Professional Data Engineer Free Practice Questions
This is the free Google Professional Data Engineer practice question bank —
170 of 328 total questions, each with a full explanation, free to
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Question 1
You need to load a dataset with multiple terabytes of clickstream data into BigQuery. The data arrives each day as compressed JSON files in a Cloud Storage bucket. You need a low-cost, programmatic, and scalable solution to load the data into BigQuery. What should you do?
A. Create an external table in BigQuery pointing to the Cloud Storage bucket and run the INSERT INTO ... SELECT * FROM external_table command.
B. Use the BigQuery Data Transfer Service from Cloud Storage.
C. Create a Cloud Run function to run a Python script to read and parse each JSON file, and use the BigQuery streaming insert API.
D. Use Cloud Data Fusion to create a pipeline to load the JSON files into BigQuery.
Show Answer
Correct Answer: B
Explanation: The requirement is a low-cost, programmatic, and scalable way to load daily JSON files from Cloud Storage into BigQuery. The BigQuery Data Transfer Service for Cloud Storage is designed for this use case: it automatically loads files from a bucket into BigQuery using batch load jobs, which are free (you only pay for BigQuery storage and queries). Option A requires querying external tables and inserting data, which incurs query costs on multi-terabyte data. Option C uses streaming inserts, which are expensive and unnecessary for batch data. Option D (Cloud Data Fusion) adds operational overhead and cost, making it unsuitable for a low-cost solution.
Question 1
You monitor and optimize the BigQuery instance for your team. You notice that a particular daily report that uses a large JOIN operation is consistently slow. You want to examine the query's execution plan to identify potential performance bottlenecks within the JOIN as quickly as possible. What should you do?
A. Use the bg query --dry_run command to review the estimated number of bytes read and review query syntax.
B. Run a query on the INFORMATION_SCHEMA.JOBS_BY_PROJECT view filtering by the job_ID and analyze total_bytes_processed.
C. Review the BigQuery audit logs in Cloud Logging.
D. Leverage BigQuery's Query History view and analyze the execution graph.
Show Answer
Correct Answer: D
Explanation: To quickly identify performance bottlenecks inside a large JOIN, you need visibility into how BigQuery actually executed the query. The Query History execution graph shows each stage of the query plan, including JOIN stages, data shuffling, skew, and slot usage, which directly highlights where time is being spent. The other options only show estimates, aggregate metrics, or logs and do not provide detailed, stage-level execution insight.
Question 2
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?
A. Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.
B. Use dynamic data masking and revoke viewer permissions when employees leave the organization.
C. Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
D. Use AEAD functions and delete keys when employees leave the organization.
Show Answer
Correct Answer: D
Explanation: The requirement is to make sensitive data values unreadable by design when access should no longer be possible. Using BigQuery AEAD encryption functions allows you to encrypt sensitive columns with a key; deleting or revoking access to that key renders the data cryptographically unreadable, even if someone still had query access. Column-level access controls or dynamic data masking only control visibility, not the underlying data. CMEK applies at the dataset/table level and is overly broad and operationally complex for this use case.
Question 2
Your company stores vital operational sales data in a BigQuery dataset in us-central1. Your company requires a disaster recovery plan to restore this data to us-east1 with a recovery point objective (RPO) of 24 hours and a recovery time objective (RTO) of 4 hours if us-central1 experiences an outage. You need to implement the disaster recovery plan while keeping costs and complexity to a minimum. What should you do?
A. Set up continuous queries and Pub/Sub to stream data changes from BigQuery tables in us-central1 to us-east1.
B. Manually export data to a CSV file in a multi-regional Cloud Storage bucket daily and use bq load to restore to us-east1.
C. Configure BigQuery cross-region dataset replication from ns-central1 to us-east1.
D. Take daily BigQuery table snapshots in us-central1.
Show Answer
Correct Answer: C
Explanation: BigQuery managed cross-region dataset replication is designed for disaster recovery with minimal operational overhead. It asynchronously replicates data from us-central1 to us-east1, meeting an RPO of 24 hours or less and providing a much lower RTO than manual restore approaches. The other options are either operationally complex, slower to restore, or do not provide cross-region recovery.
Question 3
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?
A. Disable external IP addresses from worker VMs and enable Private Google Access.
B. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
C. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
D. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
Show Answer
Correct Answer: A
Explanation: Dataflow workers only need to access Google-managed services such as Cloud Storage and BigQuery. Disabling external IPs improves security and reduces exposure, while enabling Private Google Access allows the workers to reach Google APIs over Google’s internal network without internet egress. This is the most cost-effective and simplest secure setup compared to Cloud NAT or Private Service Connect, which add unnecessary complexity for this use case.
