Google Cloud Professional Data Engineer Practice Exam
The Google Cloud Professional Data Engineer Practice Exam offered by Exam Sage is a comprehensive, expertly crafted preparation tool designed to help you succeed in the official Google Cloud Professional Data Engineer certification exam. This practice exam closely mirrors the actual test format, difficulty, and coverage, providing you with realistic questions, detailed answers, and in-depth explanations. Whether you’re new to Google Cloud Platform (GCP) or aiming to sharpen your skills, this exam will boost your confidence and readiness.
What will you learn?
By taking this practice exam, you will deepen your understanding of the core responsibilities of a professional data engineer on Google Cloud. You’ll learn how to design, build, operationalize, secure, and monitor data processing systems. The exam questions reinforce key concepts such as data ingestion, storage, transformation, machine learning integration, data pipeline automation, and real-time analytics — all essential skills for data engineers working with GCP.
Covered Topics
Designing data processing systems on Google Cloud
Building and operationalizing data pipelines using Cloud Dataflow, Pub/Sub, and BigQuery
Managing and optimizing data storage in Cloud Storage, Bigtable, and BigQuery
Implementing data security and compliance best practices with IAM and encryption
Applying machine learning models with BigQuery ML and AI Platform
Automating workflows using Cloud Composer and Cloud Functions
Monitoring and troubleshooting data processing workflows using Cloud Monitoring and Logging
Ensuring data quality and consistency in streaming and batch pipelines
Why choose Exam Sage for this exam?
Exam Sage is a trusted platform specializing in high-quality, up-to-date exam practice tests. Our Google Cloud Professional Data Engineer Practice Exam is meticulously researched and written by domain experts to reflect the latest exam objectives and trends. We prioritize detailed explanations for every question, helping you not only memorize answers but truly understand the concepts behind them. With Exam Sage, you gain access to realistic practice that prepares you to excel on your certification day.
Prepare smart, practice thoroughly, and pass with confidence—choose Exam Sage as your partner in achieving the Google Cloud Professional Data Engineer certification.
Sample Questions and Answers
1. What is the best Google Cloud service to perform large-scale batch data processing?
A) Cloud Functions
B) Cloud Dataproc
C) Cloud Run
D) App Engine
Answer: B) Cloud Dataproc
Explanation: Cloud Dataproc is a fully managed Spark and Hadoop service that is ideal for large-scale batch data processing tasks. Cloud Functions is event-driven, Cloud Run is for containerized apps, and App Engine is for web applications.
2. You need to build a data pipeline to stream logs from Compute Engine instances into BigQuery with minimal latency. Which tool is most appropriate?
A) Cloud Pub/Sub + Dataflow
B) Cloud Storage + Dataproc
C) Cloud SQL + BigQuery Data Transfer Service
D) Cloud Composer
Answer: A) Cloud Pub/Sub + Dataflow
Explanation: Cloud Pub/Sub is a messaging service that can capture streaming logs, and Dataflow can process the stream and load it into BigQuery with low latency.
3. Which GCP service provides serverless, fully managed data warehouse with built-in machine learning?
A) BigQuery
B) Cloud SQL
C) Cloud Spanner
D) Dataproc
Answer: A) BigQuery
Explanation: BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse with built-in machine learning capabilities using BigQuery ML.
4. What is the primary storage format recommended for optimizing BigQuery query performance?
A) CSV
B) JSON
C) Parquet
D) XML
Answer: C) Parquet
Explanation: Parquet is a columnar storage format that is highly optimized for query performance and cost efficiency in BigQuery.
5. Which tool can be used to schedule and orchestrate complex workflows in a data pipeline on Google Cloud?
A) Cloud Functions
B) Cloud Composer
C) Cloud Run
D) Cloud Dataflow
Answer: B) Cloud Composer
Explanation: Cloud Composer is a managed Apache Airflow service designed for orchestration and scheduling of workflows.
6. You need to migrate an on-premises relational database to Cloud Spanner with minimal downtime. What is the recommended approach?
