PCA practice questions 281–290

1. Question 281 · 335 in bank

   For this question, refer to the EHR Healthcare case study. You are responsible for designing the Google Cloud network architecture for Google Kubernetes Engine. You want to follow Google best practices. Considering the EHR Healthcare business and technical requirements, what should you do to reduce the attack surface?

   • AUse a private cluster with a private endpoint with master authorized networks configured.
   • BUse a public cluster with firewall rules and Virtual Private Cloud (VPC) routes.
   • CUse a private cluster with a public endpoint with master authorized networks configured.
   • DUse a public cluster with master authorized networks enabled and firewall rules.

   Show answer

   Answer: A

   Explanation

   It should be A. Public endpoint access disabled is the most secure option as it prevents all internet access to the control plane. This is a good choice if you have configured your on-premises network to connect to Google Cloud using Cloud Interconnect (EHR has enabled this) or Cloud VPN. If you disable public endpoint access, then you must configure authorized networks for the private endpoint. If you don't do this, you can only connect to the private endpoint from cluster nodes or VMs in the same subnet as the cluster. Public endpoint access enabled, authorized networks enabled: This is a good choice if you need to administer the cluster from source networks that are not connected to your cluster's VPC network using Cloud Interconnect or Cloud VPN (but EHR is already using interconnect) So answer C is wrong. Reference- https://cloud.google.com/kubernetes-engine/docs/concepts/private-cluster-concept

   Source: Adapted from community discussion — jask

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 282 · 335 in bank

   Your company uses a custom-built application running on a Compute Engine virtual machine (VM). This application processes real-time sales data and writes it to a zonal Persistent Disk. A recent internal audit requires that you implement a backup and recovery plan to protect against zonal failures. Your company has a strict policy that all backup data must be retained for at least 90 days and stored in a separate project with limited access. You need to implement a fully automated backup solution that meets these requirements with minimal operational overhead. What should you do?

   • AWrite a script to create daily backups of the Persistent Disk. Copy the backups to a different zone and apply a label to each snapshot to indicate the deletion date.
   • BUse gcloud commands to create snapshots of the Persistent Disk. Store the snapshots in a regional Cloud Storage bucket and configure a lifecycle rule to delete objects older than 90 days.
   • CCreate a snapshot schedule to automatically create Persistent Disk snapshots and use a script to move and store them in a multi-regional Cloud Storage bucket.
   • DUse the Backup and Disaster Recovery (DR) service to create a backup plan. Configure the backup plan to take daily snapshots and store them in a backup vault with a 90-day retention policy.

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   Answer: D

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3. Question 283 · 335 in bank

   Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?

   • ATests should scale well beyond the prior approaches
   • BUnit tests are no longer required, only end-to-end tests
   • CTests should be applied after the release is in the production environment
   • DTests should include directly testing the Google Cloud Platform (GCP) infrastructure

   Show answer

   Answer: A

   Explanation

   Tests should include directly "testing the Google Cloud Platform (GCP) infrastructure". I don't what does this mean? Testing their resources running on GCP infrastructure or testing GCP services itself? Regardless, Option D suggests SLA requirements which is not the problem. The problem is scalability that can be resolved through stress testing. Also, GCP services are indirectly tested through it (Option D). Hence, I choose option A.

   Source: Adapted from community discussion — Smart

   References

   • Google Cloud documentation

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4. Question 284 · 335 in bank

   Select 2

   Your ecommerce web application includes many products with pictures and videos. You need to improve the application's page load speed and reduce the latency for customer requests. What should you do? (Choose two.)

   • ADeploy your application to Cloud Run and enable concurrency.
   • BUse Memorystore tor Redis.
   • CImplement CDN with the application's external HTTPS load balancer.
   • DChange the database from Bigtable to Spanner.
   • EDeploy your application to a GKE Autopilot cluster instead of a regular GKE cluster.

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   Answer: BC

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5. Question 285 · 335 in bank

   Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a through testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?

   • ACreate a scalable environment in GCP for simulating production load
   • BUse the existing infrastructure to test the GCP-based backend at scale
   • CBuild stress tests into each component of your application using resources internal to GCP to simulate load
   • DCreate a set of static environments in GCP to test different levels of load ג€" for example, high, medium, and low

   Show answer

   Answer: A

   Explanation

   A is correct because simulating production load in GCP can scale in an economical way. B is not correct because one of the pain points about the existing infrastructure was precisely that the environment did not scale well. C is not correct because it is a best practice to have a clear separation between test and production environments. Generating test load should not be done from a production environment. D is not correct because Mountkirk Games wants the testing environment to scale as needed. Defining several static environments for specific levels of load goes against this requirement.

