GCP-ADP Practice Test 2 — 30 Questions

Question 1

You are using Vertex AI Workbench. You want to install a new Python library, `seaborn`, for visualization. What is the correct way to install it in the notebook instance?

Accepted Answer

!pip install seaborn. In a Vertex AI Workbench notebook (JupyterLab-based), the correct way to install a Python package is to run a shell command from a notebook cell using the `!` prefix, so `!pip install seaborn` executes pip in the notebook's kernel environment. This installs the library into the instance's Python environment so it can be imported in subsequent cells.

Answer

Use the Google Cloud Console library menu.

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import seaborn

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apt-get install seaborn

Question 2

You need to store sensitive data in GCS. Which feature ensures data is encrypted at rest?

Accepted Answer

Default Encryption. Google Cloud Storage automatically encrypts all data at rest using AES-256 encryption before it is written to disk, with no configuration required. This default encryption ensures that sensitive data is protected without any additional action from the user. While CMEK and Cloud DLP offer additional control or inspection, the fundamental guarantee of encryption at rest is provided by default encryption.

Answer

Identity and Access Management

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Cloud DLP

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Customer-Managed Encryption Keys (CMEK)

Question 3

You need to ensure that PII (Personally Identifiable Information) in BigQuery is automatically detected and tagged across your organization. Which Dataplex feature should you enable on your Lake?

Accepted Answer

Automatic data discovery. Automatic data discovery in Dataplex scans data in a Lake and automatically identifies and tags sensitive information such as PII using built-in or custom data classifiers. It profiles data assets and applies metadata tags, which is exactly what's needed to detect and tag PII across an organization.

Answer

Data quality task

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Lifecycle management policy

Answer

IAM policy inheritance

Question 4

You have a BigQuery table containing millions of rows of web server logs. You need to run a query that filters on a timestamp column and returns only the last 24 hours of data. The table is not partitioned. What should you do to improve query performance and reduce cost for this and future similar queries?

Accepted Answer

Create a partitioned table on the timestamp column and copy the data into it.. Partitioning a large table by a timestamp column enables partition pruning, so queries that filter on that column only scan relevant partitions. This reduces both cost and query time. Without partitioning, even with a WHERE clause, BigQuery scans the entire table. Clustering alone is less effective without partitioning, and materialized views are not ideal for dynamic time windows.

Answer

Create a clustered table on the timestamp column.

Answer

Add a WHERE clause that uses the timestamp column with a specific date range.

Answer

Create a materialized view that selects the last 24 hours of data and refresh it every hour.

Question 5

You need to perform data cleansing on a large dataset in Dataprep. Which execution engine should you choose for scalability?

Accepted Answer

Dataflow. Dataflow is the correct execution engine for Dataprep because Dataprep is built on top of Cloud Dataflow, which provides distributed, autoscaling processing for large datasets. Dataflow handles the parallel execution of Dataprep recipes across many workers, so cleansing operations scale horizontally without manual cluster management. Choosing Dataflow ensures the job can handle terabyte-scale data efficiently.

Answer

Local Desktop

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BigQuery

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Cloud Run

Question 6

You are a data engineer at a healthcare company. You need to create a Dataflow pipeline that reads from a Pub/Sub subscription and writes to BigQuery. The pipeline must handle late-arriving data and ensure exactly-once processing. You also need to minimize cost. Which of the following should you use?

Accepted Answer

Use Dataflow with Apache Beam's PubSubIO and BigQueryIO, enabling windowing and triggers for late data, and use BigQueryIO.Write with exactly-once semantics.. Dataflow with Apache Beam provides the necessary constructs for exactly-once processing and late data handling. PubSubIO and BigQueryIO support windowing and triggers, and BigQueryIO.Write can be configured for exactly-once semantics. Other options either lack these features or are not cost-effective for this use case. Therefore, using Dataflow with these configurations is the correct approach.

Answer

Use Dataflow with the Pub/Sub to BigQuery template and set the --discardUnknownFields option.

Answer

Use BigQuery's streaming ingestion with a Pub/Sub subscription directly.