Question 3
You are using Dataflow to build a streaming data pipeline to analyze user website click activity from Pub/Sub. You need to calculate the number of clicks for each user site visit. A site visit is defined as a period of activity followed by 30 minutes of inactivity for a specific user. What should you do?
A. Use tumbling windows with a 30-minute window.
B. Use hopping windows with a 30-minute window, and a 1-minute period.
C. Use hopping windows with a 30-minute window, and a 30-minute period.
D. Use session windows with a 30-minute gap duration.
Show Answer
Correct Answer: D
Explanation: The requirement is to count clicks per user site visit, where a visit is defined by activity separated by 30 minutes of inactivity. This is exactly the use case for session windows in Dataflow/Apache Beam, which group events into sessions based on a configurable inactivity gap. Tumbling or hopping windows are fixed-time windows and cannot model inactivity-based session boundaries.
Question 4
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?
A. Build authorized views on top of the sales table to aggregate data at the day and month level.
B. Enable BI Engine and add your sales table as a preferred table.
C. Build materialized views on top of the sales table to aggregate data at the day and month level.
D. Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.
Show Answer
Correct Answer: C
Explanation: Materialized views precompute and store the day- and month-level aggregations over the 50 TB fact table. This dramatically reduces data scanned per query, lowering on-demand costs, and improves response time since results are incrementally maintained by BigQuery with minimal maintenance overhead. Authorized views do not reduce scan costs, BI Engine may help latency but not consistently reduce scan costs for large aggregations, and scheduled aggregate tables require more maintenance.
Question 4
You are defining the data governance strategy for a new BigQuery table with medical and financial data. You want a scalable solution that ensures the clinical researchers can access patient medical data without financial information, while allowing the accounting team to access only financial data with minimal patient identifiers. What should you do?
A. Implement column-level security policies in BigQuery tables with IAM permissions.
B. Create separate tables for personally identifiable information (PII), financial data, and anonymized medical data. Use IAM permissions to control access to each table.
C. Implement row-level security policies in BigQuery tables with IAM permissions.
D. Create separate datasets with authorized views exposing only approved data.
Show Answer
Correct Answer: A
Explanation: Column-level security with policy tags in BigQuery is designed for this exact use case: selectively exposing columns based on data sensitivity. It scales well without duplicating data, allows clinical researchers to access medical columns while excluding financial data, and allows accounting to access financial columns with only minimal identifiers. Row-level security is not sufficient, and separate tables or authorized views add unnecessary complexity and duplication.
Question 5
You are using Workflows to call an API that returns a 1KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?
A. Create a Cloud Composer environment and run the logic in Cloud Composer.
B. Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.
C. Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.
D. Invoke a subworkflow in Workflows to apply the logic on your JSON file.
Show Answer
Correct Answer: C
Explanation: You need to run custom Python logic that is not supported by the Workflows standard library, while keeping the solution simple and fast. Invoking a Cloud Function lets you execute arbitrary Python code using the standard library with minimal operational overhead and low latency. It integrates cleanly with Workflows for synchronous execution, is well suited for small payloads (1KB JSON), and avoids the complexity and startup cost of Cloud Composer or Dataproc. Subworkflows would not help because they still rely on Workflows' limited built-in capabilities.
Question 5
You are designing a data lake on Google Cloud to store vast amounts of customer interaction data from various sources, such as websites, mobile apps, and social media. You need to ensure that this data, which arrives in different formats, is consistently cataloged and easy for data analysts to discover and use. You also want to perform basic data quality checks and transformations before the data is consumed by downstream applications. You need an automated and managed data governance solution. What should you do?
A. Use Cloud Storage as the central repository. Use Vertex AI to classify and process the data and perform data quality checks.
B. Stream all the data directly into BigQuery, where it is automatically cataloged and governed.
C. Use Cloud Storage and BigQuery as repositories. Use Dataplex Universal Catalog for metadata discovery, data quality checks, and transformations.
D. Use Cloud Storage as the central repository. Use a Cloud Run function to catalog, transform the data, and perform data quality checks.
Show Answer
Correct Answer: C
Explanation: Dataplex provides a fully managed data governance and data fabric layer across Cloud Storage and BigQuery, offering automated metadata discovery (Universal Catalog), data quality checks, and managed transformations. This fits the need for consistent cataloging, discoverability, basic data quality, and governance across heterogeneous data sources in a data lake.
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