A) Export data to CSV and import into Cloud Spanner
B) Use Database Migration Service for continuous replication
C) Use Cloud Dataflow to transform data into Spanner
D) Dump data into Cloud Storage and then load manually
Answer: B) Use Database Migration Service for continuous replication
Explanation: The Database Migration Service supports minimal downtime migration with continuous replication for databases migrating to Cloud Spanner.
7. What does BigQuery’s “slot” refer to?
A) A storage unit for datasets
B) A unit of compute capacity for query execution
C) A partition in a table
D) A security role in IAM
Answer: B) A unit of compute capacity for query execution
Explanation: Slots represent units of computational capacity that execute queries in BigQuery. They determine concurrency and throughput.
8. Which data ingestion method allows you to load data from files stored in Google Cloud Storage into BigQuery efficiently?
A) BigQuery Data Transfer Service
B) BigQuery Batch Load Jobs
C) BigQuery Streaming Inserts
D) Cloud Pub/Sub
Answer: B) BigQuery Batch Load Jobs
Explanation: Batch load jobs allow bulk data loading from GCS files into BigQuery efficiently.
9. Which of the following is the best approach for ensuring data quality in a data pipeline?
A) Adding Cloud IAM roles
B) Implementing Dataflow data validation transforms
C) Using Cloud Scheduler
D) Monitoring with Stackdriver Logs only
Answer: B) Implementing Dataflow data validation transforms
Explanation: Data validation transforms in Dataflow allow checking data integrity and quality during pipeline execution.
10. Your data pipeline requires a real-time dashboard with sub-second latency. Which tool combination should you use?
A) Cloud Pub/Sub + BigQuery batch loads
B) Cloud Storage + Dataproc
C) Cloud Pub/Sub + BigQuery streaming inserts
D) Cloud SQL + Cloud Functions
Answer: C) Cloud Pub/Sub + BigQuery streaming inserts
Explanation: Pub/Sub with streaming inserts into BigQuery supports near real-time data ingestion, suitable for real-time dashboards.
11. How can you ensure data at rest in BigQuery is encrypted?
A) Use Cloud KMS to encrypt the data manually
B) BigQuery automatically encrypts data at rest by default
C) Enable encryption in Cloud Storage bucket
D) Use Customer Supplied Encryption Keys only
Answer: B) BigQuery automatically encrypts data at rest by default
Explanation: BigQuery encrypts all data at rest by default using Google-managed encryption keys.
12. What is the recommended way to monitor the health and performance of Dataflow jobs?
A) Use Cloud Monitoring with Dataflow metrics
B) Review logs manually on Compute Engine
C) Check Cloud Storage buckets
D) Use BigQuery audit logs only
Answer: A) Use Cloud Monitoring with Dataflow metrics
Explanation: Cloud Monitoring provides built-in metrics for Dataflow jobs to track performance and health.
13. Which is a benefit of using BigQuery partitioned tables?
A) Enables streaming inserts
B) Improves query performance and reduces cost by scanning less data
C) Allows data versioning
D) Supports multi-cloud querying
Answer: B) Improves query performance and reduces cost by scanning less data
Explanation: Partitioning helps by restricting queries to specific partitions, reducing data scanned and cost.
14. What does the Cloud Dataflow service use internally to execute batch and stream processing?
A) Apache Spark
B) Apache Flink
C) Apache Beam
D) Apache Hadoop
Answer: C) Apache Beam
Explanation: Dataflow executes pipelines based on the Apache Beam programming model.
15. You want to anonymize personally identifiable information (PII) in your dataset stored in BigQuery. Which tool should you use?
A) Cloud DLP (Data Loss Prevention)
B) Cloud IAM
C) Cloud Functions
D) Cloud Pub/Sub
Answer: A) Cloud DLP (Data Loss Prevention)
Explanation: Cloud DLP can discover, classify, and redact sensitive data in BigQuery tables.
16. What is the best method to optimize BigQuery query cost when working with large datasets?
A) Use SELECT * queries
B) Use table partitioning and clustering
C) Export data to CSV before querying
D) Use only streaming inserts
Answer: B) Use table partitioning and clustering
Explanation: Partitioning and clustering reduce data scanned during queries, optimizing cost.
