Google Google Cloud Architect Professional Exam Prep
Google Cloud Certified - Professional Cloud Architect (Page 14 )

Updated On: 31-Aug-2026

The development team has provided you with a Kubernetes Deployment file. You have no infrastructure yet and need to deploy the application.
What should you do?

  1. Use gcloud to create a Kubernetes cluster. Use Deployment Manager to create the deployment.
  2. Use gcloud to create a Kubernetes cluster. Use kubectl to create the deployment.
  3. Use kubectl to create a Kubernetes cluster. Use Deployment Manager to create the deployment.
  4. Use kubectl to create a Kubernetes cluster. Use kubectl to create the deployment.

Answer(s): B

Explanation:

The correct answer is B. Here's why:
First, before deploying any Kubernetes application, a Kubernetes cluster needs to be provisioned. Google Cloud Platform (GCP) offers Google Kubernetes Engine (GKE) as its managed Kubernetes service. The gcloud command-line tool is the primary way to interact with GCP resources, including GKE clusters. Therefore, option A and B correctly start by utilizing gcloud to create a GKE cluster. Options C and D are incorrect because kubectl is used to interact with an existing Kubernetes cluster, not to create one.
After a Kubernetes cluster is up and running, you can then deploy the application defined in the Deployment file. kubectl is the primary command-line interface for managing Kubernetes resources, including deployments. It interprets the YAML-based Deployment file and interacts with the Kubernetes API server to create the necessary pods and other resources in the cluster. Deployment Manager, on the other hand, is a Google Cloud service for defining and managing infrastructure deployment, not for deploying resources into Kubernetes. This makes options A and C incorrect.
Therefore, the correct workflow involves first using gcloud to create the necessary Kubernetes cluster, and then using kubectl to deploy your application using the provided Deployment file. This confirms option B as the most appropriate course of action.
Here are some authoritative links for further research:
Google Kubernetes Engine (GKE) Documentation: https://cloud.google.com/kubernetes-engine/docs gcloud Command-Line Tool Documentation: https://cloud.google.com/sdk/gcloud kubectl Command-Line Tool Documentation: https://kubernetes.io/docs/reference/kubectl/
Kubernetes Deployments: https://kubernetes.io/docs/concepts/workloads/controllers/deployment/ Deployment Manager Documentation: https://cloud.google.com/deployment-manager/docs



You need to evaluate your team readiness for a new GCP project. You must perform the evaluation and create a skills gap plan which incorporates the business goal of cost optimization. Your team has deployed two GCP projects successfully to date.
What should you do?

  1. Allocate budget for team training. Set a deadline for the new GCP project.
  2. Allocate budget for team training. Create a roadmap for your team to achieve Google Cloud certification based on job role.
  3. Allocate budget to hire skilled external consultants. Set a deadline for the new GCP project.
  4. Allocate budget to hire skilled external consultants. Create a roadmap for your team to achieve Google Cloud certification based on job role.

Answer(s): B

Explanation:

The best course of action is to invest in training your existing team by creating a roadmap for Google Cloud certifications (Option B). This approach directly addresses the need to evaluate team readiness and creates a skills gap plan. It leverages the team’s existing experience with two successful GCP projects, building upon their foundation rather than replacing them. Focusing on certifications ensures structured learning aligned with specific job roles, which addresses skill gaps efficiently. By building internal expertise, the team develops long-term competence, which is crucial for sustainable cost optimization. Hiring consultants (Options C and D) offers a quick fix but doesn't foster internal skill growth, potentially leading to increased long-term costs and dependency. Simply setting a deadline (Options A and C) without addressing the skill gap is unlikely to result in efficient and cost-optimized deployments. The strategic investment in training ensures the team can optimize costs through informed architecture decisions and effective resource management, directly aligning with the business goal.
Further Research:
Google Cloud Certifications: https://cloud.google.com/certification GCP Cost Optimization: https://cloud.google.com/blog/topics/cost-management Building Effective Cloud Teams: https://www.gartner.com/en/information-technology/insights/cloud-computing (This is a high-level link to Gartner; specific research reports would be more helpful if accessible).



You are designing an application for use only during business hours. For the minimum viable product release, you'd like to use a managed product that automatically `scales to zero` so you don't incur costs when there is no activity.
Which primary compute resource should you choose?

