PCD Exam Simulation — 100 Questions

Question 1

A company is deploying a microservices architecture on GKE. They need to expose a set of related microservices under a single external IP address with path-based routing. Which Kubernetes resource should they use?

Accepted Answer

Ingress resource. An Ingress resource is the correct choice because it provides HTTP/HTTPS layer-7 routing to expose multiple services under a single external IP address, using path-based or host-based rules. This directly meets the requirement of exposing a set of related microservices with path-based routing on GKE, whereas a Service of type LoadBalancer would create a separate external IP per service.

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Service of type NodePort

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NetworkPolicy

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Service of type LoadBalancer

Question 2

You need to monitor the CPU usage of a Compute Engine instance and trigger an alert when it exceeds 80% for 5 minutes. Which Google Cloud service should you use?

Accepted Answer

Cloud Monitoring. Cloud Monitoring (formerly Stackdriver Monitoring) is the correct service because it provides metrics, dashboards, and alerting policies for Compute Engine instances. You can create a metric-based alert condition that triggers when the CPU utilization metric exceeds 80% for a duration of 5 minutes, using the MQL or policy builder.

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Cloud Debugger

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Cloud Logging

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Error Reporting

Question 3

Match each Firebase feature to its description.

Question 4

Drag and drop the steps to create a Cloud Run service in the correct order.

Question 5

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

Question 6

Drag and drop the steps to set up a Cloud SQL instance with a private IP in the correct order.

Question 7

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

Question 8

Drag and drop the steps to set up a Cloud Function triggered by a Cloud Storage event in the correct order.

Question 9

Drag and drop the steps to grant a service account access to a Cloud Storage bucket in the correct order.

Question 10

A team is developing a microservice that processes messages from Pub/Sub. The service is deployed on Cloud Run and uses Cloud Firestore to store processed data. During load testing, the service frequently fails with 'DeadlineExceeded' errors from Firestore. What is the most likely cause and best practice to fix it?

Accepted Answer

Increase the Cloud Run container instance request timeout. The 'DeadlineExceeded' error from Firestore indicates that the Firestore client-side timeout has been exceeded, not the Cloud Run request timeout. However, the most likely cause is that the Cloud Run container instance request timeout (default 5 minutes) is too short for the processing time required, causing the instance to be terminated before the Firestore operation completes. Increasing the Cloud Run request timeout allows the container to wait longer for Firestore responses, preventing premature termination.

Answer

Increase the Pub/Sub subscription acknowledgment deadline

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Enable CPU always allocation for the Cloud Run service

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Add retry logic with exponential backoff for Firestore operations

Question 11

A developer notices that a Cloud Function is timing out after 60 seconds. The function makes an external API call that occasionally takes longer than the timeout. What is the best practice to handle this?

Accepted Answer

Increase the timeout for the specific Cloud Function to a higher value. Option C is correct because Cloud Functions have a configurable timeout per function (up to 540 seconds for HTTP functions). Increasing the timeout for the specific function that makes the slow external API call directly addresses the timeout issue without affecting other functions or introducing unnecessary retry overhead. This is the most targeted and efficient solution.

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Implement retry logic without changing the timeout

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Increase the timeout for all Cloud Functions in the project

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Decrease the timeout to fail fast and implement retry logic

Question 12

An application running on GKE is experiencing high latency. The team uses Cloud Trace to identify the bottleneck. They notice that a particular service spends most of its time waiting on a database query. How can they optimize performance?

Accepted Answer

Optimize the database query and add appropriate indexes. Option C is correct because the bottleneck is identified as a database query causing high latency. Optimizing the query and adding appropriate indexes directly reduces the time spent waiting on the database, which is the root cause. Cloud Trace shows the service is waiting on the database, so improving database performance is the most effective solution.

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Decrease the number of pods to reduce load

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Use Cloud CDN to cache database results

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Increase the number of replicas for the service

Question 13

A company uses Cloud Logging to centralize logs from multiple projects. They want to create a log-based metric for tracking 404 errors. However, the metric shows zero data even though 404 errors are occurring. What is the most likely reason?

Accepted Answer

The logs are being excluded by an exclusion filter before the metric is applied. Option D is correct because log-based metrics are computed from logs that have passed through all exclusion filters. If an exclusion filter is configured to discard logs matching certain criteria (e.g., all HTTP 4xx responses), those logs are never evaluated by the metric filter, causing the metric to show zero data even though 404 errors are occurring. Exclusion filters are applied before log-based metric evaluation in Cloud Logging.

