GCP-ADP Exam Questions and Answers

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

Default Encryption

Google Cloud Storage automatically encrypts every object at rest using AES-256 before writing to disk, with no configuration required. This default server-side encryption satisfies the stem's requirement to protect sensitive data at rest, applying uniformly across all storage classes and regions without additional keys or setup from you.

Identity and Access Management

Cloud DLP

Customer-Managed Encryption Keys (CMEK)

Why: 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.

All Data Analysis And Presentation questions

Which GCS storage class is most cost-effective for data accessed only once per year?

Standard

Coldline

Domain 2: Data Analysis And Presentation

You are building a Looker dashboard and need to calculate the year-over-year growth rate. Which feature should you use to perform this without modifying the underlying SQL?

BigQuery query parameters.

Looker Table Calculations.

Table Calculations compute values in Looker's query layer using LookML functions such as offset and pivot_offset, deriving year-over-year growth from returned rows. This satisfies the stem's constraint of calculating growth without modifying the underlying SQL.

LookML derived tables with persistent caching.

Database-level User Defined Functions (UDFs).

Why: Looker Table Calculations allow you to create calculations (such as year-over-year growth) directly in the Explore or dashboard using Looker's expression language, without modifying the underlying SQL or LookML. They operate on the query results already returned, so you can compute percent change, offsets, and running totals on the fly. This makes them ideal for ad-hoc metrics like YoY growth.

You are analyzing query performance in BigQuery. You notice that your queries are spilling to disk. What is the most likely cause?

The project is using On-Demand pricing.

The dataset is stored in a multi-region location.

Using too many slots in the reservation.

An inefficient JOIN or GROUP BY operation causing high memory pressure.

BigQuery spills to disk when a query's intermediate data exceeds available memory per slot. Inefficient JOINs or GROUP BY operations producing large shuffles are the typical cause, forcing temporary data onto disk and slowing execution.

Why: Spilling to disk in BigQuery occurs when a query's intermediate results exceed the memory available to the execution stage. This typically happens due to inefficient JOINs or GROUP BY operations that produce large intermediate datasets, forcing BigQuery to write data to disk (shuffle) and significantly slowing performance. The correct answer directly identifies this memory pressure as the root cause.

You are identifying trends in a large dataset using BigQuery. Which SQL function is best suited to identify the top 5 products sold per region?

GROUP BY region, product.

SELECT TOP 5 * FROM sales.

RANK() OVER (PARTITION BY region ORDER BY sales DESC).

RANK() OVER (PARTITION BY region ORDER BY sales DESC) assigns a rank within each region independently, letting you filter to ranks 1-5 per region. The PARTITION BY region clause is the mechanism that satisfies the per-region top-5 requirement.

SUM(sales) OVER (ORDER BY region).

Why: RANK() OVER (PARTITION BY region ORDER BY sales DESC) assigns a rank to each product within each region based on sales, so filtering for rank <= 5 yields the top 5 products per region. This is the standard SQL window-function pattern for per-group top-N queries.

A marketing manager needs to see daily sales trends in Looker. The underlying data is in BigQuery and is updated every 6 hours. What is the best way to visualize this?

Create a static PDF report sent via email.

Export BigQuery data to a CSV file.

Build a dashboard in Cloud Monitoring.

Create a Looker Explore and build a time-series line chart.

A Looker Explore queries BigQuery directly, so the line chart always reflects the latest data without extraction or scheduling. Time-series line charts suit daily sales trends, and Looker's persistent derived tables or caching handle the six-hourly refresh cadence.

Why: Creating a Looker Explore and building a time-series line chart is the best approach because Looker connects directly to BigQuery, supports scheduled/real-time data refresh, and provides interactive visualization tailored to trend analysis. A time-series line chart is the appropriate visualization for daily sales trends over time, and Looker's Explore lets the marketing manager self-serve without manual data exports.

You are using Vertex AI Workbench and want to ensure your notebook environment persists after the instance is stopped. Where should you save your work?

In the root directory of the container image.

In a persistent disk attached to the Workbench instance.

A persistent disk attached to the Workbench instance stores data independently of the instance's running state, so notebooks survive stopping and restarting. Saving to the boot disk or instance memory would be lost, whereas the attached persistent disk satisfies the durability requirement.