  1. Cloud Functions
  2. Compute Engine
  3. Google Kubernetes Engine
  4. AppEngine flexible environment

Answer(s): A

Explanation:

The correct answer is A. Cloud Functions .
Cloud Functions are serverless compute resources designed to execute code in response to events. A key benefit of serverless architecture, and Cloud Functions specifically, is the ability to scale to zero. This means when no events trigger the function, no compute resources are allocated, and thus no costs are incurred. This perfectly fits the requirement of an application used only during business hours.
Compute Engine (B) involves provisioning virtual machines which, even when idle, incur costs related to the virtual machine itself and attached storage.
While Compute Engine can be scaled down, achieving an actual "scale to zero" requires complex automation and is not its primary operational mode. Google Kubernetes Engine (C) is a managed container orchestration service and is also not designed to scale to zero automatically. It requires a cluster to be running, incurring associated costs. Similarly, App Engine flexible environment (D) requires application instances to always be running to serve traffic.
Cloud Functions are specifically designed for event-driven workloads, making them ideally suited for situations where resources should only be consumed when there’s activity. Therefore, Cloud Functions provide the most cost-effective approach, aligning with the requirement of a minimum viable product that automatically scales to zero. This is crucial for controlling expenditure when the application is not in use, typically during non-business hours, or when there's no traffic.
Further research:
Google Cloud Functions Documentation: https://cloud.google.com/functions/docs Serverless Computing: https://cloud.google.com/serverless Cloud Functions Pricing: https://cloud.google.com/functions/pricing



You are creating an App Engine application that uses Cloud Datastore as its persistence layer. You need to retrieve several root entities for which you have the identifiers. You want to minimize the overhead in operations performed by Cloud Datastore.
What should you do?

  1. Create the Key object for each Entity and run a batch get operation
  2. Create the Key object for each Entity and run multiple get operations, one operation for each entity
  3. Use the identifiers to create a query filter and run a batch query operation
  4. Use the identifiers to create a query filter and run multiple query operations, one operation for each entity

Answer(s): A

Explanation:

The most efficient way to retrieve multiple entities by their keys in Cloud Datastore is through batch get operations. Option A, "Create the Key object for each Entity and run a batch get operation," is the correct approach. Cloud Datastore's get operation is optimized for key lookups, offering direct retrieval based on the provided keys. Batch operations, specifically, combine multiple individual requests into a single API call, minimizing network overhead and latency compared to individual operations. This reduces the total time taken for the data retrieval process. Conversely, option B, using multiple individual get operations, incurs the cost of repeated API calls and increased latency. Options C and D, employing queries with filters based on identifiers, are unsuitable. Queries are designed for finding entities based on properties and are less efficient than get operations when direct key access is available. Datastore indexes would need to be used for query filtering, leading to increased complexity and slower performance for key retrieval. Batch queries, though feasible, are not optimized for direct key lookups, making the batch get more effective and resource-friendly in this scenario. Therefore, leveraging the key-based get functionality with batching delivers the most optimized and cost-effective retrieval of entities.
https://cloud.google.com/datastore/docs/concepts/entitieshttps://cloud.google.com/datastore/docs/concepts/read



You need to upload files from your on-premises environment to Cloud Storage. You want the files to be encrypted on Cloud Storage using customer-supplied encryption keys.
What should you do?

  1. Supply the encryption key in a .boto configuration file. Use gsutil to upload the files.
  2. Supply the encryption key using gcloud config. Use gsutil to upload the files to that bucket.
  3. Use gsutil to upload the files, and use the flag --encryption-key to supply the encryption key.
  4. Use gsutil to create a bucket, and use the flag --encryption-key to supply the encryption key. Use gsutil to upload the files to that bucket.

Answer(s): A

Explanation:

The correct answer is A. Supply the encryption key in a .boto configuration file. Use gsutil to upload the files.
Here's why:
Customer-supplied encryption keys (CSEK) for Cloud Storage require the key to be provided with every request that interacts with the encrypted data. gsutil , Google Cloud's command-line tool for Cloud Storage,
can manage these keys. Options B, C, and D either use incorrect methods for supplying the key or attempt to apply the key at the wrong time. Option B uses gcloud config , which is used to configure gcloud itself, not gsutil ’s encryption key settings. Options C and D try to use the --encryption-key flag directly during upload or bucket creation respectively, while the gsutil documentation specifies that using a boto config file is the recommended approach.
Option A, correctly uses a .boto configuration file. This is the recommended approach for repeatedly using the same customer-supplied encryption key. The .boto file (typically located in the user's home directory) allows you to set various gsutil parameters, including the CSEK, on a per-bucket or even per-prefix basis. gsutil then automatically uses this key for all subsequent operations on the specified bucket or path within the bucket. This ensures consistent encryption on Cloud Storage with a user-managed key. Supplying the key directly via command line arguments, as attempted in options C and D, can be cumbersome and less secure, especially if frequently used. The encryption process is managed transparently by gsutil , ensuring files are encrypted before being stored and decrypted upon download.
Authoritative Links:
Using customer-supplied encryption keys: https://cloud.google.com/storage/docs/encryption/customer-supplied-keys gsutil config: https://cloud.google.com/storage/docs/gsutil/commands/config Boto Configuration: https://cloud.google.com/storage/docs/boto-gsutil



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