20+ practice questions focused on Google Cloud products, services, and solutions — one of the most tested topics on the Google Cloud Digital Leader exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Google Cloud products, services, and solutions PracticeA company uses BigQuery for analytics and needs to enforce row-level security based on user department. Only users from the 'Sales' department should see rows where department = 'Sales'. Which BigQuery feature should they use?
Explanation: Row-level access policies in BigQuery allow you to attach a filter expression (e.g., department = 'Sales') directly to a table, so only users whose department matches the policy's condition can see those rows. This is the native, purpose-built feature for row-level security and works with IAM principals such as users or groups, making it the right fit for restricting Sales users to Sales rows. Custom IAM roles (A) control actions on resources, not individual rows, so they cannot enforce a per-row department filter. Column-level security (B) uses policy tags to mask or restrict columns, not rows. Authorized views (C) can filter rows, but they require querying through the view and do not enforce row-level security on the underlying table itself.
A developer deploys a Cloud Function with the command shown: gcloud functions deploy my-function --gen2 --region=us-central1 --runtime=python39 --trigger-http --allow-unauthenticated --timeout=300s The function needs to process a file upload that typically takes 2 minutes. What is the most likely issue?
Explanation: The command shown does not specify a memory allocation, so the Cloud Function defaults to 256 MB. If the file being uploaded is large, this low memory can cause the function to run out of memory and fail, even if the timeout is sufficient. Processing a file upload often requires loading the file into memory, making memory a critical resource.
A financial services company is migrating a legacy monolithic application to Google Cloud. The application uses a SQL Server database and has compliance requirements to encrypt data at rest and in transit. The migration must minimize code changes. The application runs on Windows Server and currently uses Active Directory for authentication. The company wants to use Google Cloud's managed services where possible. Which approach best meets these requirements?
Explanation: Option B minimizes code changes by lifting and shifting the application to Compute Engine while using Cloud SQL for SQL Server, a managed database that supports encryption at rest and in transit. Managed Microsoft AD provides Active Directory authentication. The other options either keep the database on-premises, require containerization, or use unmanaged SQL Server on VMs with manual encryption and connectivity, so they do not best meet the managed services and compliance requirements.
Given the Cloud Run service configuration above, what happens when a new revision is created after deploying a change to the container image?
Explanation: When you deploy a change to a Cloud Run service, a new revision is created and, by default, it receives 100% of traffic automatically. Traffic is not held on the old revision unless you explicitly use --no-traffic or configure a traffic split that excludes the new revision.
A financial services company runs a multi-tier application on Google Kubernetes Engine (GKE). The application consists of a frontend service, a backend service, and a database on Cloud SQL. Recently, they noticed that the backend service experiences high latency during peak trading hours, causing the frontend to time out. The backend service is CPU-intensive and currently runs with a single replica. The team wants to reduce latency and ensure high availability without over-provisioning resources. They have enabled Horizontal Pod Autoscaling (HPA) based on CPU utilization with a target of 80% and default stabilization windows. However, during sudden traffic spikes, the HPA takes over 5 minutes to scale up because of the scale-up stabilization window and the time to trigger. The company cannot tolerate latency spikes during scaling. Which course of action should they take to minimize latency during traffic spikes?
Explanation: Reducing the HPA target CPU utilization to 70% provides a larger buffer before the threshold is crossed, allowing earlier scaling initiation. Removing the scale-up stabilization window (setting it to 0 seconds) eliminates the default 3-minute scale-up delay, enabling the HPA to react immediately to CPU spikes. This directly addresses the latency issue during sudden traffic spikes by reducing the time to add replicas.
+15 more Google Cloud products, services, and solutions questions available
Practice all Google Cloud products, services, and solutions questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Google Cloud products, services, and solutions. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Google Cloud products, services, and solutions questions on the GCDL frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Google Cloud products, services, and solutions is tested as part of the Google Cloud Digital Leader blueprint. Practicing with targeted Google Cloud products, services, and solutions questions ensures you can handle any format or difficulty that appears.
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