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Google Cloud Professional Data Engineer Exam Guide

Everything you need to know about the Google Cloud Professional Data Engineer certification exam, from study strategies to exam day tips.

Exam Information

120 minutes

Duration

50-60 questions

Questions

~70% (Google doesn't publish exact score)

Passing Score

$200 USD

Exam Cost

Recommended Study Plan

Week 1-2

Designing Data Processing Systems

Designing data processing systems (~22%)

15-20 hours

BigQuery architecture and optimization
Dataflow streaming and batch processing
Pub/Sub for real-time messaging
Data pipeline design patterns

Recommended Resources:

Google Cloud Documentation

Qwiklabs

Week 3-4

Ingesting & Processing Data

Ingesting and processing data (~25%)

15-20 hours

Cloud Storage for data lakes
Dataproc for Spark/Hadoop workloads
Cloud Functions and Cloud Run
Data transfer services and patterns

Recommended Resources:

Data Engineering Learning Path

Practice Questions

Week 5-6

Storing & Analyzing Data

Storing and managing data (~20%)

20-25 hours

BigQuery advanced features (partitioning, clustering)
Cloud Spanner for global transactions
Bigtable for low-latency NoSQL
Data catalog and metadata management

Recommended Resources:

BigQuery Best Practices

Hands-on Labs

Week 7-8

ML Pipelines, Security & Automation

Machine learning models (~18%), Security (~15%)

15-20 hours

Vertex AI for ML pipelines
AutoML and custom model training
IAM roles and data security
Encryption, VPC Service Controls, Cloud DLP

Recommended Resources:

ML on GCP Guide

Practice Exams

Exam Tips & Strategies

Master BigQuery

BigQuery is central to GCP data engineering. Know partitioning, clustering, materialized views, and cost optimization.

Manage Your Time

You have about 2 minutes per question. Read scenarios carefully but don't overthink—eliminate wrong answers quickly.

Know When to Use Which Service

Understand when to use Dataflow vs Dataproc, BigQuery vs Bigtable, Cloud SQL vs Cloud Spanner.

Hands-on Experience is Essential

Use Qwiklabs and Google Cloud Skills Boost. The exam tests practical knowledge, not just theory.

Key Topics to Master

BigQuery

Dataflow (Apache Beam)

Cloud Pub/Sub

Dataproc

Cloud Storage

Cloud Bigtable

Cloud Spanner

Cloud Composer (Airflow)

Vertex AI

Data Catalog

Cloud DLP

IAM & Security

Streaming vs Batch Processing

Data Pipeline Design

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide

Back to Professional Data Engineer

Google Cloud Professional Data Engineer Exam Guide

Everything you need to know about the Google Cloud Professional Data Engineer certification exam, from study strategies to exam day tips.

Exam Information

120 minutes

Duration

50-60 questions

Questions

~70% (Google doesn't publish exact score)

Passing Score

$200 USD

Exam Cost

Recommended Study Plan

Week 1-2

Designing Data Processing Systems

Designing data processing systems (~22%)

15-20 hours

BigQuery architecture and optimization
Dataflow streaming and batch processing
Pub/Sub for real-time messaging
Data pipeline design patterns

Recommended Resources:

Google Cloud Documentation

Qwiklabs

Week 3-4

Ingesting & Processing Data

Ingesting and processing data (~25%)

15-20 hours

Cloud Storage for data lakes
Dataproc for Spark/Hadoop workloads
Cloud Functions and Cloud Run
Data transfer services and patterns

Recommended Resources:

Data Engineering Learning Path

Practice Questions

Week 5-6

Storing & Analyzing Data

Storing and managing data (~20%)

20-25 hours

BigQuery advanced features (partitioning, clustering)
Cloud Spanner for global transactions
Bigtable for low-latency NoSQL
Data catalog and metadata management

Recommended Resources:

BigQuery Best Practices

Hands-on Labs

Week 7-8

ML Pipelines, Security & Automation

Machine learning models (~18%), Security (~15%)

15-20 hours

Vertex AI for ML pipelines
AutoML and custom model training
IAM roles and data security
Encryption, VPC Service Controls, Cloud DLP

Recommended Resources:

ML on GCP Guide

Practice Exams

Exam Tips & Strategies

Master BigQuery

BigQuery is central to GCP data engineering. Know partitioning, clustering, materialized views, and cost optimization.

Manage Your Time

You have about 2 minutes per question. Read scenarios carefully but don't overthink—eliminate wrong answers quickly.

Know When to Use Which Service

Understand when to use Dataflow vs Dataproc, BigQuery vs Bigtable, Cloud SQL vs Cloud Spanner.

Hands-on Experience is Essential

Use Qwiklabs and Google Cloud Skills Boost. The exam tests practical knowledge, not just theory.

Key Topics to Master

BigQuery

Dataflow (Apache Beam)

Cloud Pub/Sub

Dataproc

Cloud Storage

Cloud Bigtable

Cloud Spanner

Cloud Composer (Airflow)

Vertex AI

Data Catalog

Cloud DLP

IAM & Security

Streaming vs Batch Processing

Data Pipeline Design

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide