Google Google Cloud Architect Professional Exam Prep
Google Cloud Certified - Professional Cloud Architect (Page 10 )

Updated On: 15-Sep-2026

Your customer support tool logs all email and chat conversations to Cloud Bigtable for retention and analysis.
What is the recommended approach for sanitizing this data of personally identifiable information or payment card information before initial storage?

  1. Hash all data using SHA256
  2. Encrypt all data using elliptic curve cryptography
  3. De-identify the data with the Cloud Data Loss Prevention API
  4. Use regular expressions to find and redact phone numbers, email addresses, and credit card numbers

Answer(s): C

Explanation:

The correct answer is C. De-identify the data with the Cloud Data Loss Prevention API.
Here's why:
The primary goal is to sanitize sensitive information (PII, PCI) before it's stored in Bigtable. Options A, B, and D are insufficient or inappropriate for this task.

A: Hashing with SHA256: While hashing can anonymize, it's not suitable for this scenario because it's irreversible. Once hashed, the original data is lost, making future analysis of specific customer interactions impossible. Additionally, it wouldn't prevent identification if a user's unique identifier were hashed.
B: Encrypting with elliptic curve cryptography: Encryption secures data from unauthorized access, but it doesn’t mask the sensitive information itself. The data remains personally identifiable once decrypted. Also, encryption is usually performed for data in transit or at rest, not typically during the ingestion phase.
D: Regular expressions for redaction: While regular expressions can identify patterns like phone numbers and email addresses, they are brittle and prone to errors. They might miss variations or edge cases and require constant maintenance. This approach is also computationally expensive and inefficient for large-scale data ingestion.
C: Cloud Data Loss Prevention (DLP) API: The DLP API is purpose-built to identify and de-identify sensitive data. It uses sophisticated techniques beyond regular expressions, such as contextual analysis and machine learning, for better accuracy and flexibility. DLP can perform actions like masking, redacting, tokenizing, and pseudonymizing data. This is ideal for ensuring sensitive information is protected before reaching its final storage location in Bigtable while still allowing for meaningful data analysis. It also offers a consistent, managed service, reducing maintenance overhead.
Therefore, the Cloud DLP API provides a comprehensive, reliable, and scalable solution for sanitizing sensitive data before storing it in Cloud Bigtable.
Authoritative Links:
Google Cloud DLP API Documentation: https://cloud.google.com/dlp/docs Cloud DLP Concepts: https://cloud.google.com/dlp/docs/concepts-dlp



You are using Cloud Shell and need to install a custom utility for use in a few weeks.
Where can you store the file so it is in the default execution path and persists across sessions?

  1. ~/bin
  2. Cloud Storage
  3. /google/scripts
  4. /usr/local/bin

Answer(s): A

Explanation:

The correct location to store a custom utility for persistent use across Cloud Shell sessions and within the default execution path is ~/bin . This directory, located in the user's home directory, is specifically designed for user-specific executable files. Cloud Shell, being an ephemeral environment, requires a location within the persistent home directory to preserve files across sessions. Placing the utility in ~/bin ensures that it's accessible by simply typing its name in the terminal, as this directory is typically included in the user's PATH
environment variable. This variable lists directories that the shell searches when a command is entered without a full path. Unlike options like Cloud Storage or /google/scripts , ~/bin is tailored for this precise purpose
– storing user-specific commands. Storing in /usr/local/bin while technically a common location for executables would typically require root access and would not persist across Cloud Shell sessions. Thus, A, ~/bin is the most appropriate and efficient location.The use of a user's home directory for personal executables is a common practice in Linux-based systems, aligning with the user's ability to customize their working environment without affecting system-level files.
Authoritative Links:
Google Cloud Shell documentation: https://cloud.google.com/shell/docs/features (Specifically look for information on persistent storage and home directory behavior) Linux directory structure: https://www.pathname.com/fhs/ (For understanding the general purpose of directories like /usr/local/bin and ~/ )
Understanding the PATH variable: You can find numerous online resources explaining the PATH environment variable by searching for "linux path environment variable."



You want to create a private connection between your instances on Compute Engine and your on-premises data center. You require a connection of at least 20 Gbps. You want to follow Google-recommended practices. How should you set up the connection?

  1. Create a VPC and connect it to your on-premises data center using Dedicated Interconnect.
  2. Create a VPC and connect it to your on-premises data center using a single Cloud VPN.
  3. Create a Cloud Content Delivery Network (Cloud CDN) and connect it to your on-premises data center using Dedicated Interconnect.
  4. Create a Cloud Content Delivery Network (Cloud CDN) and connect it to your on-premises datacenter using a single Cloud VPN.

