GCP-ADP Practice Test 5 — 10 Questions

Question 1

You have a Data Catalog taxonomy that you want to share across multiple projects. How do you achieve this?

Accepted Answer

Define it in a central project and share access. Data Catalog taxonomies are project-scoped resources, so to share one across multiple projects you define it once in a central (host) project and grant other projects' users or service accounts the appropriate Data Catalog IAM roles (e.g., Data Catalog Viewer/Editor) on that taxonomy. This avoids duplication and keeps policy definitions consistent. Cross-project sharing is achieved through IAM bindings, not by relocating the resource.

Answer

Move the taxonomy to the Organization node

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Duplicate the taxonomy in each project

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Use Cloud Resource Manager to link them

Question 2

What is the role of the 'Dataplex Data Steward'?

Accepted Answer

To manage metadata, quality rules, and compliance. A Dataplex Data Steward is responsible for managing metadata, defining and enforcing data quality rules, and ensuring compliance with governance policies across data assets. This role uses Dataplex features like Business Glossary, Data Quality tasks, and Data Catalog to curate and govern data. It is a governance-focused role, not an engineering or analytics role.

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To write SQL queries for the business

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To develop ETL pipelines

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To manage the underlying infrastructure

Question 3

You are configuring a Dataplex Data Quality task. You want to validate that a 'user_id' column has no NULL values. Where do you define this rule?

Accepted Answer

In a YAML configuration file. Dataplex Data Quality tasks are defined using a YAML specification that describes the rules, including non-null constraints on columns. The YAML file is uploaded to a Cloud Storage bucket and referenced when creating the Data Quality task. This approach allows for flexible, version-controlled rule definitions that are decoupled from the underlying data schema.

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In the Cloud Storage metadata

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In the BigQuery table schema

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In the Dataplex Asset definition

Question 4

A data analyst is working in a Vertex AI Workbench notebook. They want to connect to a private BigQuery dataset. How should they authenticate?

Accepted Answer

The instance's attached service account will handle authentication automatically.. Vertex AI Workbench instances are associated with a service account that provides credentials to Google Cloud APIs. When the notebook calls BigQuery, the client library uses the instance's attached service account automatically via the metadata server, so no manual credentials are needed. This is the secure and recommended authentication method.

Answer

Enter your username and password in every query.

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Hardcode the service account key in the notebook.

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Use the 'gcloud auth login' command in every cell.

Question 5

Which component of Cloud Composer acts as the primary interface for managing and triggering DAGs manually?

Accepted Answer

Airflow Web Interface. The Airflow Web Interface (the Airflow UI) is the primary interface in Cloud Composer for viewing, triggering, and managing DAGs manually. It provides DAG views, task logs, graph view, and manual trigger buttons.

Answer

Cloud Storage Browser

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Cloud Shell

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BigQuery Console

Question 6

A financial analyst needs a Looker dashboard tile that shows month-over-month revenue growth as a percentage. The underlying BigQuery table stores completed transactions, and the analyst wants the tile to update without manual intervention each month. Which approach should the analyst use in Looker?

Accepted Answer

Build a table calculation using offset() to compare each month's revenue with the prior month and express the difference as a percentage.. Growth between consecutive periods requires comparing a row with the row before it, which is exactly what offset() provides inside a table calculation. Because the calculation is evaluated when the query runs, the tile reflects current data on every dashboard load with no rebuild schedule. Dividing the month-over-month difference by the prior month's revenue expresses the change as a percentage, matching the requested metric.

Answer

Define a custom dimension using a CASE statement that labels each month as higher or lower than the previous month.

Answer

Create a derived table in Looker's SQL editor that precomputes monthly growth for every historical month and schedule it to rebuild quarterly.

Answer

Add a measure using the percent_of_total type so Looker automatically expresses each month's revenue as a share of the yearly total.

Question 7

You need to run a Workflows execution based on a schedule. Which Google Cloud service is designed to manage this triggering?

Accepted Answer

Cloud Scheduler. Cloud Scheduler is Google Cloud's fully managed cron service, purpose-built to invoke HTTP endpoints, Pub/Sub topics, or App Engine routes on a defined schedule. Workflows exposes an HTTP trigger endpoint, so a Cloud Scheduler job can POST to that endpoint on a cron expression to start executions. This is the canonical, documented pattern for scheduled Workflows runs.

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Eventarc

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Cloud Tasks

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Pub/Sub

Question 8

You are using Dataflow with autoscaling. What metric determines whether the worker pool scales up or down?

Accepted Answer

Work backlog and throughput. Dataflow autoscaling is driven by the work backlog (unprocessed elements) and throughput relative to the current worker pool. The autoscaler compares the backlog and the rate of processing to decide whether to add or remove workers, targeting a steady-state where backlog stays low without over-provisioning. This is the documented signal for Streaming Engine and batch autoscaling.

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Network latency

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Memory usage

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User-defined triggers

Question 9

Your Cloud Composer DAG includes a task that runs a long-running data export, and you have observed that the task sometimes exceeds the default Airflow worker pod timeout, causing the task to be killed and retried. You want to increase the allowed execution time for this task without affecting other tasks. What should you do?

Accepted Answer

Set the execution_timeout parameter on the specific task in the DAG.. The execution_timeout parameter on an individual task sets the maximum runtime for that task, overriding any default. This is the correct way to extend time for a specific long-running task without altering global settings. Retries, schedule changes, and worker pod timeouts do not provide a targeted per-task timeout adjustment.

Answer

Add a retry_delay and increase retries in the task's default_args.

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Change the DAG's schedule_interval to run less frequently.

Answer

Increase the worker pod timeout in the Cloud Composer environment configuration.

Question 10

A data engineer must ingest change data capture streams from an on-premises PostgreSQL database into BigQuery with minimal latency and without writing custom extraction code. The source table has a primary key that updates frequently. Which Google Cloud service should be used to replicate these changes continuously?

Accepted Answer

Datastream with a PostgreSQL source and a BigQuery destination. Continuous, low-latency change data capture from PostgreSQL into BigQuery without custom code is exactly what Datastream provides, reading the database log and applying changes to BigQuery. Scheduled transfer jobs and timer-driven Dataflow templates operate in batches and miss intermediate changes, while federated queries read at query time rather than replicating, so none deliver the required streaming replication.

Answer

A scheduled BigQuery Data Transfer Service job with a PostgreSQL connector

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A Cloud SQL federated query from BigQuery against the on-premises PostgreSQL instance

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A Dataflow pipeline using the JDBC-to-BigQuery template run on a timer