Google Professional Cloud Architect Exam Dumps & Practice Test Questions

Question 1:

Your organization is updating its API to enhance the developer experience. They want to keep the existing API version accessible for current users while allowing new users and testers to access the new API version. 

Both API versions should share the same SSL certificate and DNS configuration. What is the best approach to achieve this?

A. Set up a new load balancer specifically for the new API version
B. Require existing clients to switch to a new endpoint for the updated API
C. Have the old API redirect requests to the new API based on URL paths
D. Use different backend server pools for each API version behind a single load balancer

Answer: D

Explanation:

When managing multiple API versions simultaneously, especially to maintain backward compatibility while rolling out new features, it's essential to route requests efficiently without changing the domain or SSL certificates. The goal is to serve both API versions under the same domain and SSL while properly directing traffic.

Option A suggests creating a new load balancer for the new API. This would require additional DNS records and certificates, which conflicts with the requirement to keep the existing DNS and SSL unchanged, and increases infrastructure complexity.

Option B involves reconfiguring all existing clients to use a new endpoint. This approach can disrupt current users and demands extensive client-side changes, which is usually impractical and can lead to user dissatisfaction.

Option C proposes forwarding traffic from the old API to the new one. While this may seem viable, it adds latency and overhead, complicates maintenance, and may create performance bottlenecks since the old API acts as an intermediary.

Option D is the optimal solution. By configuring the load balancer with multiple backend pools, it can route incoming requests based on URL paths—e.g., /v1/ to the old API and /v2/ to the new API—while serving both from the same domain and SSL certificate. This approach simplifies management, allows independent scaling, and offers a seamless experience for all users.

Hence, D best meets the company's needs by efficiently managing traffic for both API versions without requiring DNS or SSL changes.

Question 2:

Your company is migrating a multi-petabyte dataset to the cloud, requiring 24/7 availability. The business analysts accessing the data are only familiar with SQL interfaces. 

What storage solution should you choose to optimize ease of data analysis?

A. Load the data into Google BigQuery
B. Store the data in Google Cloud SQL
C. Save flat files in Google Cloud Storage
D. Stream data into Google Cloud Datastore

Answer: B

Explanation:

Selecting the right cloud storage for large datasets depends on the data access patterns and user familiarity. Since the analysts only know SQL, the storage must support SQL queries natively and offer high availability.

Option A, Google BigQuery, is a fully managed data warehouse designed for large-scale analytics and can handle petabyte-scale datasets with excellent query performance. However, BigQuery is best suited for analytical, batch workloads rather than transactional or day-to-day SQL queries that analysts typically run. It also requires learning its SQL dialect and data loading mechanisms.

Option B, Google Cloud SQL, provides fully managed relational databases (MySQL, PostgreSQL, SQL Server) with a familiar SQL interface. It supports transactional workloads and real-time querying, making it easy for business analysts to work with data using traditional SQL. Cloud SQL also offers high availability features suitable for 24/7 operations. The main limitation is scalability for extremely large datasets; while Cloud SQL can handle large volumes, managing petabyte-scale data might require sharding or other architectural considerations.

Option C, Google Cloud Storage, is object storage ideal for unstructured data like flat files. Although it offers excellent scalability and availability, it does not provide a native SQL interface, requiring additional services like BigQuery or Dataproc for analysis, which complicates workflows.

Option D, Google Cloud Datastore, is a NoSQL document database optimized for scalability and high availability but lacks SQL support, making it unsuitable for users needing SQL querying.

Given the analysts’ SQL-only knowledge and the need for 24/7 availability, Google Cloud SQL is the best balance of familiarity, availability, and ease of use, making B the correct choice.

Question 3:

An operations manager wants to ensure best practices are followed when migrating a J2EE application to the cloud. 

Which three of the following practices would you recommend to guarantee efficient, secure, and manageable cloud deployment? (Select three.)

A. Modify the application to run on Google App Engine
B. Integrate Cloud Dataflow to collect real-time application metrics
C. Use a monitoring tool like Stackdriver Debugger to instrument the app
D. Adopt an automation framework for consistent cloud infrastructure provisioning
E. Implement continuous integration with automated testing in a staging environment
F. Migrate the database from MySQL to a NoSQL option like Cloud Datastore or Bigtable

Answers: C, D, E

Explanation:

Migrating a J2EE application to the cloud requires attention to maintain operational efficiency, security, and ease of management. The right practices can reduce downtime, improve scalability, and support ongoing maintenance.

Option C is crucial because monitoring is the backbone of understanding application health. Tools like Stackdriver Debugger allow developers to profile and debug live applications, identify performance bottlenecks, and quickly react to errors, ensuring stable operation after migration.

