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Google Professional-Data-Engineer exam : Google Certified Professional Data Engineer Exam

Professional-Data-Engineer Exam Questions
  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Aug 06, 2026
  • Q & A: 433 Questions and Answers
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About Google Professional-Data-Engineer Exam Guide

Google Professional Data Engineer exam covers a wide range of topics, including the understanding of the Google Cloud Platform for storing, processing, and analyzing data, designing data processing systems, data modeling, data security, and compliance. Additionally, the exam tests the candidate's knowledge of implementing data pipelines, data transformation and processing, and machine learning models on the Google Cloud Platform. Passing Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge required to design and build data processing systems that meet business requirements and scale efficiently on the Google Cloud Platform.

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Google Professional-Data-Engineer exam is a comprehensive assessment that requires extensive preparation and study. It consists of 50 multiple-choice questions that need to be answered within two hours. Professional-Data-Engineer exam fee is $200, and it can be taken online or at a testing center. Professional-Data-Engineer exam is available in English, Japanese, Spanish, and Portuguese languages.

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To prepare for the Google Professional-Data-Engineer certification exam, candidates must have a solid understanding of data engineering fundamentals, as well as knowledge of the Google Cloud Platform and its associated services. Candidates can use a variety of study resources, such as official Google Cloud Platform documentation, online courses, and practice exams, to prepare for the exam. Additionally, candidates should have experience working with data processing systems, data warehousing, and data analysis tools.

Reference: https://cloud.google.com/certification/data-engineer

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Workflow orchestration
  • 3. Scheduling jobs
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. AI data enrichment
  • 2. Integrating with new data sources
  • 3. Data acquisition and import
  • 4. Batch processing
  • 5. Processing logic
  • 6. Streaming (e.g., windowing, late arriving data)
- Designing data processing resources
  • 1. Cluster sizing and autoscaling
  • 2. Cost optimization
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
Storing the data (~20% of the exam)20%- Designing for a data platform
  • 1. Building a federated governance model for distributed data systems
  • 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Planning for storage costs and performance
  • 3. Analyzing data access patterns
- Planning for using a data warehouse
  • 1. Designing the data model
  • 2. Defining architecture to support data access patterns
  • 3. Mapping business requirements
  • 4. Deciding the degree of data normalization
Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Identity and Access Management (IAM)
  • 3. Data encryption

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