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The metric filter uses the wrong resource type

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The metric is sampled and not all logs are considered

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The logs are not being exported to Cloud Logging

Question 14

A company uses Cloud Run for a serverless application. They notice that cold starts are causing high latency for some requests. What is the best strategy to reduce cold starts?

Accepted Answer

Set a minimum number of instances to keep containers always warm. Option B is correct because setting a minimum number of instances ensures that Cloud Run keeps a baseline of container instances always warm and ready to serve requests. This eliminates cold starts for the first requests that hit those pre-warmed instances, directly addressing the latency issue. Cloud Run automatically scales to zero when idle, but a minimum instance setting overrides that behavior for the specified number of containers.

Answer

Increase the max instances setting

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Migrate the application to Cloud Functions

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Reduce the container concurrency setting

Question 15

A developer deploys the above app.yaml to App Engine standard environment. The deployment succeeds, but the application fails to connect to the database. What is the most likely reason?

Accepted Answer

The application is trying to connect to a local database on localhost, which is not available in the App Engine sandbox.. Option C is correct because in App Engine standard environment, applications run in a sandboxed environment that does not support connections to a local database on localhost. The application code is attempting to connect to a database at 127.0.0.1 or localhost, which is not available in the sandbox. Instead, the application must connect to a Cloud SQL instance using a Unix socket or a private IP, or use a fully managed database service like Firestore.

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The runtime 'python39' is not supported in App Engine standard environment.

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The $PORT environment variable is not set in App Engine standard environment.

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The entrypoint command is incorrect because gunicorn is not allowed.

Question 16

Your Cloud Run service is experiencing 5xx errors. You have enabled Cloud Logging and Cloud Error Reporting. How can you quickly identify the most common error type?

Accepted Answer

Open Cloud Error Reporting to see grouped error counts.. Cloud Error Reporting automatically groups similar errors (e.g., same stack trace or error message) and shows a count for each group, making it the fastest way to identify the most common 5xx error type without manual log inspection. It is purpose-built for this exact use case, aggregating errors from Cloud Logging and presenting them in a dashboard sorted by frequency.

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Use Cloud Trace to analyze the traces of failing requests.

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View the logs in Cloud Logging and manually count error messages.

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Create a Cloud Monitoring alert on 5xx response codes.

Question 17

A team is designing a disaster recovery plan for a critical application on Google Cloud. The application runs on Compute Engine with a regional persistent disk. They want to minimize data loss in case of a regional outage. Which strategy should they use?

Accepted Answer

Use persistent disk snapshot replication to another region. Persistent disk snapshot replication to another region is the correct strategy because snapshots are stored in Cloud Storage and can be replicated across regions. This allows you to restore the disk from a snapshot in a different region if the primary region experiences an outage, minimizing data loss by ensuring the backup is geographically separate. Regional persistent disks are synchronous within a region but do not provide cross-region replication, so snapshots are the recommended approach for cross-region disaster recovery.

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Create a snapshot schedule and store snapshots in the same region

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Use synchronous replication across regions

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Configure a managed instance group with autohealing

Question 18

An organization is migrating a critical application to Google Cloud and needs to ensure high availability and disaster recovery. The application runs on Compute Engine and uses a stateful database. Which three design choices should they make? (Choose three.)

Accepted Answer

Use managed instance groups distributed across multiple zones.. Option A is correct because managed instance groups (MIGs) distributed across multiple zones provide automatic failover and self-healing for the Compute Engine instances. If a zone fails, the MIG automatically recreates instances in healthy zones, ensuring high availability for the application layer. This aligns with Google Cloud's best practices for regional resilience.

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Use preemptible VMs to reduce costs for the database layer.

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Deploy all instances in a single zone and use snapshots for backup.

Question 19

A company is designing a global e-commerce platform on Google Cloud. The application requires low-latency access for users worldwide and must be highly available. Which load balancing solution should they use?

Accepted Answer

External HTTP(S) Load Balancer. The External HTTP(S) Load Balancer is the correct choice because it is a global, proxy-based Layer 7 load balancer that terminates HTTP/HTTPS traffic at Google's edge points of presence (PoPs) and routes requests to the nearest healthy backend. This provides low-latency access for users worldwide by leveraging Google's global network and anycast IPs, while also offering built-in high availability, SSL offloading, and content-based routing.

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External TCP/UDP Network Load Balancer

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Cloud CDN

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Internal TCP/UDP Load Balancer

Question 20

Match each Kubernetes resource to its function.