In the local /tmp folder.

In the system memory.

Why: Vertex AI Workbench instances use a persistent disk as the boot disk and for user data, so files saved outside the container image persist across stop/start cycles. Saving work in the persistent disk ensures notebooks, datasets, and environment customizations survive instance shutdown. The root directory of the container image and /tmp are ephemeral and are lost when the instance stops.

You need to analyze a petabyte-scale dataset in BigQuery. To optimize cost, you must ensure that users only query relevant partitions. How should you enforce this?

Enable the 'Require partition filter' option in the table settings.

Enabling 'Require partition filter' forces every query against the table to include a partition filter, rejecting unfiltered scans. This prevents full-table scans across the petabyte dataset, directly enforcing the cost-optimisation constraint by limiting bytes read.

Use a materialized view for all queries.

Apply a data masking policy to the partitioning column.

Set a project-level query quota.

Why: BigQuery's 'Require partition filter' table option forces every query against the table to include a WHERE clause that filters on the partitioning column, preventing full-table scans and the associated on-demand costs. This is the native, enforced mechanism for ensuring users only query relevant partitions. It applies to partitioned tables and rejects queries that lack a partition predicate.

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All Data Pipeline Orchestration questions

Domain 3: Data Pipeline Orchestration

You are orchestrating a pipeline with Workflows that invokes several Cloud Functions. You need to pass the output of one function as an argument to the next. What is the correct syntax?

Use global environment variables to store intermediate state.

Assign the call result to a variable and reference it in the next step.

Workflows steps reference prior outputs through variables assigned from call results, so storing the function's return value and passing it as an argument in the subsequent step satisfies the requirement to chain outputs between functions.

Combine all function calls into a single synchronous block.

Write the output to a temporary GCS file and read it back.

Why: Workflows uses a YAML/JSON syntax where each step's return value can be assigned to a variable, and subsequent steps reference that variable using ${variableName} syntax. This is the idiomatic way to chain Cloud Functions and pass outputs forward. For example: 'call: http.post' with 'result: fn1Result', then the next step uses '${fn1Result.body}'.

In a Cloud Composer DAG, you need to trigger a BigQuery job that runs as a service account other than the one assigned to the GKE nodes. How should you implement this?

Hardcode the credentials in a JSON file inside the DAG folder.

Use the 'impersonation_chain' parameter in the BigQueryInsertJobOperator.

The impersonation_chain parameter makes the operator obtain short-lived credentials for a specified service account, so the BigQuery job runs under that identity rather than the node service account. This satisfies the stem's requirement to use a different service account.

Change the worker node's identity in the Cloud Console.

Configure a connection with the 'Service Account Key' type in Airflow UI.

Why: The 'impersonation_chain' parameter is the native Airflow mechanism for a task to assume a different service account identity at runtime. When set on BigQueryInsertJobOperator, the operator calls the IAM Credentials API to generate short-lived access tokens for the target service account, so the BigQuery job executes under that SA's permissions rather than the Composer worker SA. This preserves least privilege without storing any long-lived secrets.

When configuring a Workflows execution, you want to ensure the workflow can be triggered by an HTTP POST request. Which feature allows this?

Executing the workflow via the REST API.

Invoking the workflow through the REST API satisfies the HTTP POST trigger requirement, since Workflows exposes an execution endpoint that accepts POST requests carrying the workflow ID and runtime arguments. This lets external callers start executions directly over HTTP, matching the stem's constraint without needing an Eventarc or Pub/Sub intermediary.

Configuring a Cloud Scheduler job with an HTTP target.

Enabling the 'HTTP Gateway' on the workflow itself.

Creating a Service Account with 'Public Access'.

Why: Workflows executions can be triggered by an HTTP POST request using the Workflows REST API (executions.create endpoint). This allows external systems to start a workflow by sending an authenticated POST to the Workflows API with the workflow ID and input arguments.

Your organization uses Workflows to orchestrate multi-step API calls. You need to handle transient failures in a specific HTTP request by retrying it up to 5 times with exponential backoff. Which block should you use?

A 'loop' block with a counter increment.

An 'if' statement checking for 500 status codes.

A global 'exception' handler at the end of the workflow.

