Professional Data Engineer Practice Exams

PDE

⭐ Most popularAdvancedProfessionalData Engineer

Validates the ability to design, build, operationalize, secure, and monitor data processing systems on Google Cloud — with an emphasis on BigQuery, Dataflow, and machine learning — the professional-level credential for data engineers.

120

minutes

70%

passing score

$200

exam fee

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5 flashcard sets · 5 learning tests · 2 timed exams · 50-question bank

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Overview

What this certification is and how the exam works.

The Google Cloud Professional Data Engineer certification validates the ability to design and build data processing systems and operationalize machine learning models on Google Cloud. It covers designing pipelines for batch and streaming, ingesting and processing data with Pub/Sub, Dataflow, Dataproc, and Data Fusion, storing data in BigQuery, Bigtable, Cloud SQL, Spanner, and Cloud Storage, preparing and analyzing data (BigQuery optimization, BigQuery ML, Looker), and maintaining and automating workloads with Cloud Composer, plus data governance, security, and quality. The exam is 50–60 questions in 2 hours, and Google recommends 3+ years of industry experience, including 1+ year designing and managing solutions on Google Cloud.

Why should I take the exam?

What this credential does for your career.

1Consistently ranked among the highest-paying IT certifications in the world
2The definitive credential for data engineering roles on Google Cloud — BigQuery, Dataflow, and ML
3Data and analytics roles are among the fastest-growing and most in-demand across every industry
4Case-based preparation teaches real pipeline, storage, and ML trade-offs used on the job

Skills measured

What you need to know to pass this exam.

✓Design batch and streaming data pipelines and choose the right storage for each workload
✓Ingest and process data with Pub/Sub, Dataflow (Apache Beam), Dataproc, and Data Fusion
✓Model and optimize BigQuery: partitioning, clustering, slots/reservations, and materialized views
✓Design Bigtable schemas and row keys to avoid hotspots at scale
✓Build and operationalize ML with BigQuery ML, Vertex AI, AutoML, and pre-trained APIs
✓Orchestrate and automate workflows with Cloud Composer (Apache Airflow)
✓Secure data with IAM, CMEK, VPC Service Controls, and Cloud DLP
✓Govern data with Dataplex and Data Catalog; ensure data quality and lineage
✓Monitor pipeline reliability, cost, and performance across the data platform

About our PDE practice exams

Our Professional Data Engineer PDE question bank holds 50 exam-style questions with a written explanation on every answer, mapped to the five exam domains published for PDE, covering Designing data processing systems, Ingesting and processing the data, Storing the data, Preparing and using data for analysis and Maintaining and automating data workloads.

Alongside the bank there are two full-length timed exams at the real 120-minute limit and 70% pass mark, plus five topic-based learning tests for working a single domain at a time. The bank includes 2 multiple-response questions, so the interactive formats are not a surprise on exam day. Randomised mock exams are dealt on demand from the full 50-question pool, so you never run out of fresh papers.

Questions: 50
Exam domains: 5
Timed exams: 2
Pass mark: 70%

How our practice content is created

Practice questions are independently developed using the official Google Cloud PDE exam guide and Google Cloud documentation. Each question is checked for alignment with the current exam objectives and reviewed for technical accuracy before publication.

The PDE bank cites 48 distinct Google Cloud documentation pages, and every question links the source it was written against — so you can check any answer at first hand.

Official PDE exam guide

Google Cloud is a trademark of its respective owner. This is an independent study resource and is not affiliated with, endorsed by, or authorised by Google Cloud.