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Exam Topics

The certification exam covers six domains. Each of them has a list of subtopics that the applicants should understand before taking the test. To develop competence in the content, it is recommended that you spend some time going over the exam guide and taking some practice tests. Below are the details of the subjects covered in the Google Professional Cloud Architect exam:

Designing & Planning Cloud Solution Architectures

Create a migration plan: the students should be able to integrate the solution with current systems; migrate data and systems to support solutions; license mapping; test and proof concepts; network planning; dependency management strategy;
Design network, compute, and storage resources: this covers one’s competence in selecting data processing technologies, relevant storage types, and compute resources; integration with multi-Cloud/on-premises environments; Cloud-native networking; mapping compute requirements for platform products;
Design a solution infrastructure that fulfills the technical prerequisites: the candidates must demonstrate their understanding of Cloud resources elasticity; failover design and high availability; latency and performance; scalability to fulfill growth needs;
Design a solution infrastructure that fulfills the business prerequisites: this covers skills in cost optimization; product strategy and business use cases; integration with the external systems; supporting application designs; build-buy-or-modify; design decision trade-off; data movement; observability and compliance; success measurements;
Envision future solutions enhancements: the individuals should understand the technology and Cloud improvements; evangelism and advocacy; business requirements evolution.

To become a Google Certified Professional - Cloud Architect, candidates must pass the Google Professional-Cloud-Architect Exam, which consists of multiple-choice and scenario-based questions. Professional-Cloud-Architect exam covers a wide range of topics, including cloud architecture, storage and data management, compute and networking, security, and business continuity. Passing Professional-Cloud-Architect exam demonstrates that the candidate has the necessary knowledge and skills to design and implement solutions on the Google Cloud Platform, making them a valuable asset for any organization looking to leverage cloud technology to drive innovation and growth.

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Google Professional-Cloud-Architect certification exam consists of 50 multiple-choice and multiple-select questions that need to be completed within two hours. Professional-Cloud-Architect exam measures the candidate's proficiency in several areas such as designing and planning a cloud solution architecture, managing and provisioning the cloud infrastructure, and ensuring the security and compliance of the cloud solutions. Professional-Cloud-Architect Exam also tests the candidate's understanding of cloud-native architecture principles, application development methodologies, and hybrid and multi-cloud solutions.

Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q280-Q285):

NEW QUESTION # 280
You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore. What should you do?

A. Point gcloud datastore create-indexes to your configuration file
B. Create an HTTP request to the built-in python module to send the index configuration file to your application
C. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file
D. Upload the configuration file the App Engine's default Cloud Storage bucket, and have App Engine detect the new indexes

Answer: A

Explanation:
You upload your index.yaml configuration file to Datastore with the gcloud command. If the index.yaml file defines any indexes that don't exist in Datastore, those new indexes are built.
gcloud datastore indexes create index.yaml
https://cloud.google.com/sdk/gcloud/reference/datastore/indexes/create

NEW QUESTION # 281
You are a cloud architect for a healthcare organization that is running its containerized, stateless web applications on Google Kubernetes Engine (GKE) with Autoscaler enabled. These customer-facing services have a strict 99.5% availability requirement. You also run nightly batch processing jobs for data analysis that are fault-tolerant and can be stopped and restarted. You need to ensure the solution meets the availability SLA while minimizing compute costs wherever possible. What should you do?

A. Configure the GKE cluster with a single node pool of Standard VMs to run both workloads.
B. Create two separate GKE node pools. Use a node pool of Standard VMs for the web applications and another node pool of Spot VMs for the batch jobs.
C. Create two separate GKE node pools. Use a node pool with Spot VMs for the web applications and a node pool with Standard VMs for the batch jobs.
D. Create two separate GKE node pools using Standard VMs for both. Purchase one-year committed use discounts (CUDs) for the resources in the batch processing node pool.

Answer: B

Explanation:
Optimizing infrastructure costs while maintaining workload availability requires matching application runtime characteristics with appropriate Compute Engine instance purchasing models. Stateless, customer-facing web applications require deterministic availability to meet a 99.5% SLA, making Standard VMs the required choice to prevent unexpected disruptions.
Conversely, nightly batch processing jobs are fault-tolerant and capable of handling interruptions. Running these batch jobs on a separate node pool composed of Spot VMs allows the organization to take advantage of deeply discounted compute resources (up to 60-91% cheaper than on-demand pricing). Since Spot VMs can be reclaimed by Compute Engine at any time with a 30-second termination notice, they are ideal for stateless batch jobs, while remaining isolated from the critical web application node pool. This approach maximizes cost efficiency without introducing availability risks to customer-facing traffic.
References: Cloud Architect (GCP) Study Guide topics: " GKE Node Pool Optimization " , " Compute Engine Spot VM Lifecycles " , " Designing Cost-Optimized Architectures " , and " Workload Isolation Strategies " .

NEW QUESTION # 282
You have an outage in your Compute Engine managed instance group: all instance keep restarting after 5 seconds. You have a health check configured, but autoscaling is disabled. Your colleague, who is a Linux expert, offered to look into the issue. You need to make sure that he can access the VMs. What should you do?

A. Disable autoscaling for the instance group. Add his SSH key to the project-wide SSH Keys
B. Perform a rolling restart on the instance group
C. Grant your colleague the IAM role of project Viewer
D. Disable the health check for the instance group. Add his SSH key to the project-wide SSH keys

Answer: D

NEW QUESTION # 283
For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the compute workloads for your company, Mountkirk Games. Considering the Mountkirk Games business and technical requirements, what should you do?

A. Create a global load balancer with managed instance groups and autoscaling policies. Use preemptible Compute Engine instances.
B. Create network load balancers. Use non-preemptible Compute Engine instances.
C. Create network load balancers. Use preemptible Compute Engine instances.
D. Create a global load balancer with managed instance groups and autoscaling policies. Use non-preemptible Compute Engine instances.

Answer: D

Explanation:
Topic 8, Helicopter Racing League Case
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
Existing content is stored in an object storage service on their existing public cloud provider.
Video encoding and transcoding is performed on VMs created for each job.
Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
Support ability to expose the predictive models to partners.
Increase predictive capabilities during and before races:
Race results
Mechanical failures
Crowd sentiment
Increase telemetry and create additional insights.
Measure fan engagement with new predictions.
Enhance global availability and quality of the broadcasts.
Increase the number of concurrent viewers.
Minimize operational complexity.
Ensure compliance with regulations.
Create a merchandising revenue stream.
Technical requirements
Maintain or increase prediction throughput and accuracy.
Reduce viewer latency.
Increase transcoding performance.
Create real-time analytics of viewer consumption patterns and engagement.
Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.

NEW QUESTION # 284
Your company is moving 75 TB of data into Google Cloud. You want to use Cloud Storage and follow Google- recommended practices. What should you do?

A. Move your data onto a Transfer Appliance. Use a Transfer Appliance Rehydrator to decrypt the data into Cloud Storage.
B. Install gsutil on each server containing data. Use streaming transfers to upload the data into Cloud Storage.
C. Install gsutil on each server that contains data. Use resumable transfers to upload the data into Cloud Storage.
D. Move your data onto a Transfer Appliance. Use Cloud Dataprep to decrypt the data into Cloud Storage.

Answer: C

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/transferring-big-data-sets-to-gcp

NEW QUESTION # 285
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