17. Which BigQuery feature enables you to query data stored in external sources like Cloud Storage without loading it?
A) Federated queries
B) Data Transfer Service
C) Dataflow connectors
D) Cloud Storage snapshots
Answer: A) Federated queries
Explanation: Federated queries let you query external data sources directly without data ingestion.
18. Which IAM role should you assign to a user who only needs to run queries in BigQuery without the ability to modify datasets?
A) BigQuery Admin
B) BigQuery Data Viewer
C) BigQuery Job User
D) BigQuery Data Editor
Answer: C) BigQuery Job User
Explanation: BigQuery Job User can run queries but cannot modify datasets or tables.
19. How does BigQuery handle schema changes in append-only tables?
A) It requires manual schema migration
B) Supports automatic schema updates on append
C) Does not allow any schema changes
D) Schema changes are only possible through export/import
Answer: B) Supports automatic schema updates on append
Explanation: BigQuery allows adding nullable columns automatically when appending data.
20. You want to build a machine learning model directly inside BigQuery. Which feature enables this?
A) BigQuery ML
B) AI Platform
C) TensorFlow on Cloud Functions
D) AutoML Tables
Answer: A) BigQuery ML
Explanation: BigQuery ML lets you create and train ML models using SQL inside BigQuery.
21. Which is the best practice to reduce Dataflow job cost?
A) Use batch mode instead of streaming when possible
B) Use Cloud Functions for all data processing
C) Store intermediate data in Cloud Storage only
D) Disable autoscaling
Answer: A) Use batch mode instead of streaming when possible
Explanation: Batch jobs typically cost less than streaming jobs for large datasets due to resource utilization.
22. What is the default consistency model for BigQuery?
A) Eventual consistency
B) Strong consistency
C) Read-after-write consistency only for streaming
D) No consistency guarantees
Answer: B) Strong consistency
Explanation: BigQuery provides strong consistency for all queries.
23. What is the main difference between Cloud Dataproc and Cloud Dataflow?
A) Dataproc is serverless, Dataflow requires cluster management
B) Dataproc manages Hadoop/Spark clusters; Dataflow is serverless stream and batch processing
C) Dataflow supports only batch; Dataproc supports streaming
D) Dataproc is only for SQL workloads
Answer: B) Dataproc manages Hadoop/Spark clusters; Dataflow is serverless stream and batch processing
Explanation: Dataproc requires cluster management, Dataflow is fully serverless and supports both batch and streaming natively.
24. How can you prevent unauthorized access to sensitive BigQuery datasets?
A) Use VPC Service Controls and IAM policies
B) Use Cloud Scheduler to disable datasets
C) Export data and encrypt manually
D) Use Cloud Functions as proxy
Answer: A) Use VPC Service Controls and IAM policies
Explanation: VPC Service Controls provide network-level security and IAM controls manage identity and access.
25. You want to orchestrate a data pipeline that runs every day at midnight and triggers multiple Dataflow jobs. Which tool should you use?
A) Cloud Scheduler + Cloud Functions + Dataflow API
B) Cloud Composer
C) Cloud Pub/Sub only
D) Cloud Run
Answer: B) Cloud Composer
Explanation: Cloud Composer is designed to schedule and orchestrate complex workflows, including Dataflow job triggers.
26. What is the primary benefit of clustering tables in BigQuery?
A) Improves streaming data ingestion speed
B) Organizes data to speed up queries with filter predicates on clustered columns
C) Encrypts data at rest
D) Enables multi-region replication
Answer: B) Organizes data to speed up queries with filter predicates on clustered columns
Explanation: Clustering organizes data based on column values, making filtering queries more efficient.
27. What is the purpose of a Data Catalog in GCP?
A) To store and query large datasets
B) To manage and discover metadata about datasets across Google Cloud
C) To orchestrate ETL pipelines
D) To stream data from on-premises to cloud
Answer: B) To manage and discover metadata about datasets across Google Cloud
Explanation: Data Catalog is a metadata management service to discover and govern data assets.
