Google Google Cloud Architect Professional Exam Prep
Google Cloud Certified - Professional Cloud Architect (Page 2 )

Updated On: 15-Sep-2026

Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs.
What should they do?

  1. Configure a new load balancer for the new version of the API
  2. Reconfigure old clients to use a new endpoint for the new API
  3. Have the old API forward traffic to the new API based on the path
  4. Use separate backend pools for each API path behind the load balancer

Answer(s): D

Explanation:

The correct answer is D. Use separate backend pools for each API path behind the load balancer. This approach leverages the capabilities of a load balancer to intelligently route traffic based on the URL path.
Here's a detailed justification:
1. Maintaining Existing Infrastructure: The company aims to keep the same SSL and DNS records, indicating a desire to minimize disruption to existing users. Creating separate load balancers (Option A) would necessitate changes to DNS and SSL, violating this requirement. 2. Granular Control: By using separate backend pools, each pool is associated with either the old or the new API. This allows for independent management and scaling of each version. 3. Path-Based Routing: Modern load balancers can be configured to examine the path component of a URL (e.g., /v1/users for the old API, /v2/users for the new API). They then route requests to the corresponding backend pool. 4. No Client Modification: Option B, requiring old clients to use a new endpoint, is undesirable as it forces all clients to upgrade, rather than a phased rollout as indicated in the prompt. 5. Avoids Complex Forwarding: Option C, having the old API forward to the new API based on the path, is inefficient and creates unnecessary dependencies. The old API shouldn't need to be aware of the new API's logic. 6. Scalability and Isolation: Each backend pool can be independently scaled, meaning increased traffic to the new API will not impact the performance of the old API. This ensures better resource utilization and prevents cascading failures.
Using path-based routing with separate backend pools is a common best practice in microservices and API management. It ensures seamless transitions, maintains existing client experience, and permits for independent management and scaling, all using a single, shared load balancer.
Authoritative Links:
1. Google Cloud Load Balancing: https://cloud.google.com/load-balancing 2. Backend services: https://cloud.google.com/load-balancing/docs/backend-service 3. URL maps (path based routing): https://cloud.google.com/load-balancing/docs/url-map



Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?

  1. Load data into Google BigQuery
  2. Insert data into Google Cloud SQL
  3. Put flat files into Google Cloud Storage
  4. Stream data into Google Cloud Datastore

Answer(s): A

Explanation:

The optimal solution for storing a multi-petabyte dataset, requiring 24/7 availability and accessible via SQL by business analysts, is Google BigQuery (Option A). BigQuery is a fully-managed, serverless data warehouse designed for large-scale analytics. It inherently handles petabyte-scale datasets, ensuring high availability and scalability. Unlike Cloud SQL (Option B), which is a transactional database not optimized for large analytical workloads, BigQuery is built for analysis.
While Cloud Storage (Option C) provides scalable object storage, it requires additional tools for querying the data with SQL. Google Cloud Datastore (Option D) is a NoSQL database not suitable for SQL-based analysis. BigQuery offers a standard SQL interface, enabling analysts familiar with SQL to readily query and analyze the data. Moreover, BigQuery's serverless nature removes operational overhead, allowing businesses to focus solely on data analysis. It optimizes query performance through various techniques like columnar storage and query optimization. Its cost-effectiveness makes it suitable for large datasets. Using other options would necessitate additional efforts in setting up infrastructure or translating SQL to other querying mechanisms. BigQuery, being designed for analytics with SQL support, perfectly aligns with the stated requirements for accessibility and analysis.
Authoritative Links:
Google BigQuery Documentation: https://cloud.google.com/bigquery/docs Google Cloud SQL Documentation: https://cloud.google.com/sql/docs Google Cloud Storage Documentation: https://cloud.google.com/storage/docs Google Cloud Datastore Documentation: https://cloud.google.com/datastore/docs



The operations manager asks you for a list of recommended practices that she should consider when migrating a J2EE application to the cloud.
Which three practices should you recommend? (Choose three.)

