PCD Practice Exam 61 — 20 Questions

Question 1

A developer is writing a Cloud Function that throws an exception when processing invalid input. They want to ensure the function returns an appropriate HTTP error response. What should they do?

Accepted Answer

Return a response with a status code and error message from the function. In Cloud Functions (and serverless platforms like Google Cloud Functions), the function code itself is responsible for constructing and returning an HTTP response, including setting the appropriate status code and error message. Throwing an exception alone does not automatically map to an HTTP error response; the platform will typically return a generic 500 error, which is not appropriate for invalid input (e.g., 400 Bad Request). By explicitly returning a response object with a status code (e.g., 400) and a descriptive error message, the developer ensures the client receives a meaningful and correct HTTP error response.

Answer

Log the error and return a success response

Answer

Use a global error handler in the function framework

Answer

Throw an exception and let the platform handle it automatically

Question 2

A financial services company requires a relational database that can handle hybrid transactional/analytical processing (HTAP) workloads with sub-millisecond transactional latency and real-time analytics without ETL. Which Google Cloud database is BEST suited?

Accepted Answer

AlloyDB for PostgreSQL. AlloyDB is PostgreSQL-compatible and includes a columnar engine for in-database ML inference and HTAP workloads. It is optimized for OLTP and real-time analytics without ETL.

Answer

Cloud SQL for PostgreSQL

Answer

Cloud Spanner

Answer

BigQuery

Question 3

A DevOps team is deploying a critical application on GKE. To ensure application performance monitoring and reliability, which three actions should they take? (Choose three.)

Accepted Answer

Enable Cloud Monitoring to collect container metrics. A is correct because Cloud Monitoring (formerly Stackdriver) integrates natively with GKE to collect container metrics such as CPU, memory, disk, and network usage, as well as custom metrics via the Monitoring agent. This provides the visibility needed for performance monitoring and reliability without additional configuration overhead.

Answer

Enable Cloud Debugger in production to debug code

Answer

Use Cloud Profiler to continuously profile CPU usage

Question 4

A company is planning to migrate from Snowflake to BigQuery. They have existing complex SQL transformations using Snowflake-specific functions like LISTAGG and PIVOT. What is the best approach to handle these differences?

Accepted Answer

Rewrite the SQL queries to use BigQuery equivalent functions and syntax.. Snowflake and BigQuery have different SQL dialects. The best approach is to use a migration assessment tool to identify incompatible syntax, rewrite queries to BigQuery-compatible syntax (e.g., use STRING_AGG instead of LISTAGG), and test thoroughly. BigQuery does not support PIVOT natively; use conditional aggregation.

Answer

Use BigQuery Data Transfer Service to automatically convert Snowflake functions.

Answer

Keep Snowflake for complex queries and use BigQuery only for simple analytics.

Answer

Use Cloud Spanner as an intermediary to translate queries.

Question 5

A developer is using Cloud Functions and wants to ensure that their testing environment mirrors production as closely as possible. Which approach should they take?

Accepted Answer

Use the Functions Framework with a simulated production event. The Functions Framework is a local emulator that allows developers to run Cloud Functions locally with the same invocation context and event triggers as in production. By using the Functions Framework with a simulated production event, you can test your function's behavior, including event data parsing and response handling, without deploying to any cloud environment, ensuring the testing environment mirrors production as closely as possible.

Answer

Use Cloud Build to run tests before deployment

Answer

Run all tests in Cloud Shell

Answer

Deploy to a staging Cloud Function and run tests against it

Question 6

A DevOps engineer is automating deployments to Compute Engine using a CI/CD pipeline. They want to minimize downtime and ensure that if a new VM fails health checks, the old VM continues serving. Which deployment strategy should they implement?

Accepted Answer

Blue/green deployment with health checks and a managed instance group. Blue/green deployment with health checks and a managed instance group is correct because it allows the new version (green) to be fully deployed and validated against health checks before any traffic is switched from the old version (blue). If the new VM fails health checks, the managed instance group automatically keeps the old version serving, ensuring zero downtime and immediate rollback without manual intervention.

Answer

Redeploy the old version manually if the new version fails

Answer

Rolling update with a readiness probe

Answer

Canary deployment with a small percentage of traffic

Question 7

Refer to the exhibit. A team has created this alerting policy for a Cloud Run service. However, the alert never fires even though the error rate sometimes exceeds 1%. What is the most likely issue?