28. Which approach is recommended for encrypting data with your own keys in BigQuery?
A) Use Customer Managed Encryption Keys (CMEK) via Cloud KMS
B) Upload encrypted files manually
C) Use Customer Supplied Encryption Keys (CSEK) only
D) Use default Google-managed keys exclusively
Answer: A) Use Customer Managed Encryption Keys (CMEK) via Cloud KMS
Explanation: CMEK allows customers to manage encryption keys through Cloud KMS, integrated with BigQuery.
29. How can you optimize streaming inserts into BigQuery to reduce cost?
A) Batch streaming inserts to reduce API calls
B) Use CSV instead of JSON
C) Disable encryption
D) Use multiple tables instead of partitioned tables
Answer: A) Batch streaming inserts to reduce API calls
Explanation: Batching streaming inserts reduces the number of API calls, lowering cost and improving throughput.
30. You want to analyze data in BigQuery with a BI tool like Looker. What is the best practice to ensure efficient querying?
A) Use federated queries with external data sources only
B) Use materialized views or scheduled queries to pre-aggregate data
C) Export data to Cloud Storage and import into BI tool
D) Use Cloud Functions to transform data
Answer: B) Use materialized views or scheduled queries to pre-aggregate data
Explanation: Pre-aggregating data reduces query time and cost, improving BI tool performance.
31. Which Google Cloud service can be used to automate the deployment and management of machine learning models?
A) AI Platform Prediction
B) BigQuery ML
C) Cloud Functions
D) Cloud Dataproc
Answer: A) AI Platform Prediction
Explanation: AI Platform Prediction enables managed deployment, versioning, and serving of ML models with autoscaling.
32. What is the primary reason to use Cloud Storage as a staging area in a data pipeline?
A) To enable streaming data ingestion
B) To provide durable, scalable storage for raw or intermediate data before processing
C) To run SQL queries directly on stored files
D) To schedule data workflows
Answer: B) To provide durable, scalable storage for raw or intermediate data before processing
Explanation: Cloud Storage acts as a durable staging area to hold large datasets before processing or loading into other systems.
33. You want to enforce data governance and track data lineage across your data pipeline. Which Google Cloud service supports this?
A) Cloud Data Loss Prevention
B) Cloud Data Catalog
C) Cloud Pub/Sub
D) Cloud Composer
Answer: B) Cloud Data Catalog
Explanation: Data Catalog helps manage metadata, enforce governance policies, and track lineage for datasets.
34. What is the recommended method to transform data in real-time before loading it into BigQuery?
A) Cloud Dataproc
B) Cloud Dataflow
C) Cloud SQL
D) BigQuery Data Transfer Service
Answer: B) Cloud Dataflow
Explanation: Dataflow is a serverless data processing service ideal for real-time ETL before data lands in BigQuery.
35. Which of the following is a valid use case for Cloud Bigtable?
A) Transactional relational database
B) Large-scale NoSQL database for time series or IoT data
C) Data warehousing and analytics
D) Object storage for media files
Answer: B) Large-scale NoSQL database for time series or IoT data
Explanation: Cloud Bigtable is designed for high throughput, low latency workloads, like time series and IoT data.
36. You want to ensure your Dataflow pipeline automatically scales resources based on workload. Which feature should you enable?
A) Autoscaling
B) Manual worker allocation
C) Reserved slots
D) Batch mode only
Answer: A) Autoscaling
Explanation: Dataflow’s autoscaling automatically adjusts worker instances to optimize resource usage and cost.
37. How can you optimize query performance in BigQuery when dealing with large datasets?
A) Use SELECT * to get all columns
B) Partition tables by date and cluster on commonly filtered columns
C) Export data to Cloud SQL and query there
D) Use only streaming inserts
Answer: B) Partition tables by date and cluster on commonly filtered columns
Explanation: Partitioning and clustering improve query efficiency by limiting scanned data.