Answer(s): A

Explanation:

The correct answer is A: Create a VPC and connect it to your on-premises data center using Dedicated Interconnect. Here's why:
Dedicated Interconnect is Google Cloud's service offering private, high-bandwidth connections (up to 100 Gbps per connection) between your on-premises network and Google's network. This directly addresses the requirement of a connection of at least 20 Gbps and aligns with Google-recommended best practices for high-throughput connectivity. A Virtual Private Cloud (VPC) is a fundamental building block within Google Cloud, providing a logically isolated network environment where you can launch your Compute Engine instances. Therefore, establishing a connection between your on-premises network and a VPC using Dedicated Interconnect enables private and high-bandwidth communication between your resources in both locations.
Cloud VPN, while providing a secure connection, is designed for lower bandwidth use cases and typically doesn't meet the 20 Gbps requirement with a single connection. Furthermore, Cloud VPN utilizes the public internet, making it less ideal for private connections when dedicated bandwidth is paramount. Cloud CDN, on the other hand, is for content delivery and caching closer to users, not for private networking between your data center and Google Cloud. Thus, using Cloud CDN would not address the primary requirement of establishing a private connection. Therefore, combining a VPC with Dedicated Interconnect is the most appropriate solution for a high-bandwidth, private network connection between on-premises and Google Cloud, as it is purpose-built for this scenario. This option ensures the connection is private, scalable, and adheres to best practices.
Supporting Links:
Google Cloud Dedicated Interconnect: https://cloud.google.com/network-connectivity/docs/interconnect/ Google Cloud VPC: https://cloud.google.com/vpc/docs Google Cloud VPN: https://cloud.google.com/vpn/docs Google Cloud CDN: https://cloud.google.com/cdn/docs



You are analyzing and defining business processes to support your startup's trial usage of GCP, and you don't yet know what consumer demand for your product will be. Your manager requires you to minimize GCP service costs and adhere to Google best practices.
What should you do?

  1. Utilize free tier and sustained use discounts. Provision a staff position for service cost management.
  2. Utilize free tier and sustained use discounts. Provide training to the team about service cost management.
  3. Utilize free tier and committed use discounts. Provision a staff position for service cost management.
  4. Utilize free tier and committed use discounts. Provide training to the team about service cost management.

Answer(s): B

Explanation:

The correct answer is B because it best addresses the startup's need to minimize costs during an uncertain trial period while adhering to Google Cloud best practices. Free tier usage is crucial for reducing initial expenses and testing services at no cost. Sustained use discounts automatically apply to resources used consistently for a large portion of the month, which, while demand is uncertain, can be beneficial for any workloads that do stabilize during the trial. Importantly, instead of creating a potentially unnecessary staff position for cost management (as seen in options A and C), training the team (option B and D) empowers everyone to understand cost implications of their choices and encourages cost-aware development practices. This distributed responsibility approach aligns with a startup's agile environment and is more scalable than relying on a single staff member. Committed use discounts, while beneficial, are inappropriate for the trial phase where demand is undefined, and these discounts lock the company into resource commitments which go against the requirement to minimize costs during this time.
Therefore, option B provides the best blend of cost-optimization strategies with a scalable approach to cost management through team training.
Authoritative Links:
Google Cloud Free Tier: https://cloud.google.com/free Google Cloud Sustained Use Discounts: https://cloud.google.com/compute/docs/sustained-use-discounts Google Cloud Cost Management Best Practices: https://cloud.google.com/architecture/framework/cost-optimization



You are building a continuous deployment pipeline for a project stored in a Git source repository and want to ensure that code changes can be verified before deploying to production.
What should you do?

  1. Use Spinnaker to deploy builds to production using the red/black deployment strategy so that changes can easily be rolled back.
  2. Use Spinnaker to deploy builds to production and run tests on production deployments.
  3. Use Jenkins to build the staging branches and the master branch. Build and deploy changes to production for 10% of users before doing a complete rollout.
  4. Use Jenkins to monitor tags in the repository. Deploy staging tags to a staging environment for testing. After testing, tag the repository for production and deploy that to the production environment.

Answer(s): D

Explanation:

Option D is the most appropriate approach for verifying code changes before production deployment in a continuous deployment pipeline. It leverages a structured process using tags to differentiate between staging and production deployments. By monitoring tags in the Git repository using Jenkins, the pipeline ensures that only explicitly tagged commits are promoted. Deploying 'staging' tags to a staging environment allows for thorough testing in a pre-production setting, verifying code functionality and identifying potential issues. This step avoids impacting end-users in the production environment. Once the staged deployment is verified, a new tag for 'production' is applied, which triggers deployment to the live production environment. This gated approach ensures only thoroughly tested and approved changes reach production. Options A, B, and C are less suitable. Options A and B suggest direct deployment to production which can be risky. Option C introduces a canary deployment strategy, but lacks the pre-production testing step and is less focused on comprehensive validation prior to wider deployment. Option D’s approach focuses on controlled rollout using tags, with testing in a separate staging environment and a subsequent promotion to production environment. This ensures a reliable and safe continuous deployment.
Supporting Concepts:
Continuous Deployment: Automated release of code changes to production. Staging Environment: An environment that mirrors the production environment for pre-production testing. Git Tags: Labels that mark specific points in a repository's history for deployment control. Jenkins: A popular automation server used to orchestrate build and deployment pipelines. Deployment Strategies: Methods for releasing new software versions.
Authoritative Links:
Jenkins: https://www.jenkins.io/ Continuous Deployment: https://martinfowler.com/bliki/ContinuousDeployment.html Git Tagging: https://git-scm.com/book/en/v2/Git-Basics-Tagging



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