Free Google Cloud Professional Data Engineer Practice Test

A media company needs to ingest clickstream events from millions of mobile devices with highly variable traffic. The events must be processed in near real time, and the ingestion layer must absorb traffic spikes without losing data. What is the best Google Cloud solution?

Your organization wants to build a data lake on Google Cloud for raw and curated datasets in multiple formats, including CSV, JSON, and Parquet. The storage layer must be low cost, highly durable, and accessible by multiple analytics services. Which service should you choose?

A data analyst needs to run SQL queries over several terabytes of structured data with minimal operational overhead. The team does not want to manage infrastructure. Which Google Cloud service best fits this requirement?

You need to orchestrate a daily data pipeline that runs BigQuery SQL transformations, executes a Dataflow job, and sends a notification if any task fails. The solution should support dependencies, scheduling, and retries. What should you use?

A company is designing a new analytics platform. They need to process both streaming and batch data using a unified programming model and want the service to automatically manage scaling and worker provisioning. Which service should they use?

A retail company loads daily sales records into BigQuery. Most queries filter by transaction_date and often aggregate by store_id. Query performance is degrading as the table grows. What design change should you implement first to improve performance and reduce scanned data?

Your team receives JSON event data from partners. The schema occasionally changes by adding new optional fields, and ingestion should continue without frequent pipeline failures. Which approach is most appropriate?

A financial services company needs a globally distributed relational database for operational data that requires strong consistency, horizontal scalability, and high availability. Which service should be selected?

You have a streaming Dataflow pipeline that reads messages from Pub/Sub and writes results to BigQuery. During temporary downstream slowdowns, you want to avoid data loss and allow the system to recover automatically once BigQuery throughput improves. Which design principle is most important?

A company needs to transfer a large on-premises relational database into BigQuery with minimal custom coding. They want managed migration with schema and data movement support. Which service should they consider first?

A data engineering team wants to transform raw Cloud Storage files into curated BigQuery tables each night. The source files are large, and the transformations require distributed processing with SQL and open source tools like Spark. Which service is the best fit?

Your organization wants analysts to query raw CSV and Parquet files stored in Cloud Storage without first loading them into BigQuery tables. Which feature should you use?

A streaming pipeline computes user session metrics. Some events arrive several minutes late because of intermittent mobile connectivity. The business wants metrics to remain accurate even when late events arrive within an acceptable delay threshold. What should you do?

A company stores time-series IoT sensor readings and needs single-digit millisecond reads for individual device lookups at very high scale. The queries are based primarily on device ID and timestamp ranges, not complex joins or SQL analytics. Which storage service is most appropriate?

You need to provide secure BigQuery access to a curated subset of columns and rows from a sensitive dataset without copying the data into separate tables. What is the best approach?

A BigQuery ETL workflow occasionally fails because upstream files arrive later than expected. You want to reduce pipeline fragility and automatically delay downstream execution until source data is available. What is the best solution?

A company wants to improve data quality in a batch pipeline. They need to validate incoming records for required fields, acceptable ranges, and referential integrity before loading curated tables. Which approach is best?

You are designing a multi-stage analytics pipeline that includes raw ingestion, standardized transformations, and business-ready reporting tables. The business wants reproducibility, traceability, and the ability to reprocess historical data when transformation logic changes. Which design is best?

A Dataflow streaming job has rising end-to-end latency. Investigation shows one transformation is much slower than others and is causing worker imbalance. What is the most appropriate first action?

A team needs to build a feature engineering workflow where large historical datasets in BigQuery are transformed with SQL and then consumed by analysts and downstream ML processes. They want to maximize reuse of transformed datasets and minimize data movement. What is the best approach?