Option D addresses the complexity of cloud infrastructure provisioning. Automation frameworks such as Terraform or Google Cloud Deployment Manager allow consistent, repeatable deployments, eliminating manual errors and enabling quick scaling or updates to infrastructure. This improves reliability and operational efficiency.

Option E emphasizes the importance of continuous integration (CI). By implementing CI pipelines with automated testing in a staging environment, teams can catch bugs early and ensure that code changes do not disrupt application functionality. This practice supports faster development cycles and a more reliable production environment.

Option A, while potentially helpful in some contexts, might require extensive re-architecting of the J2EE app, which can be costly and risky. Not all applications are suitable for App Engine.

Option B is not ideal because Cloud Dataflow is designed for data processing pipelines rather than application monitoring. Real-time metrics are better captured by dedicated monitoring tools.

Option F suggests migrating from relational MySQL to NoSQL, which can be a major architectural shift. This should only be considered if the application’s data patterns warrant it. Otherwise, maintaining a relational database often simplifies migration.

In conclusion, C, D, and E represent practical, low-risk best practices that ensure the application runs smoothly, is well-monitored, and supports agile development in the cloud environment.

Question 4:

A software development team is launching a new cloud application but feels their current logging system won’t meet their needs for error tracking and historical log analysis. 

As a solutions architect, what is the best approach to guide them in selecting an appropriate logging tool?

A. Advise them to install the Google StackDriver logging agent.
B. Share a list of online resources about logging best practices.
C. Help them define their specific logging requirements and evaluate suitable tools.
D. Support them in upgrading their existing logging tool with new features.

Answer: C

Explanation:

When a development team is moving to a new cloud-based product and finds their existing logging solution inadequate, it’s essential first to thoroughly understand their specific needs before recommending a tool. Logging tools vary greatly in their capabilities—such as error capture, real-time monitoring, scalability, and integration with other services—so a one-size-fits-all approach is often ineffective.

Option C, helping the team clearly define their requirements, is the most strategic choice. This involves identifying what types of logs they need to capture (errors, warnings, info), how they want to analyze historical data, whether they require real-time alerts, and if there are any compliance or security constraints. Once these factors are clear, you can evaluate and compare tools that best align with their technical environment and budget.

Option A, recommending a specific tool like Google StackDriver, could be premature without understanding if it fits the team’s environment or requirements. While it’s a strong cloud-native logging option, pushing it without evaluation risks missing better fits.

Option B provides general guidance but doesn’t offer actionable support in tool selection or customization.

Option D, upgrading the current tool, might be suitable if the existing solution can meet needs with enhancements. However, it could also limit the team if the tool lacks fundamental features required by a cloud-native application.

In summary, understanding the team’s exact logging needs and then assessing available tools ensures the chosen solution will be effective, scalable, and sustainable for their new cloud product.

Question 5:

Your company’s web hosting platform has seen an 80% reduction in unplanned rollbacks due to improved QA and testing. 

To further decrease rollback occurrences, which two of the following strategies should you implement? (Select two.)

A. Adopt a green-blue deployment model
B. Replace the QA environment with canary releases
C. Convert the platform from monolithic to microservices architecture
D. Reduce reliance on relational databases
E. Switch from relational databases to NoSQL databases

Answer: A, B

Explanation:

After achieving a significant rollback reduction through enhanced QA and testing, the next focus should be on deployment methodologies that reduce risks and increase control over production releases.

Green-blue deployment (Option A) is a powerful approach where two identical environments exist: one live (blue) and one updated version (green). The new release is fully tested in the green environment before switching traffic from blue to green. If problems arise, it’s easy to revert to the stable blue environment, minimizing downtime and risk. This strategy is widely used to reduce rollback occurrences because it separates deployment from production traffic.

Canary releases (Option B) gradually roll out new features to a small subset of users, allowing early detection of issues without impacting all users. This incremental approach enables quicker, safer validation of changes in production and reduces rollback risk by limiting exposure.

Options C, D, and E relate more to architectural or database changes. While microservices (C) can improve scalability and development agility, they don’t directly reduce rollbacks and introduce complexity in deployment and testing. Similarly, database changes (D and E) don’t inherently affect rollback rates, which are typically tied to deployment strategy rather than database technology.

Therefore, deploying green-blue releases and canary deployments effectively target deployment risks, enabling further rollback reduction beyond what QA improvements can achieve.

Question 6:

The Director of Engineering wants all developer environments moved from on-premises VMs to Google Cloud Platform (GCP) to cut costs. These environments require multiple start/stop cycles daily with state persistence. Additionally, the finance team needs clear cost tracking. 

Which two steps should you implement to satisfy these requirements? (Select two.)

