GCP-ADP Practice Test 8 — 10 Questions

Question 1

You are using Dataflow with a custom container. Which command do you use to specify the container image?

Accepted Answer

--sdk_container_image. When running an Apache Beam pipeline on Dataflow with a custom container, the pipeline option that specifies the container image is --sdk_container_image. This tells Dataflow to pull the specified image from Artifact Registry or Container Registry and use it as the SDK harness environment for the workers. The other flags do not exist in the Dataflow pipeline options.

Answer

--container_path

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--custom_image

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--worker_image

Question 2

A healthcare organization needs to analyze sensitive patient data in BigQuery while ensuring that analysts can see aggregated results but cannot view individual patient identifiers. Which Google Cloud feature should they implement?

Accepted Answer

Column-level security with policy tags. Column-level security with policy tags allows classifying sensitive columns and granting access only to authorized users. Analysts without the policy tag role can query other columns and compute aggregates but cannot see patient identifiers. Other options either restrict rows, use less scalable view patterns, or address network boundaries rather than column visibility.

Answer

Row-level security with filter predicates

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BigQuery authorized views

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VPC Service Controls perimeter around BigQuery

Question 3

You are creating a Looker Studio report that must allow users to interactively explore sales data. The report should include controls that let users filter data by product category and date range. Which two controls should you add? (Choose two.)

Accepted Answer

A date range control.. A filter control on the product category dimension and a date range control are the standard Looker Studio controls for interactive filtering by category and time. They apply to all charts on the page, enabling users to dynamically explore sales data. The other options either do not provide filtering or are not interactive.

Answer

A parameter control to adjust the date granularity.

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A chart-level filter on each chart for product category.

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A data control to switch between different data sources.

Question 4

Which tool is used to monitor the performance of your Dataflow pipelines?

Accepted Answer

Cloud Monitoring. Cloud Monitoring is the GCP service that collects and visualizes operational metrics from Dataflow pipelines, including system lag, throughput, worker CPU/memory, and autoscaling behavior. Dataflow natively publishes these metrics to Cloud Monitoring, where you can build dashboards and alerting policies. Cloud Logging captures log entries (errors, debug output) rather than performance metrics, so it is not the monitoring tool.

Answer

Cloud Logging

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BigQuery Console

Answer

Dataprep

Question 5

When designing a Looker dashboard, you need to display a value that is the average of a specific metric across the entire dashboard. What is the best method?

Accepted Answer

Single Value visualization.. A Single Value visualization in Looker is designed to display one aggregated number prominently, such as an average, sum, or count, and it can compute that value across the entire dashboard's underlying query. It is the standard tile for KPI-style metrics like an overall average.

Answer

Treemap.

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Data table.

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Waterfall chart.

Question 6

In Cloud Composer, what is the default behavior if a DAG execution takes longer than the 'dagrun_timeout' setting?

Accepted Answer

The DAG run is marked as failed.. In Cloud Composer (Apache Airflow), if a DAG run exceeds the dagrun_timeout setting, the scheduler marks the DAG run as failed. This prevents stuck runs from consuming resources indefinitely and allows alerting or retry logic to kick in.

Answer

The DAG enters a 'running' state indefinitely.

Answer

The DAG run is paused.

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The scheduler automatically restarts the DAG.

Question 7

You want to share a BigQuery dataset with a partner who uses a different Google Cloud project. What is the most secure way to grant them access?

Accepted Answer

Grant the partner's service account the 'BigQuery Data Viewer' role on the dataset.. Granting the partner's service account the BigQuery Data Viewer role on the dataset is the most secure approach because it follows the principle of least privilege: the partner can read data in that specific dataset only, using their own identity, with no credential sharing. IAM roles can be granted at the dataset level, scoping access precisely and allowing easy revocation.

Answer

Make the dataset public.

Answer

Provide the partner with your project's service account key.

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Export the data to a public Cloud Storage bucket.

Question 8

A data team is building a BigQuery ML model to predict customer churn directly from a BigQuery table. They want to evaluate the model's quality and understand which input features influenced its predictions. Which two BigQuery ML capabilities should they use to accomplish these goals? (Choose two.)

Accepted Answer

Use ML.EXPLAIN_PREDICT to return per-row feature attributions alongside each prediction.. ML.EVALUATE measures the churn model's predictive quality with classification metrics on held-out data, while ML.EXPLAIN_PREDICT returns per-row feature attributions that reveal which inputs drove each prediction. Together they cover both evaluation and interpretability. The other functions describe training data, training progress, or generate embeddings, none of which answers how good the model is or why it predicts churn.

Answer

Use ML.TRAINING_INFO to inspect the iteration history of the model's training run.

Answer

Use ML.GENERATE_EMBEDDING to convert customer records into vector embeddings for similarity search.

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Use ML.FEATURE_INFO to list the summary statistics of each feature column used during training.

Question 9

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?

Accepted Answer

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

Answer

GROUP BY region, product.

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SELECT TOP 5 * FROM sales.

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SUM(sales) OVER (ORDER BY region).

Question 10

You manage a BigQuery dataset that contains a table with 500 TB of historical order data. Analysts frequently run queries that filter on the `order_date` column. Query costs are rising because every query scans the entire table. You want to reduce the bytes scanned and cost without changing the table name or existing SQL. What should you do?

Accepted Answer

Partition the table by the `order_date` column so queries filtering on that column scan only relevant partitions.. Partitioning a table by a column that queries commonly filter on lets BigQuery prune partitions and read only the data needed, which reduces bytes scanned and therefore cost. Because partitioning does not change the table name or require SQL changes, it satisfies all the constraints. The other choices either require SQL rewrites, do not affect scan volume, or block queries instead of optimizing them.

Answer

Set a custom query quota on the project so analysts cannot run queries that scan more than 1 TB.

Answer

Enable the BigQuery BI Engine reservation on the dataset to cache query results and lower cost.

Answer

Create a materialized view that pre-aggregates orders by date and instruct analysts to query the view instead.