Google Professional Data Engineer Exam Practice Questions, practice question pack
GOOGLE · Professional Data Engineer

Google Professional Data Engineer Exam Practice Questions

349 questionsPDF by emailUpdated September 2026

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Three of the 349 questions in this pack

Question 1

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

  1. Threading
  2. Serialization
  3. Dropout Methods
  4. Dimensionality Reduction
Show answer and explanation

Correct answer: C. Dropout Methods

Dropout is a regularization technique that randomly deactivates neurons during training, preventing the model from relying too heavily on specific pathways and reducing overfitting. The model fits training data well but performs poorly on new data, which is the classic sign of overfitting. Dropout addresses this by forcing the network to learn more robust features that generalize better to unseen data.

Why the other options are wrong

  • A. Threading is a concurrency mechanism unrelated to model generalization or overfitting issues.
  • B. Serialization is about model storage and persistence, not addressing poor performance on new data.
  • D. Dimensionality reduction may help with high-dimensional data but doesn't directly address overfitting in a neural network with excessive neurons and layers.

Question 2

You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

  1. Continuously retrain the model on just the new data.
  2. Continuously retrain the model on a combination of existing data and the new data.
  3. Train on the existing data while using the new data as your test set.
  4. Train on the new data while using the existing data as your test set.
Show answer and explanation

Correct answer: B. Continuously retrain the model on a combination of existing data and the new data.

existing data and the new data. When retraining a model with streaming data, you should use a combination of existing and new data to preserve learned patterns while adapting to changing user preferences. Training only on new data (option A) causes catastrophic forgetting where the model loses previously learned fashion preferences. Using new data as a test set or training only on new data would result in a model that doesn't retain historical knowledge about user behavior.

Why the other options are wrong

  • A. Training only on new data causes catastrophic forgetting and loss of previously learned patterns.
  • C. New data should be used for training, not testing, to capture evolving user preferences.
  • D. Training only on new data while testing on old data produces a model that cannot generalize to historical patterns.

Question 3

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

  1. Add capacity (memory and disk space) to the database server by the order of 200.
  2. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
  3. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
  4. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
Show answer and explanation

Correct answer: C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

patient table and the visits table, and create other necessary tables to avoid self- join. The single wide table forces expensive self-joins and stores repeated patient attributes on every visit row, so cost grows sharply as record volume multiplies by 100. Normalizing into a patient table and a visits table, plus supporting lookup tables, removes the duplicated data and replaces self-joins with ordinary indexed joins between much smaller tables. That fixes the root cause of the failures rather than masking it with more hardware or narrower queries.

Why the other options are wrong

  • A. Scaling the server 200 times is costly and leaves the redundant schema and self- joins in place, so queries remain inefficient.
  • B. Sharding by date range adds operational overhead, restricts reporting to prespecified windows, and still requires the same self-joins within each shard.
  • D. Splitting by clinic keeps the denormalized structure and its self-joins while forcing unions for any cross-clinic report.

See all 10 free questions Get the full pack, US$39

349 practice questions for Google Professional Data Engineer (PDE), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 349 questions mapped to the PDE exam objectives
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A PDE attempt costs US$200. This pack is US$39, paid once.

Try 10 questions free before you buy.

Last updated September 2026 · 349 questions

What makes the PDE hard

Every question on the PDE is a storage-and-processing decision wearing a business scenario as a disguise: a company with specific latency, cost and consistency requirements, and four Google services that all technically hold data.

BigQuery versus Bigtable versus Spanner versus Firestore is the exam’s favourite fork, followed closely by Dataflow windowing and late-data handling for streaming pipelines, Pub/Sub delivery semantics, and when Dataproc exists purely because the scenario mentions existing Hadoop jobs.

It is two hours for only 40 to 50 questions, fewer than most Google professional exams, which means each one carries more weight and the scenarios are longer. IAM, encryption, DLP and a layer of Vertex AI questions round out the paper. This pack will have 349 practice questions mapped to the PDE exam objectives, covering the service-selection and pipeline scenarios Google rotates through.

About the exam

The Professional Data Engineer certifies designing, building, securing, and operationalising data processing systems on Google Cloud. Google recommends three or more years of industry experience including one or more year on Google Cloud. It is valid for two years.

Exam sections

  • Design data processing systems
  • Ingest and process the data
  • Store the data
  • Prepare and use data for analysis
  • Maintain and automate data workloads

Google does not publish percentage weightings for these sections. 40 to 50 questions, 2 hours, multiple choice and multiple select, US$200 per attempt, available in English and Japanese, online proctored or test centre, valid 2 years.

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Questions before you buy

What do I get when I buy the Google Professional Data Engineer pack?

349 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

How quickly do I receive it?

Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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