A. Use the --no-auto-delete flag on persistent disks and stop the VM
B. Use the --auto-delete flag on persistent disks and terminate the VM
C. Apply CPU utilization labels on VMs and include these in the BigQuery billing export
D. Enable BigQuery billing export and use labels to allocate costs by group
E. Store state on local SSDs, snapshot persistent disks, then terminate the VM
F. Store state in Google Cloud Storage, snapshot persistent disks, then terminate the VM

Answer: C, D

Explanation:

When migrating developer environments to GCP with requirements for state persistence and cost visibility, the solution must focus both on data durability and transparent billing.

Applying labels to VMs (Option C) is critical because labels act as metadata tags (e.g., by project, team, or department) that categorize cloud resources. Combined with GCP’s BigQuery billing export (Option D), which exports detailed billing data to a queryable warehouse, these labels enable granular cost tracking by group or project. This setup gives the finance team the ability to monitor and allocate costs accurately based on actual resource consumption, improving financial transparency.

Using the --no-auto-delete flag on persistent disks (Option A) ensures disks aren’t deleted when the VM stops, preserving state. However, this alone doesn’t provide cost visibility and may result in lingering costs if disks aren’t actively managed. The --auto-delete flag (Option B) deletes disks when VMs terminate, which risks losing data state and conflicts with persistence needs.

Storing state on local SSDs or snapshots (Option E) may preserve data but doesn’t contribute to cost visibility and is less flexible compared to persistent disks or cloud storage. Storing state in Google Cloud Storage (Option F) supports persistence but lacks direct VM-level cost attribution or billing transparency.

Overall, combining resource labeling with detailed billing exports is the best practice to provide both state persistence and financial accountability during cloud migration.

Question 7:

Your company wants to monitor the occupancy of meeting rooms that are reserved for scheduled meetings. There are 1,000 rooms distributed across five offices on three continents. Each room is equipped with a motion sensor that sends data every second, including a sensor ID and other discrete status information. Alongside data about account owners and office locations, this information will be analyzed. 

Which type of database is best suited to store and manage this continuous sensor data?

A. Flat file
B. NoSQL
C. Relational
D. Blobstore

Correct Answer: B

Explanation:

In this use case, the company must efficiently capture and manage real-time data streams from motion sensors installed in 1,000 meeting rooms worldwide. These sensors generate data every second, resulting in a large volume of time-stamped, semi-structured data that requires quick storage and retrieval. The ideal database must be able to handle this scale, variability, and velocity effectively.

A NoSQL database is the best choice here because it offers several advantages suited for sensor data management:

1. Handling High Volume and Velocity:
   Sensors continuously emit data, leading to rapid accumulation. NoSQL databases are designed for handling vast amounts of unstructured or semi-structured data, such as time-series sensor data, efficiently. They are optimized to ingest and process high-velocity data streams in near real-time.

2. Schema Flexibility:
   Sensor data often evolves over time — new types of readings or metadata may be introduced. Unlike relational databases that require predefined schemas, NoSQL databases allow flexible, dynamic schemas that adapt easily to changes in data structure without downtime.

3. Scalability:
   With devices spread across continents, the database must scale horizontally to distribute workload and data storage across multiple nodes or regions. NoSQL databases like Cassandra or MongoDB are built to scale horizontally, providing fault tolerance and high availability.

4. Performance:
   NoSQL solutions are optimized for fast writes and reads, crucial for processing frequent sensor updates and enabling real-time analytics.

Why not the other options?

• Flat files are simple but lack indexing, querying, and scalability needed for large-scale sensor data.
  
  

• Relational databases excel with structured data and fixed schemas but can struggle with high write loads and schema changes inherent in sensor data.

• Blobstore is suited for storing large binary objects (images, videos) but is inefficient for querying or indexing sensor data.

Thus, a NoSQL database offers the ideal combination of scalability, flexibility, and performance to manage continuous, distributed sensor data effectively.

Question 8:

You’ve set up an autoscaling instance group to manage web traffic for an upcoming product launch. After configuring it as the backend for an HTTP(S) load balancer, you observe that VM instances are terminated and relaunched every minute. These VMs do not have public IP addresses. You confirmed each instance returns the expected web response using curl. 

What should you do to ensure the backend is correctly configured?

A. Check if a firewall rule allows inbound HTTP/HTTPS traffic to the load balancer.
B. Assign public IPs to each VM and configure firewall rules to allow load balancer traffic to these IPs.
C. Verify a firewall rule exists allowing load balancer health checks to reach the VM instances.
D. Tag each instance with the load balancer's name and configure a firewall rule to permit traffic from the load balancer source to those tagged instances.

Correct Answer: C

Explanation:

The frequent termination and relaunching of VM instances within the autoscaling group strongly indicates that the load balancer’s health checks are failing to confirm that backend instances are healthy. Load balancers use health checks as a primary method to determine if an instance can handle incoming traffic. If an instance fails these checks, it is marked unhealthy and replaced.

