PCD Practice Test 8 — 10 Questions

Question 1

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.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable retry policy on the Pub/Sub subscription to automatically retry failed messages.. 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 2

A company deploys a microservices application on Google Kubernetes Engine (GKE). The operations team needs to monitor API latency between services. Which Google Cloud service should they use to trace requests across services?

Accepted Answer

Cloud Trace. Cloud Trace is the correct choice because it is a distributed tracing system designed to capture latency data as requests propagate through microservices. It provides end-to-end visibility by collecting trace spans from each service, allowing the operations team to identify bottlenecks and measure API latency between services in a GKE environment.

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Error Reporting

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Cloud Logging

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Cloud Monitoring

Question 3

A development team is implementing a CI/CD pipeline using Cloud Build. They need to ensure that sensitive data, such as API keys, are never exposed in build logs. What is the best practice?

Accepted Answer

Use Cloud Build's encrypted variables or Secret Manager to pass secrets at build time.. Cloud Build's encrypted variables and Secret Manager are designed to securely inject sensitive data like API keys at build time without exposing them in logs. Encrypted variables are stored in Cloud KMS and decrypted only during the build, while Secret Manager provides versioned secrets with fine-grained access control. This ensures secrets never appear in plaintext in the build configuration or output logs.

Answer

Store the API keys in a separate file in the source repository and reference it in the build.

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Use custom substitutions with default values and rely on Cloud Build's encryption.

Answer

Store the API keys as plain text in cloudbuild.yaml and restrict access to the file.

Question 4

A multinational corporation runs a web application on Google Kubernetes Engine (GKE) with multiple microservices. They use Cloud Service Mesh (Anthos) for observability and security. The application uses gRPC for inter-service communication. Recently, they have observed increased latency and occasional timeouts between services in different regional clusters connected via Cloud VPN. The team wants to diagnose the issue and improve reliability. They suspect network round-trip time (RTT) is causing the latency, but they are not sure if the problem is at the application or network layer. Which tool should they use to pinpoint the exact cause?

Accepted Answer

Use Cloud Trace to analyze distributed traces and identify bottlenecks in request paths.. Cloud Trace is the correct tool because it provides end-to-end distributed tracing, which can capture the exact latency contribution of each gRPC call across microservices and regional clusters. By analyzing trace spans, the team can determine whether the increased latency is due to network round-trip time (RTT) between clusters or due to application-level processing delays within a service.

Answer

Use Cloud Monitoring to view gRPC latency distributions and break down by service and method.

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Use VPC Flow Logs to examine network throughput and packet loss.

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Use Cloud Logging to search for error logs in the application containers.

Question 5

A company uses Cloud Spanner for a globally distributed application. They need to design their table schema for maximum scalability and performance. Which two design considerations are critical? (Choose two.)

Accepted Answer

Use interleaved tables to colocate related data.. Option A is correct because interleaved tables physically colocate child rows with their parent row, so related data is stored on the same node and joins between parent and child can be executed locally without network shuffling, which improves performance and scalability for hierarchical data. Option E is correct because Cloud Spanner shards data by primary key ranges, so choosing keys that spread writes evenly across nodes (for example, using hashed or reversed keys) avoids hotspots and lets the system scale horizontally. Option B is not appropriate because Spanner is optimized for structured relational data and large binary blobs should be stored in Cloud Storage with references in Spanner. Option C is wrong because indexing every column adds write amplification and storage overhead without query benefit. Option D is wrong because monotonically increasing primary keys (such as timestamps or sequential IDs) concentrate writes on the last key range, creating a hotspot that limits scalability.

Answer

Store large binary blobs directly in Spanner.

Answer

Define secondary indexes on every column.

Answer

Use monotonically increasing primary keys.

Question 6

A company is migrating a 5 TB SQL Server database to Cloud SQL for SQL Server using Database Migration Service (DMS). After the full dump phase, the continuous replication (CDC) phase starts. The team notices that the source database transaction log is growing rapidly and the CDC phase is failing intermittently with 'log space' errors. What should they do?

Accepted Answer

Increase the size of the source transaction log and ensure it is not limited. The intermittent 'log space' errors during CDC indicate that the source transaction log is running out of space to record changes for replication. Database Migration Service (DMS) requires sufficient log space to capture ongoing changes; increasing the log size and removing any size limits ensures the log can grow to accommodate the replication lag without failing.

Answer

Reduce the retention period for the source transaction log backups

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Shrink the source transaction log to free up space

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Switch the source database to simple recovery model to reduce logging

Question 7

An e-commerce company relies on a Compute Engine backend serving content to global users. They notice high latency for users outside the primary region. Which service should they add to reduce latency by caching content at edge locations?

Accepted Answer

Cloud CDN. Cloud CDN (Content Delivery Network) uses Google's global edge cache locations to serve cached content closer to users, reducing latency for requests that would otherwise travel to the origin Compute Engine backend in a single region. By caching static or dynamic content at edge nodes, Cloud CDN minimizes round-trip time and offloads traffic from the backend instance.

Answer

Cloud Armor

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Cloud NAT

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Cloud Endpoints

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Cloud Load Balancing

Question 8

A startup is migrating an on-premises PostgreSQL 14 database to Cloud SQL. The database is 500 GB and handles OLTP workloads. They want to minimize downtime during migration. Which approach should they use?

Accepted Answer

Use Database Migration Service (DMS) with continuous replication. Database Migration Service (DMS) supports continuous replication from source PostgreSQL to Cloud SQL, allowing near-zero downtime. Export/import requires downtime. Replica promotion is not a migration method.

Answer

Export the database using pg_dump and import using psql

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Create a Cloud SQL read replica from the on-premises database

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Use gcloud sql import to directly import from a dump file

Question 9

Match each Cloud Logging and Monitoring concept to its definition.

Accepted Answer

Cloud Logging: Collects and stores log data from applications and services.. Correct matches: Cloud Logging handles log data, Cloud Monitoring provides performance insights, Log-based Metrics turn logs into metrics, Error Reporting aggregates errors. Common confusions include swapping Cloud Logging and Cloud Monitoring definitions.

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Cloud Monitoring: Collects and stores log data from applications and services.

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Error Reporting: Provides visibility into the performance, uptime, and health of cloud applications.

Question 10

A data engineering team needs to replicate data from a PostgreSQL database to BigQuery in near real-time for analytics. Which Google Cloud service is most suitable for this task with minimal setup?

Accepted Answer

Datastream. Datastream is a serverless CDC replication service specifically designed for replicating from sources like PostgreSQL, MySQL, and Oracle to BigQuery, Cloud Storage, or Pub/Sub. It handles schema mapping and initial backfill.

Answer

Cloud SQL for PostgreSQL

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Cloud Data Fusion

Answer

Cloud Composer