PCD Practice Test 10 — 10 Questions

Question 1

A team is implementing a CI/CD pipeline for a Cloud Function using Cloud Build. Which three steps should they include in their cloudbuild.yaml? (Choose 3)

Accepted Answer

Static code analysis. Static code analysis (A) is correct because it helps identify code quality issues, security vulnerabilities, and adherence to coding standards early in the pipeline, which is a best practice for Cloud Functions. Running unit tests (C) is essential to validate function logic before deployment. Deploying the function (B) is the final step that pushes the validated code to Cloud Functions, making it a required step in the CI/CD pipeline.

Answer

Build a container image

Answer

Manual approval step

Question 2

Drag and drop the steps to deploy a containerized application to Google Kubernetes Engine (GKE) in the correct order.

Accepted Answer

Create GKE cluster, authenticate kubectl with the cluster, apply Kubernetes manifest files, expose the application via Service or Ingress. Deploying to GKE requires creating a cluster, authenticating, then applying manifests and exposing the service.

Answer

Authenticate kubectl with the cluster, create GKE cluster, apply Kubernetes manifest files, expose the application via Service or Ingress

Answer

Create GKE cluster, apply Kubernetes manifest files, authenticate kubectl with the cluster, expose the application via Service or Ingress

Answer

Create GKE cluster, expose the application via Service or Ingress, authenticate kubectl with the cluster, apply Kubernetes manifest files

Question 3

Drag and drop the steps to configure a Cloud Storage bucket with uniform bucket-level access in the correct order.

Accepted Answer

Create the bucket, then enable uniform bucket-level access (either during creation or after), then configure IAM policies. Uniform bucket-level access is configured during bucket creation by selecting the appropriate access control settings.

Answer

Configure IAM policies first, then create the bucket, then enable uniform bucket-level access

Answer

Enable uniform bucket-level access globally, then create the bucket, then configure IAM policies

Answer

Create the bucket with fine-grained access, then configure IAM policies, then change to uniform bucket-level access

Question 4

A startup wants to deploy a web application on App Engine standard environment. They need to handle sudden traffic spikes automatically. How should they configure scaling?

Accepted Answer

Use automatic scaling.. Automatic scaling is the correct choice because App Engine standard environment natively supports automatic scaling, which dynamically adds or removes instances based on request rate, latency, and other metrics. This allows the application to handle sudden traffic spikes without manual intervention, as the platform automatically provisions resources to meet demand.

Answer

Use basic scaling with idle timeout.

Answer

Use manual scaling with a fixed number of instances.

Answer

Use a combination of manual and automatic scaling.

Question 5

A company uses Cloud SQL for PostgreSQL and wants to run periodic analytical queries on the data without impacting the transactional workload. The data is updated frequently. Which integration approach is most suitable?

Accepted Answer

Create a read replica of the Cloud SQL instance and point analytical queries to the replica.. A Cloud SQL read replica is a continuously updated copy of the primary instance that serves read-only traffic, so analytical queries can be offloaded to it without competing with the transactional workload on the primary. Because replication is asynchronous and near-real-time, the replica stays current enough for periodic analytics even when the data is updated frequently. This is the lowest-friction, native Cloud SQL approach and avoids building a separate ETL pipeline.

Answer

Use Cloud Composer to schedule ETL jobs that copy data to BigQuery every minute.

Answer

Migrate the database to Cloud Spanner and use strong reads for analytics.

Answer

Export the Cloud SQL data to Cloud Storage and then load into BigQuery for analysis.

Question 6

You are designing a system that ingests high-velocity event streams from IoT devices using Pub/Sub. Each event must be processed exactly once to update a Firestore database. However, due to the distributed nature, at-least-once delivery is guaranteed by Pub/Sub. Which design pattern should you use to achieve exactly-once processing?

Accepted Answer

Make the message processor idempotent by using a unique event ID as the Firestore document ID, and perform upsert operations.. Making the message processor idempotent by using a unique event ID as the Firestore document ID ensures that even if Pub/Sub delivers the same event multiple times (at-least-once semantics), the upsert operation will overwrite the document with the same data, resulting in exactly-once processing. This pattern leverages Firestore's document-level write semantics without requiring additional infrastructure for deduplication.

Answer

Use a Cloud Function with a retry policy to ensure delivery, and deduplicate using a Cloud Bigtable row key.

Answer

Use Cloud Dataflow with exactly-once processing mode and write to Firestore using a custom sink.