38. Which tool allows you to automate repetitive BigQuery data analysis tasks?
A) Cloud Composer
B) Cloud Functions
C) Cloud Scheduler
D) BigQuery Scheduled Queries
Answer: D) BigQuery Scheduled Queries
Explanation: Scheduled Queries automate execution of SQL queries on a recurring schedule.
39. What is the best practice for handling schema evolution in BigQuery?
A) Always delete and recreate tables
B) Use schema auto-detection on load jobs and add nullable fields for new columns
C) Avoid any schema changes
D) Export and import data for every schema change
Answer: B) Use schema auto-detection on load jobs and add nullable fields for new columns
Explanation: BigQuery supports schema updates for append jobs when adding nullable columns.
40. When should you use BigQuery BI Engine?
A) For cost-free data storage
B) To accelerate dashboard and report performance by caching query results
C) To stream data into BigQuery
D) To migrate data from on-premises
Answer: B) To accelerate dashboard and report performance by caching query results
Explanation: BI Engine is an in-memory analysis service that accelerates BigQuery queries used by BI tools.
41. You have an ETL job that extracts data from multiple sources, transforms it, and loads into BigQuery nightly. Which service is ideal for orchestrating this workflow?
A) Cloud Composer
B) Cloud Dataflow
C) Cloud Pub/Sub
D) Cloud Functions
Answer: A) Cloud Composer
Explanation: Cloud Composer is ideal for scheduling and managing complex ETL workflows.
42. How can you secure data in BigQuery from unauthorized access?
A) Use Cloud IAM policies and VPC Service Controls
B) Export data regularly and encrypt manually
C) Use Cloud Functions to proxy access
D) Store data in Cloud Storage only
Answer: A) Use Cloud IAM policies and VPC Service Controls
Explanation: IAM controls identity-based access; VPC Service Controls restrict data movement to trusted networks.
43. What does a BigQuery reservation allow you to do?
A) Reserve compute capacity (slots) for your projects to guarantee resources
B) Store datasets in reserved storage
C) Schedule queries to run at fixed intervals
D) Encrypt data with custom keys
Answer: A) Reserve compute capacity (slots) for your projects to guarantee resources
Explanation: Reservations allow purchasing dedicated slots to guarantee performance.
44. Which Google Cloud service is best suited for ingesting streaming data from millions of IoT devices?
A) Cloud Storage
B) Cloud Pub/Sub
C) Cloud Dataproc
D) Cloud SQL
Answer: B) Cloud Pub/Sub
Explanation: Pub/Sub is designed for ingesting and distributing massive volumes of streaming data.
45. What type of machine learning models can be created using BigQuery ML?
A) Only linear regression
B) Linear regression, logistic regression, k-means clustering, and more
C) Deep neural networks only
D) Only image recognition models
Answer: B) Linear regression, logistic regression, k-means clustering, and more
Explanation: BigQuery ML supports multiple model types including regression, classification, clustering, and time series forecasting.
46. Which approach is recommended for cost management in BigQuery?
A) Use on-demand pricing exclusively
B) Use flat-rate pricing with slot reservations for predictable workloads
C) Disable encryption to reduce cost
D) Use streaming inserts exclusively
Answer: B) Use flat-rate pricing with slot reservations for predictable workloads
Explanation: Flat-rate pricing offers predictable costs by reserving query slots for steady workloads.
47. What is the purpose of the BigQuery Data Transfer Service?
A) To schedule queries in BigQuery
B) To automate data movement from SaaS applications and external sources into BigQuery
C) To migrate data from on-premises Hadoop clusters
D) To encrypt data at rest
Answer: B) To automate data movement from SaaS applications and external sources into BigQuery
Explanation: It automates data loading from Google Ads, YouTube, Salesforce, and more.
48. What is the primary benefit of using Cloud Pub/Sub dead-letter topics?
A) Automatically delete failed messages
B) Retain failed messages for later analysis or reprocessing
C) Encrypt messages in transit
D) Route messages to Cloud Storage
Answer: B) Retain failed messages for later analysis or reprocessing
Explanation: Dead-letter topics help handle and troubleshoot messages that cannot be processed successfully.
