Free Google Professional Data Engineer practice exams are available right now, no account required for the first set. The Professional Data Engineer (PDE) is Google Cloud's flagship credential for engineers who design, build, and operationalise data pipelines, and realistic scenario practice is the single best way to find weak spots before the real two-hour test.
What the Professional Data Engineer exam covers
The PDE exam tests whether you can take raw data from many sources, land it reliably in storage, transform it with batch and streaming pipelines, expose it for analytics and ML, and run the result in production with security and cost control in mind. You're expected to be fluent with BigQuery partitioning and clustering, Dataflow window semantics, Pub/Sub delivery guarantees, Bigtable schema design, Dataproc autoscaling, Cloud Composer DAGs, and the Vertex AI data tooling. Questions are scenario based and reward judgement over recall, so you need to recognise when a cheaper service fails a stated SLA, when partitioning by date is wrong because the predicate uses a different column, or when a session window beats a fixed window.
What's in these practice exams
You get 25 unique question sets, each with 20 questions, for a total of 500 questions. Coverage is weighted to match the real exam:
- Ingesting and processing the data (25%)
- Designing data processing systems (22%)
- Storing the data (20%)
- Maintaining and automating data workloads (18%)
- Preparing and using data for analysis (15%)
Every question includes a detailed explanation that walks through why the chosen service or technique is right and why each alternative falls short in the given scenario. Questions span Easy, Medium, and Hard difficulties so you can build confidence before tackling streaming corner cases and BigQuery cost-optimisation traps. The first set is completely free with no signup. Unlock the remaining 24 sets when you want more reps.
How to use these effectively
Start with the free first set under realistic conditions: two hours, no distractions, no documentation. Score yourself, then read every explanation, even on questions you got right. The reasoning behind each answer is where most of the learning happens, especially for trade-off questions like Bigtable versus BigQuery for time-series, or Dataflow versus Dataproc for batch ETL.
For weak areas, jump back to the official BigQuery, Dataflow, and Pub/Sub documentation and try the patterns in your own sandbox project. Hands-on reps lock in details that reading alone never quite delivers.
Aim for at least 80 percent on three different sets before booking the real exam. That margin gives you room for the few items that surprise everyone on test day.