Google Associate Cloud Engineer ACE practice test

A team's Cloud Build pipeline must: (1) run unit tests, (2) build a Docker image only if tests pass, (3) push the image to Artifact Registry. Which cloudbuild.yaml structure correctly enforces this sequential dependency?

Trap 1: Use `waitFor` with step IDs to create a dependency graph between…

`waitFor` is used when steps should run in parallel or out of order — for a simple sequential pipeline, steps already run in order without `waitFor`.

Trap 2: Define the steps in three separate cloudbuild.yaml files and chain…

Using multiple YAML files and Cloud Composer adds unnecessary complexity — sequential steps in a single cloudbuild.yaml are sufficient.

Trap 3: Set `parallel: false` at the top level of cloudbuild.yaml to…

There is no `parallel: false` setting in cloudbuild.yaml — steps run sequentially by default.

A
Define all three steps in a single `steps` list — they run sequentially by default and stop on failure
Cloud Build steps execute sequentially by default. If any step fails (non-zero exit), the build stops and subsequent steps don't run — enforcing the test-before-build-before-push dependency.
B
Use `waitFor` with step IDs to create a dependency graph between all three steps
Why wrong: `waitFor` is used when steps should run in parallel or out of order — for a simple sequential pipeline, steps already run in order without `waitFor`.
C
Define the steps in three separate cloudbuild.yaml files and chain them with Cloud Composer
Why wrong: Using multiple YAML files and Cloud Composer adds unnecessary complexity — sequential steps in a single cloudbuild.yaml are sufficient.
D
Set `parallel: false` at the top level of cloudbuild.yaml to enforce sequential execution
Why wrong: There is no `parallel: false` setting in cloudbuild.yaml — steps run sequentially by default.

Question 2easymultiple choice

A team needs a database backup job to run every day at 2 AM UTC. The job calls an HTTP endpoint to trigger the backup. The endpoint requires no complex orchestration — just a timed HTTP call. Which GCP service handles this most simply?

Trap 1: Cloud Tasks with a daily task enqueued by a Cloud Function

Cloud Tasks handles task queuing and delivery but requires something to enqueue the task on schedule — more complex than Cloud Scheduler for a simple timed HTTP call.

Trap 2: Cloud Composer DAG running at 2 AM UTC

Cloud Composer (managed Airflow) is designed for complex multi-step data pipelines — it's overengineered for a single scheduled HTTP call.

Trap 3: Cloud Run Jobs triggered by a Cloud Monitoring alert at 2 AM

Cloud Monitoring alerts respond to metric thresholds, not time-based schedules — Cloud Scheduler is the right tool for time-based job triggering.

A
Cloud Tasks with a daily task enqueued by a Cloud Function
Why wrong: Cloud Tasks handles task queuing and delivery but requires something to enqueue the task on schedule — more complex than Cloud Scheduler for a simple timed HTTP call.
B
Cloud Scheduler with an HTTP target pointing to the backup endpoint
Cloud Scheduler sends a configured HTTP request to the backup endpoint at 2 AM UTC daily — the exact use case it's designed for, requiring minimal setup.
C
Cloud Composer DAG running at 2 AM UTC
Why wrong: Cloud Composer (managed Airflow) is designed for complex multi-step data pipelines — it's overengineered for a single scheduled HTTP call.
D
Cloud Run Jobs triggered by a Cloud Monitoring alert at 2 AM
Why wrong: Cloud Monitoring alerts respond to metric thresholds, not time-based schedules — Cloud Scheduler is the right tool for time-based job triggering.

Question 3easymultiple choice

A team wants to receive an email alert when the average CPU utilization of VMs in a managed instance group exceeds 80% for more than 5 minutes. What should they create in Cloud Monitoring?

Trap 1: A dashboard with a CPU utilization chart

Dashboards display metrics visually but do not send notifications — they require a human to observe the chart.

Trap 2: A log-based metric filter for high-CPU events

Log-based metrics extract numeric values from log entries — CPU utilization is a time-series metric, not a log-based event.

Trap 3: An uptime check targeting the managed instance group

Uptime checks verify that an endpoint is reachable and returns an expected response — they don't monitor resource utilization metrics.