  1. Port the application code to run on Google App Engine
  2. Integrate Cloud Dataflow into the application to capture real-time metrics
  3. Instrument the application with a monitoring tool like Stackdriver Debugger
  4. Select an automation framework to reliably provision the cloud infrastructure
  5. Deploy a continuous integration tool with automated testing in a staging environment
  6. Migrate from MySQL to a managed NoSQL database like Google Cloud Datastore or Bigtable

Answer(s): C,D,E

Explanation:

The correct answer, CDE, aligns with best practices for migrating a J2EE application to the cloud, focusing on observability, automation, and a solid deployment pipeline. Option C, instrumenting with Stackdriver
Debugger (now Cloud Debugger), is crucial for gaining insights into application behavior and identifying performance bottlenecks post-migration, aligning with cloud observability principles. [ https://cloud.google.com/debugger/docs] . Option D, selecting an automation framework (like Terraform or Deployment Manager) for provisioning infrastructure, ensures consistent, repeatable deployments and reduces manual configuration errors, a hallmark of Infrastructure as Code (IaC). [ https://cloud.google.com/solutions/infrastructure-as-code] . Finally, option E, deploying a CI/CD tool (like Cloud Build) with automated testing, is essential for maintaining code quality and enabling rapid releases in a cloud environment. This practice embraces DevOps methodologies. [ https://cloud.google.com/build] . Options A, B, and F are not the most appropriate recommended practices for initial migration.
While porting to App Engine (A) is a valid destination, it's a significant architectural change that isn't a prerequisite for cloud migration generally. Integrating Cloud Dataflow (B) is specific to batch or stream data processing needs and not a general migration practice. Migrating to NoSQL (F) is also a large change, not always necessary for all J2EE migrations. Therefore, the best initial focus is on monitoring, infrastructure management automation, and deployment automation, as suggested by CDE. These choices ensure stability and maintainability in the cloud.



A news feed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed.
What is the most likely cause of this problem?

  1. The session variable is local to just a single instance
  2. The session variable is being overwritten in Cloud Datastore
  3. The URL of the API needs to be modified to prevent caching
  4. The HTTP Expires header needs to be set to -1 stop caching

Answer(s): A

Explanation:

It's A. AppEngine spins up new containers automatically according to the load. During peak traffic, HTTP requests originated by the same user could be served by different containers. Given that the variable `sessions` is recreated for each container, it might store different data.
The problem here is that this Flask app is stateful. The `sessions` variable is the state of this app. And stateful variables in AppEngine / Cloud Run / Cloud Functions are problematic.
A solution would be to store the session in some database (e.g. Firestore, Memorystore) and retrieve it from there. This way the app would fetch the session from a single place and would be stateless.



An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data. You want to help them find a solution that meets their needs.
What should you do?

  1. Direct them to download and install the Google StackDriver logging agent
  2. Send them a list of online resources about logging best practices
  3. Help them define their requirements and assess viable logging tools
  4. Help them upgrade their current tool to take advantage of any new features

Answer(s): C

Explanation:

The best course of action is C. Help them define their requirements and assess viable logging tools . Here's why:
Option A is premature.
While Stackdriver (now Cloud Logging) is a powerful tool, jumping directly to a specific solution without understanding the team's needs is inefficient. Option B, while useful, doesn't address the immediate need for tool selection. Option D assumes their current tool can even be upgraded to meet new cloud-specific requirements which is often not the case.
Option C is the most logical starting point. It follows a structured problem-solving approach. By first defining requirements, you ensure that any solution is a good fit. This involves understanding: the volume of logs, required retention periods, desired granularity, analysis needs, alert configuration, and budget. Once these requirements are clear, viable logging tools can be assessed. This might include Cloud Logging, or other third-party logging solutions, considering integrations and manageability. This approach aligns with best practices for solution architecture and avoids selecting a solution based on assumptions. Starting with clear requirements is essential for effective cloud solution design. This approach provides a solid foundation for the application development team to choose the appropriate solution.
Authoritative Links:
Google Cloud Logging Overview: https://cloud.google.com/logging/docs/ Google Cloud Architecture Framework: Operational Excellence: https://cloud.google.com/architecture/framework/operational-excellence (This framework emphasizes understanding requirements before choosing solutions.)



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