49. Which storage class in Cloud Storage is best suited for data accessed less than once a year but requires immediate availability?
A) Standard
B) Nearline
C) Coldline
D) Archive
Answer: C) Coldline
Explanation: Coldline offers low-cost storage with immediate availability for infrequently accessed data.
50. You want to prevent sensitive data from being ingested into your data pipeline. Which Google Cloud service can automatically detect and mask this data?
A) Cloud DLP (Data Loss Prevention)
B) Cloud Pub/Sub
C) Cloud Functions
D) Cloud SQL
Answer: A) Cloud DLP (Data Loss Prevention)
Explanation: Cloud DLP can scan and redact sensitive information like PII before ingestion.
51. What is the best practice to minimize data loss in streaming pipelines?
A) Use batch loads only
B) Enable exactly-once processing in Dataflow pipelines
C) Disable autoscaling
D) Store data only in Cloud Storage
Answer: B) Enable exactly-once processing in Dataflow pipelines
Explanation: Exactly-once processing ensures data is not lost or duplicated during streaming.
52. How can you improve query performance when filtering on multiple columns in BigQuery?
A) Use table clustering on the relevant columns
B) Avoid filters entirely
C) Use federated queries
D) Export data to CSV and process externally
Answer: A) Use table clustering on the relevant columns
Explanation: Clustering organizes data to speed up filtering on multiple columns.
53. Which service provides a serverless environment for running containerized applications triggered by events?
A) Cloud Run
B) Cloud Functions
C) App Engine Standard
D) Cloud Dataflow
Answer: A) Cloud Run
Explanation: Cloud Run runs containers in a fully managed, serverless environment, ideal for event-driven workloads.
54. Which of the following allows BigQuery to read data from external sources without importing it?
A) External tables (federated queries)
B) Batch load jobs
C) Streaming inserts
D) Cloud Storage Transfer
Answer: A) External tables (federated queries)
Explanation: External tables let you query external sources like Cloud Storage directly.
55. Which tool can be used to visualize BigQuery data interactively?
A) Looker
B) Cloud Functions
C) Cloud Storage
D) Dataproc
Answer: A) Looker
Explanation: Looker is a BI tool integrated with BigQuery for interactive data visualization.
56. What is a key advantage of serverless data processing with Dataflow?
A) Requires manual cluster provisioning
B) Automatically scales to match workload demand without management
C) Limited to batch processing only
D) Requires pre-configuration of hardware specs
Answer: B) Automatically scales to match workload demand without management
Explanation: Dataflow abstracts infrastructure management and scales resources automatically.
57. How does BigQuery handle user-defined functions (UDFs)?
A) UDFs are not supported
B) Supports JavaScript and SQL UDFs for custom logic inside queries
C) Only supports stored procedures
D) Requires external APIs for custom functions
Answer: B) Supports JavaScript and SQL UDFs for custom logic inside queries
Explanation: BigQuery allows user-defined functions written in JavaScript or SQL for reusable logic.
58. Which Google Cloud service helps you monitor and log your data engineering pipelines?
A) Cloud Monitoring and Cloud Logging
B) Cloud Scheduler
C) Cloud DNS
D) Cloud Storage
Answer: A) Cloud Monitoring and Cloud Logging
Explanation: These services provide visibility and alerting for GCP services and pipelines.
59. Which of the following is NOT a valid data ingestion method into BigQuery?
A) Batch loading from Cloud Storage
B) Streaming inserts via API
C) Direct upload from Google Sheets using BigQuery Data Transfer Service
D) Using BigQuery Data Transfer Service for scheduled SaaS data loads
Answer: C) Direct upload from Google Sheets using BigQuery Data Transfer Service
Explanation: BigQuery Data Transfer Service does not support direct uploads from Google Sheets.
60. To automate infrastructure provisioning for your data platform on GCP, which tool would you use?
A) Terraform
B) Cloud Functions
C) Dataflow
D) Cloud Storage
Answer: A) Terraform
Explanation: Terraform automates infrastructure provisioning using declarative configuration files.