A 'try' block with a 'retry' policy.

Wrapping the HTTP call in a try block with a retry policy lets Workflows re-execute that step on transient errors, applying exponential backoff automatically. This satisfies the requirement of up to 5 retries on the specific request without affecting other steps in the orchestration.

Why: Workflows provides a try/retry block where you can specify a retry policy with max_retries and a backoff (initial delay, multiplier, max delay), which is exactly the mechanism for retrying a transient HTTP failure up to 5 times with exponential backoff. The retry policy is attached to the try block and applies to the steps inside it.

A Cloud Composer DAG is failing because the Airflow database is locking up during high-concurrency periods. What is the most effective way to address this?

Delete all past DAG run logs.

Increase the number of DAGs in the repository.

Scale up the Cloud Composer environment size to increase database resources.

Scaling the environment size increases the underlying Cloud SQL database tier, raising IOPS and connection limits so row-level locks clear faster under high concurrency. This directly addresses the database locking constraint, since Composer's metadata database scales with environment size rather than worker count.

Set the 'parallelism' parameter to a very high value.

Why: Cloud Composer's Airflow metadata database (backed by Cloud SQL) is the bottleneck during high-concurrency DAG runs because every task instance, DAG run, and log entry writes to it. Scaling up the Composer environment size increases the Cloud SQL tier (CPU, memory, IOPS), which directly relieves database lock contention and connection saturation. This is the recommended remediation when the scheduler/DB cannot keep up with task state updates.

Which Cloud Composer metric should you monitor to identify if your Airflow scheduler is struggling to parse your DAGs in a timely manner?

airflow_task_execution_time

dag_run_success_count

dag_processing_total_parse_time

Monitoring `dag_processing_total_parse_time` directly exposes cumulative seconds Airflow spends parsing DAG files, so rising values reveal the scheduler falling behind on parsing. This satisfies the stem's constraint of identifying timely DAG parsing, unlike task-execution or queue metrics that ignore the parsing bottleneck entirely.

scheduler_heartbeat

Why: The dag_processing_total_parse_time metric measures the cumulative time Airflow spends parsing DAG files, which directly indicates whether the scheduler is struggling to keep up with DAG parsing. A rising or consistently high value signals that DAGs are too complex, too numerous, or inefficiently written, delaying task scheduling. This is the canonical Cloud Composer metric for diagnosing scheduler parse bottlenecks.

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All Data Management questions

Domain 4: Data Management

You have sensitive data in Cloud Storage that needs to be deleted after 30 days to comply with regulatory requirements. What is the most efficient way to manage this?

Use Cloud Storage lifecycle management policies

Cloud Storage lifecycle management policies automatically delete objects once they reach the specified age, satisfying the 30-day regulatory retention limit without custom code or manual intervention. Rules apply at bucket level across matching objects, making this the most efficient, serverless mechanism for enforcing time-based deletion at scale.

Run a manual BigQuery script

Write a Cloud Function to check dates and delete files

Create a Dataplex lifecycle task

Why: Cloud Storage Object Lifecycle Management lets you define rules directly on a bucket (e.g., Delete objects 30 days after creation) that GCS enforces automatically at no additional cost. This is the native, server-side, most efficient mechanism for time-based deletion and requires no code, scheduler, or external service.

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?

Automatic data discovery

Automatic data discovery profiles and scans data in the lake, using its built-in classifiers to detect sensitive information such as PII and attach corresponding tags automatically. This satisfies the stem's requirement for organisation-wide automatic detection and tagging without manual intervention.

Data quality task

Lifecycle management policy

IAM policy inheritance

Why: 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.

Which service should you use to search for and discover datasets across your entire Google Cloud organization?

BigQuery Explorer

Cloud Storage Browser

Cloud Logging

Data Catalog

Data Catalog provides a searchable, organisation-wide inventory of datasets and tables, letting users discover and tag assets across all projects. It indexes metadata from BigQuery and other sources, satisfying the requirement to search the entire Google Cloud organisation.

Why: Data Catalog is Google Cloud's fully managed, scalable metadata management service that enables search and discovery of datasets across the organization. It provides a unified inventory of data assets with tagging and search capabilities, making it the correct choice for discovering datasets organization-wide.