Answer

Use Cloud Data Fusion with a Realtime pipeline that writes to BigQuery.

Question 7

A marketing analyst needs to run a one-time SQL query that joins a BigQuery table containing campaign performance data with a small CSV file stored in a Cloud Storage bucket. The CSV is updated weekly by an external partner. The analyst wants the query to always reflect the latest CSV without loading it into BigQuery each week. What should the analyst do?

Accepted Answer

Create a BigQuery external table over the CSV file in Cloud Storage and join it with the campaign table in the query.. A BigQuery external table over the Cloud Storage CSV lets queries read the file directly, so the join always sees the latest partner data without any load or refresh job. This is the standard pattern when small reference files change frequently and you want to avoid maintaining a native copy.

Answer

Export the campaign table to Cloud Storage, then run the join in Cloud Shell using a local SQL engine.

Answer

Import the CSV into BigQuery as a native table and set up a scheduled query to re-import it weekly.

Answer

Use the bq load command to append the CSV into a partitioned table each week.

Question 8

You are using Cloud Scheduler to trigger a Workflows execution every hour. The Workflows definition expects an argument 'projectId' to be passed at runtime. You need to ensure the argument is provided correctly. What should you include in the Cloud Scheduler job configuration?

Accepted Answer

In the 'Body' field, provide a JSON payload with the argument, e.g., {"projectId": "my-project"}.. When Cloud Scheduler triggers a Workflows execution, it sends an HTTP POST request to the Workflows API endpoint. Runtime arguments must be included in the request body as a JSON object. The 'Body' field in Cloud Scheduler allows you to specify this JSON payload, ensuring the workflow receives the 'projectId' argument. Other fields like URL, headers, or non-existent attributes do not serve this purpose.

Answer

In the 'URL' field, append the argument as a query parameter, e.g., ?projectId=my-project.

Answer

In the 'Attributes' field, define a key-value pair with 'projectId' as the key and 'my-project' as the value.

Answer

In the 'Headers' field, add a custom header like 'X-Project-Id: my-project'.

Question 9

You are configuring a Dataplex asset that points to a GCS bucket. You want to ensure that only files in a specific folder are included in the asset. How do you specify this?

Accepted Answer

Specify a path filter in the asset definition. In Dataplex, when defining a zone or asset that points to a Cloud Storage bucket, you can specify a path filter (a prefix or folder path) in the asset definition to limit discovery and inclusion to only files under that folder. This is the native mechanism for scoping a Dataplex asset to a subset of a bucket.

Answer

Define a SQL view on the folder

Answer

Use an IAM condition on the bucket

Answer

Move the files to a separate bucket

Question 10

You need to set up an alert when a GCS bucket exceeds a certain size. Which tool do you use?

Accepted Answer

Cloud Monitoring. Cloud Monitoring is the GCP service responsible for collecting metrics, creating alerting policies, and notifying on threshold breaches. GCS automatically exports storage metrics (such as storage.googleapis.com/storage/total_bytes) to Cloud Monitoring, so you can build an alerting policy on bucket size and route it to email, Pub/Sub, or PagerDuty. This is the canonical way to alert on resource metrics in GCP.

Answer

Cloud Build

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IAM

Answer

Cloud Logging

Question 11

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

Answer

Duplicate the taxonomy in each project

Answer

Use Cloud Resource Manager to link them

Question 12

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.

Answer

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 13

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.

Answer

In the Cloud Storage metadata

Answer

In the BigQuery table schema

Answer

In the Dataplex Asset definition

Question 14

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.

Answer

Hardcode the service account key in the notebook.

Answer

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

Question 15

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

Answer

Cloud Shell

Answer

BigQuery Console

Question 16

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 17

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.

Answer

Eventarc

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Cloud Tasks

Answer

Pub/Sub

Question 18

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.

Answer

Network latency

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Memory usage

Answer

User-defined triggers

Question 19

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.

Answer

Change the DAG's schedule_interval to run less frequently.

Answer

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

Question 20

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

Answer

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

Answer

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