PCD Practice Questions 2 — 50 Questions

Question 1

A company is deploying a microservices-based application on Google Kubernetes Engine (GKE). The application consists of several stateless services that experience unpredictable traffic spikes. The team wants to ensure high availability and scalability while minimizing costs. Which design should they implement?

Accepted Answer

Deploy a Regional GKE cluster with cluster autoscaling and Horizontal Pod Autoscaler for each deployment.. Option C is correct because a Regional GKE cluster provides multi-zone high availability, cluster autoscaling dynamically adjusts node pool size to handle unpredictable traffic spikes, and Horizontal Pod Autoscaler (HPA) scales individual pod replicas based on CPU/memory or custom metrics. This combination ensures both scalability and cost efficiency by only provisioning resources when needed.

Answer

Deploy a Regional GKE cluster with node auto-provisioning and a fixed number of replicas per service.

Answer

Use a Regional GKE cluster with preemptible VMs and static pod counts.

Answer

Use a single-zone GKE cluster with a large fixed node pool to handle peak load.

Question 2

Which TWO actions should a developer take to ensure that a Cloud Run service can access a Cloud SQL instance securely?

Accepted Answer

Use the Cloud SQL Auth Proxy as a sidecar container in the same pod.. Option C is correct because the Cloud SQL Auth Proxy, when deployed as a sidecar container in the same pod, provides encrypted connections and IAM-based authentication to Cloud SQL without requiring a public IP or complex network configuration. It automatically handles TLS 1.3 encryption and uses the service account's IAM permissions to authorize connections, ensuring secure access from Cloud Run.

Answer

Use a Cloud NAT to provide outbound internet access for the service.

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Assign a public IP to the Cloud SQL instance and allow all traffic from Cloud Run.

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Create a service account with the cloudsql.instances.connect permission.

Question 3

A team is building a serverless event-driven application using Cloud Functions and Cloud Pub/Sub. The function processes messages from a Pub/Sub subscription and writes results to Firestore. During peak hours, the function experiences high latency and some messages are being retried multiple times. Which three steps should the team take to improve reliability and scalability? (Choose three.)

Accepted Answer

Enable retry policy on the Pub/Sub subscription to automatically retry failed messages.. Option A is correct because enabling a retry policy on the Pub/Sub subscription ensures that messages that fail to be processed (e.g., due to transient errors or timeouts) are automatically retried. This prevents message loss and improves reliability by allowing the Cloud Function to reprocess messages without manual intervention. The retry policy works with the subscription's acknowledgement deadline, so messages are redelivered if not acknowledged in time.

Answer

Batch multiple Pub/Sub messages into a single Cloud Function invocation.

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Increase the Cloud Function timeout to the maximum allowed value.

Question 4

A DevOps team wants to set up custom metrics for a serverless application running on Cloud Run. The application emits metrics using OpenTelemetry. They need to collect these metrics and create an alerting policy that triggers when the 99th percentile latency exceeds 500ms for 5 minutes. Which TWO actions must they take? (Choose two.)

Accepted Answer

Create a custom distribution metric for the latency data and set up a metric threshold alert using the 99th percentile value.. Option A is correct because to alert on the 99th percentile of latency, you must create a custom distribution metric, which stores a histogram of values and allows percentile calculations. A metric-threshold alert policy can then be configured to evaluate the 99th percentile value against the 500ms threshold over a 5-minute window.

Answer

Install the Cloud Monitoring agent on the Cloud Run instance to collect custom metrics.

Answer

Define a log-based metric from the application logs that captures latency entries.

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Configure the Cloud Monitoring dashboard to query the metrics using PromQL.

Question 5

A company is designing a globally distributed application using Cloud Spanner. The application requires strong consistency and the ability to handle high read/write throughput. The team is concerned about inter-continental latency. Which two design choices would optimize performance while maintaining strong consistency? (Choose two.)

Accepted Answer

Enable leader-optimized routing to direct reads to the nearest leader region.. A is correct because leader-optimized routing directs read requests to the nearest region that contains the leader replica for the requested data, reducing inter-continental latency while still reading from the leader to ensure strong consistency. C is correct because placing a multi-region Spanner instance in geographic locations close to users minimizes network round-trip time, and Spanner's synchronous replication across regions maintains strong consistency even with high read/write throughput.

Answer

Use read-only replicas in each continent to serve reads locally.

Answer

Implement client-side caching with a short TTL for frequently accessed data.

Answer

Increase the number of nodes in the Spanner instance to improve throughput.

Question 6

You are designing a serverless application using Cloud Functions that processes events from Cloud Storage and Cloud Pub/Sub. The function must be idempotent and handle duplicate events. Which three best practices should you implement? (Choose THREE.)

Accepted Answer

Generate a unique idempotency key for each event and store processed keys in a database.. Option A is correct because generating a unique idempotency key for each event and storing processed keys in a database (such as Cloud Firestore) ensures that if the same event is delivered multiple times (e.g., due to at-least-once delivery semantics in Cloud Pub/Sub or Cloud Storage notifications), the function can check the key before processing and skip duplicates. This pattern is essential for idempotent serverless functions, as Cloud Functions may be retried on failure or receive duplicate events from the source.

Answer

Invoke the function synchronously to avoid duplicates.

Answer

Set the function timeout to maximum (540 seconds) to ensure processing completes.

Question 7

A company is using Cloud Storage to store sensitive customer data. They need to ensure data is encrypted at rest and access is controlled. Which TWO statements are true regarding data protection in Cloud Storage? (Choose two.)

Accepted Answer

By default, data in Cloud Storage is encrypted at rest using Google-managed encryption keys.. Option A is correct because Cloud Storage automatically encrypts all data at rest using server-side encryption with Google-managed encryption keys by default, without any additional configuration. This ensures that data is protected before it is written to disk and remains encrypted throughout its lifecycle.

Answer

Using a signed URL revokes the underlying object's ACL.

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Enabling uniform bucket-level access disables encryption at rest.

Answer

Bucket-level policies can restrict access to only compute instances with specific service accounts.

Question 8

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.

Answer

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

Answer

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

Answer

Condition type: Metric Absence, Duration: 5 minutes

Question 9

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

Answer

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

Answer

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

Question 10

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.

Answer

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 11

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.

Answer

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 12

Match each Cloud Storage class to its typical use case.

Question 13

Match each IAM role to its description.

Question 14

Match each Cloud CDN feature to its benefit.

Question 15

Match each Cloud Logging and Monitoring concept to its definition.

Question 16

Match each Google Cloud service to its primary purpose.

Question 17

Match each Cloud SQL database engine to its description.

Question 18

Match each command-line tool to its primary use.

Question 19

Match each error code to its meaning in Google Cloud.

Question 20

A team is migrating a monolithic .NET application to Cloud Run. The application uses .NET Framework 4.8 and depends on Windows-specific libraries. What is the recommended approach to containerize and deploy this application?

Accepted Answer

Port the application to .NET Core/.NET 6+ and run on Linux. Cloud Run only supports Linux containers, so a .NET Framework 4.8 application that depends on Windows-specific libraries cannot be directly deployed. The recommended approach is to port the application to .NET Core/.NET 6+ (now .NET 8/9), which is cross-platform and can run on Linux containers, enabling deployment to Cloud Run. This aligns with Google's guidance for modernizing legacy .NET applications to leverage serverless platforms.

Answer

Deploy the application on Compute Engine with Windows Server

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Use Cloud Run for Anthos on a Windows node pool

Answer

Use a Windows base image and deploy to Cloud Run