You need to control access to specific columns in a BigQuery table using Dataplex-integrated policy tags. What must you create before you can apply a policy tag to a column?

Data Zone

IAM Condition

BigQuery Dataset permission

Policy Tag Taxonomy

A policy tag taxonomy is the prerequisite container that must exist before any policy tag can be created or applied. Dataplex requires tags to be organised within a taxonomy, so the column-level access control cannot be enforced until that hierarchy is defined in the same region as the BigQuery data.

Why: Policy tags in BigQuery are organized within a Policy Tag Taxonomy, which is a hierarchical container created in Data Catalog (now Dataplex Universal Catalog). Before any policy tag can be applied to a column, you must first create a taxonomy to hold the tags, then create the individual policy tags inside it, and finally grant the Fine-Grained Reader role to users. The taxonomy is the mandatory parent resource — you cannot create a standalone policy tag without it.

What is the purpose of a 'Data Zone' in Dataplex?

To execute Spark jobs

To store query results

To act as a physical storage location

To provide a logical grouping for governance and policies

A Data Zone groups assets within a lake so policies, IAM and metadata rules apply consistently across them. This satisfies the stem's governance purpose, providing the logical boundary where Dataplex enforces access and policy controls.

Why: A Data Zone in Dataplex is a logical construct that groups data assets (such as Cloud Storage buckets, BigQuery datasets, and BigLake tables) so that a single set of governance policies, discovery, and access controls can be applied across them. It does not provision or represent physical storage; it is an organizational boundary that maps to underlying resources. This lets data stewards manage metadata, quality, and security consistently without moving or duplicating data.

What happens if you delete a Dataplex Lake?

The data is moved to a 'trash' bucket

The lake is deleted, but the underlying assets remain

Deleting a Dataplex lake removes only the lake resource and its metadata configuration; the underlying Cloud Storage or BigQuery assets themselves are untouched. This satisfies the stem's scenario, since the assets remain accessible directly after the lake is gone.

Access to the assets is granted to all users

All underlying data assets are deleted

Why: Deleting a Dataplex Lake removes the lake resource itself but does not delete the underlying data assets (such as Cloud Storage buckets or BigQuery datasets) that were attached to it. The assets remain intact and can be re-attached to another lake or managed separately. This design prevents accidental data loss and allows for flexible re-organization of data governance structures.

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Frequently asked questions

How many questions are on the GCP-ADP exam?

The vendor does not publish an exact question count for the GCP-ADP; the exam lasts 120 minutes. Always confirm current details on the vendor's official exam page.

What types of questions appear on the GCP-ADP exam?

Scenario-based questions covering exam objectives with detailed answer explanations.

How are GCP-ADP questions organised by domain?

The exam covers 4 domains: Data Preparation And Ingestion, Data Analysis And Presentation, Data Pipeline Orchestration, Data Management. Questions are weighted by domain — higher-weight domains appear more on your actual exam.

Are these the actual GCP-ADP exam questions?

No. These are original exam-style practice questions written against the official Google Cloud GCP-ADP exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.

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GCP-ADP Exam Questions and Answers

24 real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.

120 min time limit

4 exam domains

Overview Domain Blueprint Study Guide All QuestionsSample by Domain

1. Data Preparation And Ingestion 2. Data Analysis And Presentation 3. Data Pipeline Orchestration 4. Data Management

Domain 1: Data Preparation And Ingestion

All Data Preparation And Ingestion questions

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

Default Encryption

Identity and Access Management

Cloud DLP

Customer-Managed Encryption Keys (CMEK)

All Data Analysis And Presentation questions

Which GCS storage class is most cost-effective for data accessed only once per year?

Standard

Coldline

Domain 2: Data Analysis And Presentation

You are building a Looker dashboard and need to calculate the year-over-year growth rate. Which feature should you use to perform this without modifying the underlying SQL?

BigQuery query parameters.

Looker Table Calculations.

LookML derived tables with persistent caching.

Database-level User Defined Functions (UDFs).

You are analyzing query performance in BigQuery. You notice that your queries are spilling to disk. What is the most likely cause?

The project is using On-Demand pricing.

The dataset is stored in a multi-region location.

Using too many slots in the reservation.

An inefficient JOIN or GROUP BY operation causing high memory pressure.