   Source: Adapted from community discussion — shandy

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6. Question 286 · 335 in bank

   You need to build and deploy a containerized web application to Google Cloud. The application is very write-heavy and requires a relational database as its data store. The application needs to be highly available in multiple cloud regions. You want to minimize operational overhead while following Google-recommended practices. What should you do?

   • ADeploy the application to GKE clusters in multiple regions and expose the application endpoint with a regional load balancer. Use Cloud SQL as the database.
   • BDeploy the application to Cloud Run in multiple regions behind a global HTTPS load balancer. Use Spanner as the database.
   • CDeploy the application to a GKE standard cluster behind a global HTTPS load balancer. Use Firestore as the database with multi-region locations enabled.
   • DDeploy the application to a GKE standard cluster with a global ingress to expose the application endpoint. Use Bigtable as the database.

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   Answer: B

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7. Question 287 · 335 in bank

   Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small services that they want to be able to update and roll back quickly. Mountkirk Games has the following requirements: ✑ Services are deployed redundantly across multiple regions in the US and Europe ✑ Only frontend services are exposed on the public internet ✑ They can provide a single frontend IP for their fleet of services ✑ Deployment artifacts are immutable Which set of products should they use?

   • AGoogle Cloud Storage, Google Cloud Dataflow, Google Compute Engine
   • BGoogle Cloud Storage, Google App Engine, Google Network Load Balancer
   • CGoogle Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer
   • DGoogle Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

   Show answer

   Answer: C

   Explanation

   If the Question is erroneously formulated, and they mean Google Container Registry and Google Kubernetes Engine, then C is the right answer

   Source: Adapted from community discussion — kolcsarzs

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8. Question 288 · 335 in bank

   Your machine learning (ML) engineers use self-hosted Jupyter notebooks for tasks such as data preparation, model training, and fine-tuning. The operations team then deploys these models in various environments. You want to provide maximum flexibility for ML engineers, promote collaboration with a common toolset, and leverage Google Cloud's scalability, while following Google-recommended practices. What should you do?

   • AUse AutoML for machine learning and Cloud Deploy for model deployment.
   • BUse Colab Enterprise for machine learning and DevOps for model deployment.
   • CUse Vertex AI for machine learning and machine learning operations (MLOps) for model deployment.
   • DUse TensorFlow for machine learning and Cloud Deploy for model deployment.

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   Answer: C

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9. Question 289 · 335 in bank

   Mountkirk Games' gaming servers are not automatically scaling properly. Last month, they rolled out a new feature, which suddenly became very popular. A record number of users are trying to use the service, but many of them are getting 503 errors and very slow response times. What should they investigate first?

   • AVerify that the database is online
   • BVerify that the project quota hasn't been exceeded
   • CVerify that the new feature code did not introduce any performance bugs
   • DVerify that the load-testing team is not running their tool against production

   Show answer

   Answer: B

   Explanation

   503 has two meaning, Service not available due to server load (slow response) or quota depleted. Not sure B or C. As per question, slow response, it can be C or Looking this link, it is B. https://cloud.google.com/appengine/quotas When a resource is depleted When an application consumes all of an allocated resource, the resource becomes unavailable until the quota is replenished. This may mean that your application will not work until the quota is replenished. For resources that are required to initiate a request, when the resource is depleted, App Engine by default returns an HTTP 403 or 503 error code for the request instead of calling a request handler. This behavior applies to the Instance hours resource.

   Source: Adapted from community discussion — [Removed]

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google developer documentation
   • Google Cloud documentation

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10. Question 290 · 335 in bank

    Your employer is a financial services company that recently acquired a popular fintech startup. The startup's core application is a monolithic Python application running on a managed instance group of Compute Engine virtual machines with a single, large PostgreSQL database. Your development team struggles with slow deployment cycles, and the monolithic design of the startup's core application makes it difficult to integrate new. ML-powered fraud detection models. You need a long-term strategy that improves developer agility and positions the company to leverage Google Cloud's advanced data and AI capabilities for future innovations. What should you do?

    • ADeploy the ML fraud detection model to a Vertex AI endpoint. Create a REST API for the model and modify the monolithic Python application to call this endpoint for real-time fraud analysis.
    • BContainerize the application, deploy it to Google Kubernetes Engine (GKE), and migrate the PostgreSQL database to Cloud SQL for PostgreSQL.
    • CPropose a phased, event-driven migration to a microservices architecture. Use Pub/Sub for asynchronous communication and deploy the fraud models on Vertex AI endpoints.
    • DMigrate the PostgreSQL database to Cloud SQL for PostgreSQL. Replicate the data into BigQuery using Datastream, and then train and deploy the fraud detection models directly within BigQuery using BigQuery ML.

    Show answer

    Answer: C

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PCA practice, question 281. 335 questions available.