Answer

Use a Cloud Run service to pull messages and write to Firestore; rely on Firestore's built-in deduplication using document IDs.

Question 7

You are a developer at a company that runs a critical pricing engine on Compute Engine instances in a managed instance group (MIG) behind an internal TCP load balancer. The pricing engine is a stateful application that stores state in memory and also writes to a Cloud Bigtable instance for persistence. The application uses a custom port 8080. You need to migrate this application to Cloud Run for better scalability and reduced operational overhead. The application must maintain session affinity so that requests from the same client are routed to the same instance (since the in-memory state is not yet fully externalized). The application currently uses a health check on /healthz that returns 200 OK. You have containerized the application. When you deploy to Cloud Run, you notice that traffic is not sticky; every request might go to a different revision. You also need to ensure that Bigtable writes are performed asynchronously to avoid slowing down the pricing calculations. What should you do?

Accepted Answer

Use Cloud Run with an HTTP(S) External Load Balancer and enable session affinity on the backend service.. Cloud Run does not natively support session affinity, but you can achieve it by placing Cloud Run behind an external HTTP(S) load balancer and enabling session affinity on the backend service. This ensures that requests from the same client are routed to the same Cloud Run revision, maintaining session stickiness for the in-memory state. Option A is incorrect because Cloud Run does not support custom TCP health checks; it only supports HTTP health checks, and Cloud Run automatically handles health checks via the /healthz endpoint. Option B is incorrect because setting concurrency to 1 does not provide session affinity; it only limits the number of concurrent requests per container, but subsequent requests from the same client may still go to different containers without a load balancer with session affinity. Option D is incorrect because Cloud Run cannot be placed behind an internal TCP/UDP load balancer; it only supports HTTP(S) traffic and must use an external load balancer for session affinity.

Answer

Implement a custom health check on TCP port 8080 in Cloud Run to ensure only healthy instances receive traffic.

Answer

Increase the container concurrency setting to 1 to force each container to handle one request at a time.

Answer

Deploy the application on Cloud Run and configure an Internal TCP/UDP Load Balancer in front of it with session affinity.

Question 8

A financial services company runs a transaction processing microservice on Google Kubernetes Engine (GKE). The service uses Cloud Spanner as its database. After migrating from Cloud SQL to Spanner to improve scalability, the team notices that a small percentage of transactions fail with an 'ABORTED' error due to deadlock detection. The application currently performs no retries, and the failures cause customer-facing errors. The team also observes that under peak load, transaction latencies are around 500ms, which is acceptable but they want to ensure the system remains reliable. They need to implement a solution that minimizes failures while maintaining acceptable performance. Which course of action should they take?

Accepted Answer

Implement retry logic with exponential backoff and random jitter for aborted transactions.. In Cloud Spanner, 'ABORTED' errors due to deadlock detection are a normal part of the optimistic concurrency control mechanism. The correct solution is to implement retry logic with exponential backoff and random jitter, as recommended by Google's own documentation. This approach transparently handles transient deadlocks without requiring infrastructure changes or sacrificing consistency, and it maintains acceptable latency by spacing out retries.

Answer

Increase the number of Spanner nodes to reduce the probability of deadlocks.

Answer

Reduce the size of each transaction by splitting them into smaller ones.

Answer

Change the transaction isolation level to READ UNCOMMITTED to avoid deadlocks.

Question 9

A team is migrating a monolithic application to a microservices architecture on Google Kubernetes Engine (GKE). They want to ensure that failures in one microservice do not cascade to others. Which design pattern should they implement?

Accepted Answer

Implement a circuit breaker pattern that opens when failure thresholds are exceeded.. The circuit breaker pattern is the correct choice because it prevents cascading failures by monitoring inter-service calls and opening the circuit when failures exceed a threshold, allowing the system to fail fast and recover gracefully. In a GKE-based microservices architecture, this pattern is typically implemented using libraries like Resilience4j or Istio's circuit breaker, which can be configured to trip after a certain number of consecutive failures, thus protecting downstream services from being overwhelmed.

Answer

Implement retry logic with exponential backoff for all inter-service calls.

Answer

Use synchronous HTTP calls with timeouts to detect failures quickly.

Answer

Use bulkheads to separate thread pools for each service.

Question 10

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.

Answer

The service is down during those periods

Answer

The SSL certificate is expired

Answer

The endpoint has high latency