Cloud Digital Leader Google Cloud products, services, and solutions — Timed 10 Questions

Question 1

A 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?

10 minute time limit — choose an answer to begin.

Accepted Answer

Row-level access policies using a filter on the department column. 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.

Answer

Custom IAM roles with fine-grained permissions

Answer

Column-level security with classification tags

Answer

Authorized views with a WHERE clause

Question 2

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?

Accepted Answer

The memory allocation might be too low if the file is large. 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.

Answer

The runtime python39 is not available

Answer

The function cannot be triggered by HTTP

Answer

The timeout is too short for processing a 2-minute upload

Question 3

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?

Accepted Answer

Lift and shift the application tier to Compute Engine on Windows VMs. Use Cloud SQL for SQL Server with encryption enabled, and configure Active Directory via Managed Microsoft AD.. 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.

Answer

Migrate the application to Compute Engine with SQL Server on Windows VMs. Use Cloud SQL for SQL Server but keep the database on-premises for compliance.

Answer

Containerize the application and deploy on GKE with SQL Server in a container. Use Cloud SQL for SQL Server with encryption.

Answer

Migrate the application to Compute Engine instances with SQL Server on Windows VMs. Use Cloud VPN for connectivity. Enable encryption on the VMs and use SSL for SQL connections.

Question 4

Given the Cloud Run service configuration above, what happens when a new revision is created after deploying a change to the container image?

Accepted Answer

The new revision receives all traffic automatically.. 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.

Answer

Traffic is split evenly between the old and new revision.

Answer

The service becomes unavailable until the new revision is ready.

Answer

Cloud Run automatically creates a new revision and routes traffic to it.

Answer

Traffic continues to go to the old revision until the traffic section is updated.

Question 5

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?

Accepted Answer

Set the HPA target CPU utilization to 70% and remove the scale-up stabilization window (set to 0 seconds).. 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.

Answer

Increase the HPA scale-up stabilization window to 10 minutes to prevent thrashing.

Answer

Enable session affinity on the backend service to keep users on the same pod.

Answer

Use a CronJob to proactively scale the backend service before expected peak hours.

Question 6

A startup's development team stores application logs and metrics from its Compute Engine workloads. The team wants a fully managed service that can ingest, search, and analyze this telemetry, and it wants to create alerting policies that notify an on-call engineer when error rates exceed a threshold. Which Google Cloud service should the team use?

Accepted Answer

Cloud Monitoring. Cloud Monitoring is the managed observability platform that collects metrics from Compute Engine and other Google Cloud resources, supports dashboards and metric-based alerting policies, and integrates with Cloud Logging for log-based metrics. It can notify on-call engineers through multiple channels when thresholds are breached, which matches the requirement to ingest, search, and alert on telemetry. The other services specialize in tracing, profiling, or logs alone and lack the combined metric ingestion and alerting capability.

Answer

Cloud Trace

Answer

Cloud Profiler

Answer

Cloud Logging only

Question 7

A data analytics team needs to analyze petabytes of structured data using SQL queries without managing any database infrastructure. Query results must return within seconds for most queries. Which Google Cloud service is designed for this use case?

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for analyzing petabytes of data using SQL without any infrastructure management. Its columnar storage and distributed query engine enable sub-second query performance on large datasets, making it ideal for this use case.

Answer

Cloud SQL

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 8

A developer wants to deploy a containerized web application without managing servers, clusters, or Kubernetes configuration. The application should automatically scale to zero when not in use and handle bursts of traffic. Which Google Cloud service is the best fit?

Accepted Answer

Cloud Run. Cloud Run is the best fit because it is a fully managed serverless container platform that automatically scales to zero when idle and scales up to handle traffic bursts, without requiring any server, cluster, or Kubernetes configuration. The developer simply deploys a container image, and Cloud Run handles all infrastructure management, including scaling and load balancing.

Answer

Google Kubernetes Engine (GKE)

Answer

Compute Engine

Answer

App Engine Standard

Question 9

A retail company wants to build a recommendation engine that suggests products to customers based on their browsing history. The team has ML expertise but wants to use Google's pre-built ML infrastructure to train and deploy models at scale without managing compute resources. Which Google Cloud service should they use?

Accepted Answer

Vertex AI. Vertex AI is the correct choice because it provides a fully managed, unified ML platform that handles the entire ML workflow—from data preparation and training to deployment and monitoring—without requiring the team to manage underlying compute infrastructure. It integrates with Google Cloud's pre-built ML infrastructure, including distributed training, AutoML, and custom model serving, making it ideal for building and scaling a recommendation engine.

Answer

BigQuery ML

Answer

Cloud AI Platform Notebooks (now Vertex AI Workbench)

Answer

Cloud Dataflow

Question 10

A company needs to store large volumes of unstructured data (images, videos, backups, documents) with high durability and global accessibility. Which Google Cloud service is designed for object storage at any scale?

Accepted Answer

Cloud Storage. Cloud Storage is Google Cloud's fully managed, scalable object storage service designed for unstructured data such as images, videos, backups, and documents. It offers high durability (99.999999999% annual durability) and global accessibility via a unified namespace, making it the correct choice for storing large volumes of unstructured data at any scale.

Answer

Persistent Disk

Answer

Cloud Filestore

Answer

Cloud Spanner