Accepted Answer

The filter is using response_code_class > 500, missing 500 errors.. The filter `response_code_class > 500` uses a strict greater-than operator, which excludes HTTP 500 errors (the class value is exactly 500). In Cloud Monitoring, `response_code_class` for a 500 error is 500, so the condition `> 500` only matches classes like 501, 502, etc., missing the most common server errors. To capture all 5xx errors, the filter should be `response_code_class >= 500` or `response_code_class = 500`.

Answer

The threshold is set via conditionMonitoringQuery but thresholdValue is null, causing conflict.

Answer

The group_by [] aggregates across all revisions, but errors might be per revision.

Answer

The duration is 0s, so the alert should fire immediately; it's not causing the issue.

Question 8

Which TWO statements about Cloud Run for Anthos are correct? (Choose 2)

Accepted Answer

Cloud Run for Anthos allows users to autoscale their containerized applications without worrying about underlying GKE nodes.. Cloud Run for Anthos leverages the Knative Serving autoscaler to automatically scale container instances up or down based on incoming request traffic, including scaling to zero when idle. This autoscaling operates at the pod level within the GKE cluster, abstracting away the underlying node management from the user.

Answer

Cloud Run for Anthos is a multi-region service by default.

Answer

Cloud Run for Anthos supports only HTTP requests, not gRPC.

Answer

Cloud Run for Anthos requires you to bring your own load balancer.

Question 9

Which Google Cloud database service is serverless, supports mobile and web apps with offline data synchronization, and provides document-level security rules?

Accepted Answer

Firestore. Firestore is a serverless, NoSQL document database that automatically scales, supports mobile and web clients with offline data persistence and synchronization via its SDKs, and enforces document-level security rules through Firebase Security Rules. This combination of serverless scaling, offline sync, and granular security makes it the correct choice for the described requirements.

Answer

Memorystore

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 10

A mobile app startup needs a serverless, scalable NoSQL database that supports offline sync and real-time updates. Which database should they choose?

Accepted Answer

Firestore in Native mode. Firestore (Native mode) provides serverless NoSQL document storage, offline sync for mobile clients, and real-time listeners for updates. It is the ideal choice for mobile and web apps.

Answer

Bigtable

Answer

Cloud SQL

Answer

Memorystore

Question 11

A company runs a microservices application on Google Kubernetes Engine (GKE). Users report intermittent slow responses. Developers suspect a specific service is causing latency. Which Google Cloud tool should they use to trace requests across services and identify the root cause?

Accepted Answer

Cloud Trace. Cloud Trace is the correct tool because it provides distributed tracing capabilities that capture end-to-end latency data as requests propagate through microservices. By analyzing trace spans and their timing, developers can pinpoint which specific service is introducing delay, directly addressing the intermittent slow responses described.

Answer

Cloud Debugger

Answer

Cloud Monitoring

Answer

Cloud Logging

Question 12

A developer is deploying a microservice on GKE that needs to be accessible only from within the same VPC network. The microservice must have a stable, internal IP address that does not change when pods are updated. Which options should be used?

Accepted Answer

Use a Service of type LoadBalancer with the annotation 'cloud.google.com/load-balancer-type: Internal'.. Deploying a Service of type LoadBalancer with the annotation 'cloud.google.com/load-balancer-type: Internal' creates an internal TCP/UDP load balancer within the VPC network. This provides a stable, internal IP address that persists across pod updates, as the load balancer's IP is independent of the underlying pods and is managed by Google Cloud's networking layer.

Answer

Use a Service of type NodePort with a static reservation.

Answer

Use a Service of type ClusterIP with a static IP.

Answer

Use a Service of type ExternalName pointing to the pods' internal IPs.

Question 13

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?

Accepted Answer

Write logs to stdout and stderr; Cloud Run automatically sends them to Cloud Logging.. Cloud Run is a serverless compute platform that automatically integrates with Cloud Logging. The best practice is to write logs to stdout and stderr because Cloud Run's runtime captures these streams and forwards them to Cloud Logging without any additional agents or sidecars. This approach aligns with the 12-factor app methodology and ensures logs are available for monitoring and troubleshooting.

Answer

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

Answer

Use a sidecar container to ship logs to Stackdriver.

