted rows are not immediately removed; they remain as tombstones until a garbage collection policy is applied. By configuring a garbage collection policy based on time or number of versions, you instruct Bigtable to permanently delete expired data and reclaim storage space, reducing costs.

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Set up cluster replication to a secondary cluster

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Enable compaction on the table

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Use IAM conditions to restrict write access

Question 10

A Cloud Spanner instance is configured with 1000 processing units. The workload has unpredictable traffic spikes. To ensure consistent performance, the team wants to automatically adjust capacity based on a high-priority CPU target of 60%. What should they configure?

Accepted Answer

Set up Cloud Spanner autoscaling with min=500, max=2000 processing units and high-priority CPU target=0.6.. Cloud Spanner autoscaling lets you define a minimum and maximum processing-unit range plus a high-priority CPU utilization target, and the service automatically scales capacity within that range to keep CPU near the target. Setting min=500, max=2000 with a 0.6 high-priority CPU target directly matches the requirement for automatic, performance-driven capacity adjustment during unpredictable spikes.

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Configure compute capacity with a fixed number of nodes.

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Enable Bigtable autoscaling for the Spanner instance.

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Manually adjust nodes when CPU exceeds 60%.