You are identifying trends in a large dataset using BigQuery. Which SQL function is best suited to identify the top 5 products sold per region?

GROUP BY region, product.

SELECT TOP 5 * FROM sales.

RANK() OVER (PARTITION BY region ORDER BY sales DESC).

SUM(sales) OVER (ORDER BY region).

A marketing manager needs to see daily sales trends in Looker. The underlying data is in BigQuery and is updated every 6 hours. What is the best way to visualize this?

Create a static PDF report sent via email.

Export BigQuery data to a CSV file.

Build a dashboard in Cloud Monitoring.

Create a Looker Explore and build a time-series line chart.

You are using Vertex AI Workbench and want to ensure your notebook environment persists after the instance is stopped. Where should you save your work?

In the root directory of the container image.

In a persistent disk attached to the Workbench instance.

In the local /tmp folder.

In the system memory.

You need to analyze a petabyte-scale dataset in BigQuery. To optimize cost, you must ensure that users only query relevant partitions. How should you enforce this?

Enable the 'Require partition filter' option in the table settings.

Use a materialized view for all queries.

Apply a data masking policy to the partitioning column.

Set a project-level query quota.

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All Data Pipeline Orchestration questions

Domain 3: Data Pipeline Orchestration

You are orchestrating a pipeline with Workflows that invokes several Cloud Functions. You need to pass the output of one function as an argument to the next. What is the correct syntax?

Use global environment variables to store intermediate state.

Assign the call result to a variable and reference it in the next step.

Combine all function calls into a single synchronous block.

Write the output to a temporary GCS file and read it back.

In a Cloud Composer DAG, you need to trigger a BigQuery job that runs as a service account other than the one assigned to the GKE nodes. How should you implement this?

Hardcode the credentials in a JSON file inside the DAG folder.

Use the 'impersonation_chain' parameter in the BigQueryInsertJobOperator.

Change the worker node's identity in the Cloud Console.

Configure a connection with the 'Service Account Key' type in Airflow UI.

When configuring a Workflows execution, you want to ensure the workflow can be triggered by an HTTP POST request. Which feature allows this?

Executing the workflow via the REST API.

Configuring a Cloud Scheduler job with an HTTP target.

Enabling the 'HTTP Gateway' on the workflow itself.

Creating a Service Account with 'Public Access'.

A 'loop' block with a counter increment.

An 'if' statement checking for 500 status codes.

A global 'exception' handler at the end of the workflow.

A 'try' block with a 'retry' policy.

A Cloud Composer DAG is failing because the Airflow database is locking up during high-concurrency periods. What is the most effective way to address this?

Delete all past DAG run logs.

Increase the number of DAGs in the repository.

Scale up the Cloud Composer environment size to increase database resources.

Set the 'parallelism' parameter to a very high value.

Which Cloud Composer metric should you monitor to identify if your Airflow scheduler is struggling to parse your DAGs in a timely manner?

airflow_task_execution_time

dag_run_success_count

dag_processing_total_parse_time

scheduler_heartbeat

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All Data Management questions

Domain 4: Data Management

You have sensitive data in Cloud Storage that needs to be deleted after 30 days to comply with regulatory requirements. What is the most efficient way to manage this?

Use Cloud Storage lifecycle management policies

Run a manual BigQuery script

Write a Cloud Function to check dates and delete files

Create a Dataplex lifecycle task

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?

Automatic data discovery

Data quality task

Lifecycle management policy

IAM policy inheritance

Which service should you use to search for and discover datasets across your entire Google Cloud organization?

BigQuery Explorer

Cloud Storage Browser

Cloud Logging

Data Catalog

You need to control access to specific columns in a BigQuery table using Dataplex-integrated policy tags. What must you create before you can apply a policy tag to a column?

Data Zone

IAM Condition

BigQuery Dataset permission

Policy Tag Taxonomy

What is the purpose of a 'Data Zone' in Dataplex?

To execute Spark jobs

To store query results

To act as a physical storage location

To provide a logical grouping for governance and policies

What happens if you delete a Dataplex Lake?

The data is moved to a 'trash' bucket

The lake is deleted, but the underlying assets remain

Access to the assets is granted to all users

All underlying data assets are deleted

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