Answer

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

Question 14

A developer is building a containerized application on Cloud Run. They want to test the application locally before deploying. Which two tools should they use? (Choose 2)

Accepted Answer

Docker Desktop. Docker Desktop allows running the container locally. Cloud Code (for VS Code or IntelliJ) provides integrated debugging, local emulation, and one-click deployment to Cloud Run. Cloud Build is for CI/CD, not local testing. Functions Framework is for Cloud Functions, not Cloud Run. Cloud Run for Anthos is for hybrid environments.

Answer

Functions Framework

Answer

Cloud Build

Answer

Cloud Run for Anthos

Question 15

You need to perform a one-time historical data migration from an on-premises MySQL database to Cloud SQL for MySQL. The database is 500 GB, and you can afford several hours of downtime. Which migration method is most straightforward?

Accepted Answer

Use mysqldump to create a SQL dump, then import into Cloud SQL.. Using mysqldump to export the database and then importing it via mysql client is the simplest approach for a one-time migration where downtime is acceptable. DMS is more suitable for continuous migrations with minimal downtime.

Answer

Use Database Migration Service with continuous migration.

Answer

Create a Cloud SQL clone of the on-premises database using mysqldump.

Answer

Use Dataflow to read from MySQL and write to Cloud SQL.

Question 16

You are designing a CI/CD pipeline for a microservices application deployed on GKE. Your team requires that each service have independent release cycles and canary deployments. Which combination of Google Cloud services should you use?

Accepted Answer

Cloud Source Repositories, Cloud Build, and Cloud Deploy with GKE target. Cloud Deploy provides native support for canary deployments and progressive delivery strategies to GKE targets, enabling independent release cycles per microservice. Cloud Source Repositories hosts the code, Cloud Build compiles and tests it, and Cloud Deploy manages the rollout with Skaffold-based pipelines, allowing fine-grained traffic splitting and automated promotion or rollback.

Answer

Cloud Source Repositories, Cloud Build, and App Engine

Answer

Cloud Source Repositories, Cloud Build, and Cloud Run

Answer

Cloud Source Repositories, Cloud Build, and Spinnaker

Question 17

Which two design patterns help decouple microservices?

Accepted Answer

Event-driven architecture. Event-driven architecture (B) decouples microservices by allowing them to communicate asynchronously through events, eliminating direct dependencies between services. This pattern uses a message broker (e.g., Kafka, RabbitMQ) to publish and consume events, enabling services to evolve independently without blocking each other.

Answer

Service mesh

Answer

Database per service

Answer

Circuit breaker

Question 18

A company runs a stateful application on Compute Engine with local SSDs. They want high durability. Which approach should they use?

Accepted Answer

Use persistent disks instead of local SSDs for automatic replication. Local SSDs are ephemeral and data is lost when the VM is stopped or terminated. Persistent disks, by contrast, automatically replicate data within the same zone (or across zones if using regional persistent disks), providing high durability. Option D correctly identifies that switching to persistent disks is the appropriate approach for durability, as local SSDs lack built-in redundancy.

Answer

Replicate data to another zone using synchronous replication

Answer

Use a RAID 1 array across multiple local SSDs

Answer

Take regular snapshots of local SSDs

Question 19

A company is designing a cross-database solution with eventual consistency. They need to update data in Cloud SQL (orders) and Bigtable (inventory) as part of a single business transaction. Which two patterns can help maintain eventual consistency? (Choose two.)

Accepted Answer

Implement a saga pattern with compensating transactions in case of failure.. The saga pattern breaks a distributed transaction into a series of local transactions, each with a compensating action that can undo its effects if a subsequent step fails. This pattern is ideal for maintaining eventual consistency across Cloud SQL and Bigtable without requiring a global coordinator or locking resources. Option D is correct because idempotent receivers ensure that duplicate events (e.g., from retries or network failures) do not cause unintended side effects, and retry logic allows the system to recover from transient failures, both of which are essential for achieving eventual consistency in a cross-database solution.

Answer

Use two-phase commit (2PC) across both databases.

Answer

Design for strong consistency by using global transactions.

Answer

Write directly to both databases in a single synchronous call.

Question 20

Which TWO are correct ways to reduce logging costs in Google Cloud? (Choose two.)

Accepted Answer

Set log bucket retention to a shorter period. Reducing the retention period for log buckets directly decreases the amount of log data stored, which lowers storage costs in Cloud Logging. Logs are billed based on volume ingested and stored; shorter retention means older logs are deleted sooner, reducing the total storage footprint and associated charges.

Answer

Disable all audit logs to reduce volume

Answer

Export all logs to BigQuery for analysis

Answer

Increase the retention period from 30 days to 365 days