Even though direct tests (e.g., curl commands) show the expected web responses from each instance, the load balancer itself may be unable to reach or properly assess the instance’s health. This commonly results from firewall rules blocking health check probes.

The crucial step is to ensure that the firewall permits traffic from the load balancer’s health check IP ranges to reach the backend VM instances on the necessary ports (usually HTTP/HTTPS). This is exactly what Option C addresses.

Why other options are less appropriate:

• Option A: Firewall rules allowing HTTP/HTTPS inbound traffic to the load balancer help client requests reach the load balancer, but do not affect health check traffic from the load balancer to backend VMs.

• Option B: Assigning public IPs is unnecessary and often discouraged in private or internal load balancing scenarios. The load balancer and instances should communicate using private IPs within the same VPC or network.

• Option D: While tagging instances and configuring firewall rules by tag can be useful, it is more indirect and complex than simply allowing health check traffic from the load balancer service.

In summary, the root cause is the firewall blocking health check probes, so confirming and configuring the correct firewall rules to allow load balancer health checks to reach VM instances is the key step to stabilizing the backend pool, making Option C the correct choice.

Question 9:

You are designing a highly available web application on Google Cloud Platform that must serve users globally with minimal latency. 

Which combination of services and strategies should you use to achieve this goal?

A. Deploy the application in a single region with multiple zones and use Cloud Load Balancing to distribute traffic within the region
B. Use multiple regional deployments and Cloud CDN to cache static content close to users worldwide
C. Host the application on Compute Engine instances behind an Internal Load Balancer in one region
D. Use a Cloud SQL instance in a single zone and rely on autoscaling groups to handle user load

Correct Answer: B

Explanation:

Designing a globally available and low-latency application on Google Cloud requires a combination of multi-region deployments and content delivery optimization.

Option B is the best choice because it leverages multiple regional deployments of your application, which means the app runs in several Google Cloud regions around the world. This geographical distribution reduces latency by placing compute resources closer to end-users. Additionally, using Cloud CDN (Content Delivery Network) caches static content such as images, CSS, and JavaScript files at edge locations worldwide, further speeding up delivery to users regardless of their location.

Option A is insufficient for global users because deploying in a single region, even with multiple zones, only optimizes availability within that region but does not address latency for users located far away. Cloud Load Balancing will distribute traffic within the single region but cannot reduce latency for global users.

Option C is flawed because an Internal Load Balancer is designed for distributing traffic within a Virtual Private Cloud (VPC) network and cannot be accessed from the internet or provide global load balancing.

Option D relies on a single-zone Cloud SQL instance, which introduces a single point of failure and potential latency issues. Autoscaling groups (managed instance groups) help handle load but do not improve geographical availability or latency.

By deploying the application in multiple regions and using Cloud CDN, you ensure high availability, fault tolerance, and reduced latency globally. This approach aligns with Google’s best practices for designing scalable, resilient, and performant cloud applications—key knowledge areas for the Professional Cloud Architect exam.

Question 10:

Your company needs to migrate a large, on-premises relational database to Google Cloud with minimal downtime and no loss of data. 

Which Google Cloud service and migration strategy would best support this requirement?

A. Use Cloud SQL with database import/export and schedule a maintenance window for migration
B. Use Database Migration Service (DMS) to perform continuous replication and cutover with minimal downtime
C. Migrate data using Transfer Appliance and manually configure Cloud SQL after migration
D. Export the database as SQL dump files and import them into Cloud SQL during off-peak hours

Correct Answer: B

Explanation:

Minimizing downtime and avoiding data loss during database migration is a critical requirement for many enterprises moving to Google Cloud.

Option B is the optimal solution because Google’s Database Migration Service (DMS) supports continuous data replication from on-premises databases to Cloud SQL with near real-time synchronization. This allows the source database to remain operational during the migration process. Once the data is fully synchronized and validated, a cutover can be performed with minimal downtime, reducing disruption to business operations.

Option A, using import/export with scheduled maintenance, is suitable for smaller datasets or less critical systems but typically requires downtime during the export/import process, which may not meet minimal downtime requirements.

Option C, using Transfer Appliance, is best suited for large data transfers that are not time-sensitive. It is a physical appliance shipped to your data center to collect data and then shipped back to Google for upload. While effective for large volume data, it does not support continuous replication or minimal downtime migration.

Option D, exporting SQL dump files and importing during off-peak hours, is a manual and time-consuming process that generally requires downtime, and risks data loss if changes happen during the export/import.

For the Professional Cloud Architect exam, understanding the capabilities and use cases of Google Cloud migration services like DMS is essential. DMS allows enterprises to migrate mission-critical databases with minimal downtime and ensures data consistency—key factors in a successful migration strategy and maintaining business continuity.