GCP-PDE Practice Tests

Google Cloud Professional Data Engineer

Master Google Cloud data engineering with our comprehensive practice tests. Get exam-ready with realistic questions, detailed explanations, and AI-powered feedback on all five exam domains.

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Duration

2 hours

Questions

40-50 questions

Cost

$200 USD
Where to register
Google Cloud

Issued by Google Cloud. Delivered via Kryterion (online-proctored or onsite). $200 USD standard exam. Designed for engineers with 3+ years industry experience and 1+ year designing data solutions on Google Cloud.

Register on cp.certmetrics.com
01·Overview

Certification overview

The format, prerequisites, and what to expect on exam day.

Exam details

  • Duration

    2 hours

  • Questions

    40-50 questions

  • Format

    Multiple choice and multiple select

  • Passing Score

    Not disclosed

  • Cost

    $200 USD

  • Validity

    2 years

  • Languages

    English, Japanese

Prerequisites
  • 3+ years of industry experience
  • 1+ years designing and managing data solutions on Google Cloud
  • Understanding of data warehousing and ETL/ELT patterns
  • Experience with cloud-based data processing
  • Knowledge of SQL and data manipulation
  • Familiarity with DevOps and continuous integration practices
02·Domains

Exam domains

Topics on the official blueprint, with their relative weight.

01
Design data processing systems
Weight not published
  • Designing for fairness, reliability, and scalability
  • Data pipeline architecture patterns
  • Choosing appropriate processing technologies
  • Estimating compute and storage requirements
  • Disaster recovery and business continuity planning
02
Ingest and process the data
Weight not published
  • Batch and stream data ingestion patterns
  • Data validation and error handling
  • Data transformation and enrichment
  • Real-time processing with Apache Beam and Dataflow
  • Pub/Sub for event streaming
03
Store the data
Weight not published
  • Selecting and designing storage solutions
  • BigQuery architecture and optimization
  • Cloud Storage design for analytics
  • Firestore and Cloud Spanner use cases
  • Data lifecycle and retention policies
04
Prepare and use data for analysis
Weight not published
  • Data modeling for analytics
  • Query optimization and performance tuning
  • Creating analysis-ready datasets
  • Data governance and quality assurance
  • Integrating with BI and analytics tools
05
Maintain and automate data workloads
Weight not published
  • Infrastructure as Code for data pipelines
  • Monitoring and alerting for data systems
  • Cost optimization strategies
  • Troubleshooting data pipeline failures
  • Automated testing and deployment
03·Key topics

What you actually study

Service families and concept clusters that show up across questions.

Big Data Processing

  • BigQuery for data warehousing and analytics
  • Dataflow for Apache Beam pipelines
  • Dataproc for Hadoop and Spark jobs
  • Batch and streaming processing patterns
  • Query optimization and cost control

Data Ingestion

  • Pub/Sub for event streaming
  • Cloud Storage for data lakes
  • Database Migration Service
  • Dataflow for ETL/ELT
  • API-based data collection

Databases & Storage

  • Cloud Storage object lifecycle
  • Cloud SQL relational databases
  • Firestore for NoSQL
  • Cloud Spanner for global transactions
  • BigTable for time-series data

Security & Governance

  • Identity and Access Management (IAM)
  • Data encryption at rest and in transit
  • Data loss prevention and masking
  • Audit logging and compliance
  • Network security and VPC design

Monitoring & Operations

  • Cloud Monitoring for data pipelines
  • Cloud Logging for troubleshooting
  • Error handling and retry logic
  • Performance diagnostics
  • Cost analysis and optimization

ML & Advanced Analytics

  • Vertex AI for ML pipelines
  • BigQuery ML for model training
  • Feature engineering patterns
  • Time-series forecasting
  • Anomaly detection in data pipelines
04·Study tips

How to actually pass it

Practical strategies for the weeks before, and the morning of.

Preparation strategy
  • Complete the Google Cloud Data Engineer Learning Path on Cloud Skills Boost
  • Build hands-on experience with BigQuery, Dataflow, and Pub/Sub in a real project
  • Study the official exam guide and focus on all five domain areas equally
  • Practice data pipeline design in the Google Cloud Console with sample datasets
  • Review case studies showing multi-stage data architectures end-to-end
  • Take timed practice exams weekly and review mistakes
  • Join the Google Cloud community forums to learn from others preparing for the exam
Exam day
  • Read each question carefully, paying attention to scalability and cost implications
  • Understand tradeoffs between batch and stream processing for different use cases
  • Remember BigQuery and Dataflow are core to most correct answers
  • Watch for gotchas around data consistency, error handling, and security
  • Design for automation and minimize manual intervention in all scenarios
  • Consider cost optimization alongside functional requirements
  • Verify your answer covers all requirements, not just the primary objective

Pass the GCP Data Engineer on the first attempt.

Master data pipeline design, BigQuery optimization, and stream processing across all five exam domains. Start free, no card required.

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