GCP-ADP Practice Test 10 — 10 Questions

Question 1

You are using Dataprep to prepare data. How can you share your work with a team?

Accepted Answer

Share the Flow. In Google Cloud Dataprep, a Flow is the primary container that holds all your recipes, datasets, and transformation logic. Sharing the Flow directly with team members grants them access to the entire pipeline, allowing them to view, edit, and run the same data preparation steps. This is the built-in collaboration mechanism designed for team workflows, and it respects IAM permissions on the underlying project and datasets.

Answer

Copy the JSON definition

Answer

Share the underlying GCS file

Answer

Export to a CSV

Question 2

A Cloud Composer environment is experiencing high latency in the Airflow UI. What is a common cause and mitigation for this?

Accepted Answer

Excessive DAG complexity; optimize/simplify DAG code.. Cloud Composer's Airflow UI (webserver) performance degrades when the scheduler and webserver must parse and render an excessive number of complex DAGs, tasks, and dependencies. Overly complex DAG code — deep task graphs, heavy top-level Python, dynamic task generation — increases parsing time and metadata DB load, which manifests as UI latency. Simplifying and optimizing DAG code (reducing task count, avoiding top-level DB calls, using static DAGs) is the standard mitigation.

Answer

GKE node power failure; restart cluster.

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Too many users logged in; force logout.

Answer

Browser cache; clear your local cache.

Question 3

Which TWO of the following are true regarding Dataplex security? (Choose two)

Accepted Answer

IAM roles can be applied at the Lake, Zone, or Asset level. Option A is correct because Dataplex enforces fine-grained access control through IAM policies that can be attached at the Lake, Zone, or Asset level, allowing administrators to grant roles such as roles/dataplex.viewer or roles/dataplex.editor scoped to specific resources. Option E is correct because Dataplex supports VPC Service Controls perimeter integration, letting organizations restrict access to Dataplex resources and mitigate data exfiltration by adding dataplex.googleapis.com to service perimeters. Option B is not correct because Dataplex relies on Google Cloud IAM and federated identity rather than per-asset passwords. Option C is not correct because Dataplex respects and layers on top of existing Cloud Storage IAM permissions rather than ignoring them. Option D is not correct because Dataplex uses Google Cloud's existing authentication (Cloud Identity/Google Workspace, OAuth 2.0) instead of providing a separate authentication system.

Answer

Users must provide a password for each asset

Answer

Dataplex ignores existing storage permissions

Answer

Dataplex provides its own authentication system

Question 4

You are developing a dashboard in Looker. You notice that the 'Explore' menu is missing a specific set of fields. What should you check in the LookML model?

Accepted Answer

Verify the 'fields' parameter in the Explore definition.. If the Explore menu is missing a specific set of fields in Looker, you should verify the 'fields' parameter in the Explore definition within the LookML model. This parameter explicitly lists which fields are included in the Explore, and if a field is omitted, it will not appear. Other options like BigQuery location, table indexing, or browser cache are unrelated to LookML field visibility.

Answer

Check the project's 'BigQuery' location.

Answer

Check if the table is indexed.

Answer

Clear the browser cache.

Question 5

When using Cloud Scheduler to trigger a Workflows execution, what is the best practice for authenticating the scheduler to the Workflows API?

Accepted Answer

Use an OIDC token with a service account.. The best practice for Cloud Scheduler to authenticate to the Workflows API is to configure the job with an OIDC token tied to a service account that has permission to invoke the workflow. Cloud Scheduler supports OIDC authentication for HTTP targets, and the service account's identity is verified by Workflows via IAM. This avoids static credentials and follows least-privilege.

Answer

Use an API Key generated in the console.

Answer

Hardcode credentials in the scheduler job.

Answer

Use 'Public' access for the workflow.

Question 6

You need to export query results from BigQuery to a Google Sheet. What is the easiest way to do this?

Accepted Answer

Use the 'Save results' option and select 'Google Sheets'.. The BigQuery web console provides a 'Save results' button directly in the query results pane, offering 'Google Sheets' as a destination. This creates a new Sheet with the query output, making it the simplest, no-code method for exporting results to Sheets.

Answer

Use the BigQuery Data Transfer Service.

Answer

Export to a CSV file and manually upload to Sheets.

Answer

Write a Python script to call the Google Sheets API.

Question 7

Which GCP tool provides a managed way to run Apache Airflow workflows?

Accepted Answer

Cloud Composer. Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It handles the provisioning, scaling, and maintenance of Airflow infrastructure, allowing you to author, schedule, and monitor workflows (DAGs) using Python. It integrates natively with other GCP services like BigQuery, Dataflow, and Cloud Storage, making it the go-to choice for orchestrating complex data pipelines.

Answer

Dataflow

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Cloud Scheduler

Answer

Dataprep

Question 8

Which THREE of the following are common steps in a typical data pipeline orchestration pattern?

Accepted Answer

Validation. In a typical data pipeline orchestration pattern, data ingestion (C) is a core step because it moves raw data from sources such as databases, APIs, or files into the pipeline for processing. Transformation (D) is also essential, as it cleanses, enriches, aggregates, or otherwise converts ingested data into a usable format for analytics or downstream systems. Validation (A) is a common pipeline step that checks data for schema conformance, completeness, quality, and business rules before or after transformation to prevent bad data from propagating. Network cable installation (B) and hardware provisioning (E) are infrastructure or physical-layer tasks, not orchestration steps within a data pipeline workflow, so they are not correct.

Answer

Network cable installation

Answer

Hardware provisioning

Question 9

What is the primary function of the Airflow 'Executor' in Cloud Composer?

Accepted Answer

Deciding how to distribute tasks to workers.. The Executor in Airflow determines how tasks are distributed and run — it decides whether tasks execute locally, on Celery workers, on Kubernetes pods, or via other backends. In Cloud Composer, the executor configuration (CeleryExecutor or KubernetesExecutor) directly controls task distribution across the worker pool. This is the executor's core responsibility in the Airflow architecture.

Answer

Serving the Airflow UI web pages.

Answer

Storing the DAG file metadata.

Answer

Automatically scaling the GKE cluster.

Question 10

You need to verify the integrity of a large file uploaded to GCS. What is the standard way to do this?

Accepted Answer

Check MD5 hash. The standard way to verify integrity of a file uploaded to GCS is to compare its MD5 hash (or CRC32C) against the source file's hash. GCS stores the MD5 and CRC32C checksums as object metadata, and you can compare them to confirm the upload was not corrupted. This is the canonical integrity-verification method.

Answer

Use Cloud Logging

Answer

Check file size

Answer

Use Dataflow