A
A dashboard with a CPU utilization chart
Why wrong: Dashboards display metrics visually but do not send notifications — they require a human to observe the chart.
B
An alerting policy with a CPU utilization threshold condition
Alerting policies evaluate metric conditions continuously and send notifications via configured channels when thresholds are breached for the specified duration.
C
A log-based metric filter for high-CPU events
Why wrong: Log-based metrics extract numeric values from log entries — CPU utilization is a time-series metric, not a log-based event.
D
An uptime check targeting the managed instance group
Why wrong: Uptime checks verify that an endpoint is reachable and returns an expected response — they don't monitor resource utilization metrics.

Question 4mediummultiple choice

A Go service is consuming significantly more CPU than expected. The team suspects an inefficient function but doesn't know which one. Which Cloud Operations tool identifies CPU hotspots in production code?

Trap 1: Cloud Debugger

Cloud Debugger captures application state at a specific code location (like a breakpoint) without stopping execution — it doesn't profile CPU usage across functions.

Trap 2: Cloud Trace

Cloud Trace tracks end-to-end request latency across services — it doesn't reveal which internal functions are consuming CPU.

Trap 3: Cloud Monitoring custom dashboards

Custom dashboards display aggregate metrics like total CPU — they cannot drill into which specific code functions are responsible.

A
Cloud Debugger
Why wrong: Cloud Debugger captures application state at a specific code location (like a breakpoint) without stopping execution — it doesn't profile CPU usage across functions.
B
Cloud Trace
Why wrong: Cloud Trace tracks end-to-end request latency across services — it doesn't reveal which internal functions are consuming CPU.
C
Cloud Profiler
Cloud Profiler samples production applications continuously with minimal overhead, generating flame graphs that show exactly which functions are most CPU-intensive.
D
Cloud Monitoring custom dashboards
Why wrong: Custom dashboards display aggregate metrics like total CPU — they cannot drill into which specific code functions are responsible.

Question 5mediummultiple choice

A network team is creating a new VPC and must decide between auto mode and custom mode. Why would they choose custom mode?

Trap 1: Auto mode VPCs cost more per subnet than custom mode

VPC networking costs are based on traffic and peering, not the VPC mode — auto and custom mode have the same pricing structure.

Trap 2: Auto mode VPCs cannot be used with GKE clusters

GKE clusters can be deployed in both auto mode and custom mode VPCs.

Trap 3: Custom mode VPCs support more IP addresses per subnet than auto mode

The VPC mode doesn't restrict subnet IP capacity — both modes support the same CIDR sizes.

A
Auto mode VPCs cost more per subnet than custom mode
Why wrong: VPC networking costs are based on traffic and peering, not the VPC mode — auto and custom mode have the same pricing structure.
B
Custom mode allows full control over which regions have subnets and what CIDR ranges are used
In custom mode, the team creates subnets explicitly, choosing regions and CIDRs. This avoids CIDR conflicts with on-premises networks and prevents unnecessary subnet sprawl.
C
Auto mode VPCs cannot be used with GKE clusters
Why wrong: GKE clusters can be deployed in both auto mode and custom mode VPCs.
D
Custom mode VPCs support more IP addresses per subnet than auto mode
Why wrong: The VPC mode doesn't restrict subnet IP capacity — both modes support the same CIDR sizes.

Question 6mediummultiple choice

A company organizes its GCP projects by business unit — Finance, Engineering, and Sales. Which resource is best suited to group these projects while applying shared IAM policies to all projects in each group?

Trap 1: Apply labels to each project to identify the business unit

Labels are key-value metadata for billing and filtering — they do not support IAM policy inheritance.

Trap 2: Apply resource tags to each project for policy enforcement

Tags can be used with conditional IAM policies but are more complex to manage than Folders for this use case.

Trap 3: Create a Shared VPC host project for each business unit

Shared VPC is a networking feature for sharing subnets — it does not group projects for IAM or policy purposes.

A
Apply labels to each project to identify the business unit
Why wrong: Labels are key-value metadata for billing and filtering — they do not support IAM policy inheritance.
B
Apply resource tags to each project for policy enforcement
Why wrong: Tags can be used with conditional IAM policies but are more complex to manage than Folders for this use case.
C
Create GCP Folders for each business unit and add the relevant projects
Folders are the right level in the GCP hierarchy for grouping projects by business unit. IAM and Org Policies set on a Folder cascade to all child projects.
D
Create a Shared VPC host project for each business unit
Why wrong: Shared VPC is a networking feature for sharing subnets — it does not group projects for IAM or policy purposes.