20+ practice questions focused on Designing Data Processing Systems — one of the most tested topics on the Google Professional Data Engineer exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Designing Data Processing Systems PracticeA data engineer wants to create a BigQuery table that is partitioned by day and clustered by user_id and product_id. Which SQL statement should they use?
Explanation: The correct syntax for creating a partitioned and clustered table in BigQuery requires PARTITION BY followed by CLUSTER BY in that order. Since `event_date` is of type DATE, `PARTITION BY event_date` correctly partitions by day. Option A unnecessarily wraps `event_date` in `DATE()`, which is only needed for TIMESTAMP columns. Option C is a duplicate of B and cannot be correct in a single-select question. Option D incorrectly places `CLUSTER BY` before `PARTITION BY`, which is invalid.
A Dataflow pipeline processes a high-volume stream of JSON events. The pipeline has a bottleneck where a ParDo transformation performs an external API call for each element, causing high latency. Which strategy would BEST improve throughput without sacrificing correctness?
Explanation: Grouping elements by key with GroupByKey allows batching multiple elements that require the same API call, reducing the number of external calls and improving throughput. Side inputs are not designed for dynamic batching; they provide static data to all elements. Increasing workers or switching to MapElements does not address the core issue of per-element API calls.
A company is designing a data pipeline using the lambda architecture. They need to process both real-time streams and batch historical data. Which THREE components are essential for a lambda architecture on Google Cloud?
Explanation: The three essential components for a lambda architecture on Google Cloud are: Dataflow for batch processing (batch layer), Cloud Pub/Sub for real-time ingestion (used by both batch and speed layers), and Dataflow for stream processing (speed layer). These form the batch, ingestion, and speed layers. The serving layer (e.g., BigQuery) is also important but not among the three essential components listed here.
A media company processes video metadata using a Dataflow pipeline. They need to join two streaming sources: user activity (Pub/Sub) and video catalog updates (Pub/Sub). Which THREE transforms should be used in the pipeline?
Explanation: To join two streaming Pub/Sub sources in Dataflow, you need to: (1) Use ParDo to extract the key from each element (e.g., video_id), (2) Window both PCollections into a common window (e.g., fixed 1-minute) to align the data, and (3) Use CoGroupByKey to join on the common key. GroupByKey separately is not required because CoGroupByKey internally groups by key. Flatten is used to combine PCollections of the same type, which is not applicable here.
A data pipeline ingests streaming events into Pub/Sub and needs to join them with a slowly updating reference table (few thousand rows) from a Cloud Storage CSV file. The pipeline runs on Dataflow with Apache Beam. Which approach is most cost-effective and operationally simple?
Explanation: Side inputs in Apache Beam allow you to read a bounded dataset (the CSV) once and broadcast it as a read-only map to all workers processing the unbounded stream. For a small reference table (few thousand rows), this is both cost-effective (no external database calls) and operationally simple. Option A would incur high latency and cost by making a BigQuery query per event. Option C introduces Cloud SQL as an external dependency, increasing complexity and latency. Option D is incorrect because CoGroupByKey is used for joining two unbounded PCollections or a bounded and an unbounded PCollection, but reading the CSV into a batch PCollection each window would cause repeated reads and is inefficient.
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Practice all Designing Data Processing Systems questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Designing Data Processing Systems. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Designing Data Processing Systems questions on the PDE frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Designing Data Processing Systems is tested as part of the Google Professional Data Engineer blueprint. Practicing with targeted Designing Data Processing Systems questions ensures you can handle any format or difficulty that appears.
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