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Google Cloud Platform Certified Data Engineer Practice Tests

Test your Knowledge about Google Cloud Platform - Data Engineer
Understand what to study for this challenging exam
Practice materials to ensure you understand content before taking the exam

These 50 questions are meant to test you at deep level of Data Engineering with GCP.  If you do well on these questions you should feel assured your knowledge level of the following is sufficient for passing. These questions are from experience with the exam are harder.  

 Here is the what the exam questions test on

Certification Exam Guide

Section 1: Designing data processing systems

1.1 Designing flexible data representations. Considerations include:

  • future advances in data technology
  • changes to business requirements
  • awareness of current state and how to migrate the design to a future state
  • data modeling
  • tradeoffs
  • distributed systems
  • schema design

1.2 Designing data pipelines. Considerations include:

  • future advances in data technology
  • changes to business requirements
  • awareness of current state and how to migrate the design to a future state
  • data modeling
  • tradeoffs
  • system availability
  • distributed systems
  • schema design
  • common sources of error (eg. removing selection bias)

1.3 Designing data processing infrastructure. Considerations include:

  • future advances in data technology
  • changes to business requirements
  • awareness of current state, how to migrate the design to the future state
  • data modeling
  • tradeoffs
  • system availability
  • distributed systems
  • schema design
  • capacity planning
  • different types of architectures: message brokers, message queues, middleware, service-oriented

Section 2: Building and maintaining data structures and databases

2.1 Building and maintaining flexible data representations

2.2 Building and maintaining pipelines. Considerations include:

  • data cleansing
  • batch and streaming
  • transformation
  • acquire and import data
  • testing and quality control
  • connecting to new data sources

2.3 Building and maintaining processing infrastructure. Considerations include:

  • provisioning resources
  • monitoring pipelines
  • adjusting pipelines
  • testing and quality control

Section 3: Analyzing data and enabling machine learning

3.1 Analyzing data. Considerations include:

  • data collection and labeling
  • data visualization
  • dimensionality reduction
  • data cleaning/normalization
  • defining success metrics

3.2 Machine learning. Considerations include:

  • feature selection/engineering
  • algorithm selection
  • debugging a model

3.3 Machine learning model deployment. Considerations include:

  • performance/cost optimization
  • online/dynamic learning

Section 4: Modeling business processes for analysis and optimization

4.1 Mapping business requirements to data representations. Considerations include:

  • working with business users
  • gathering business requirements

4.2 Optimizing data representations, data infrastructure performance and cost. Considerations include:

  • resizing and scaling resources
  • data cleansing, distributed systems
  • high performance algorithms
  • common sources of error (eg. removing selection bias)

Section 5: Ensuring reliability

5.1 Performing quality control. Considerations include:

  • verification
  • building and running test suites
  • pipeline monitoring

5.2 Assessing, troubleshooting, and improving data representations and data processing infrastructure.

5.3 Recovering data. Considerations include:

  • planning (e.g. fault-tolerance)
  • executing (e.g., rerunning failed jobs, performing retrospective re-analysis)
  • stress testing data recovery plans and processes

Section 6: Visualizing data and advocating policy

6.1 Building (or selecting) data visualization and reporting tools. Considerations include:

  • automation
  • decision support
  • data summarization, (e.g, translation up the chain, fidelity, trackability, integrity)

6.2 Advocating policies and publishing data and reports.

Section 7: Designing for security and compliance

7.1 Designing secure data infrastructure and processes. Considerations include:

  • Identify and Access Management (IAM)
  • data security
  • penetration testing
  • Separation of Duties (SoD)
  • security control

7.2 Designing for legal compliance. Considerations include:

  • legislation (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), etc.)
  • audits

I challenge you to test your knowledge.  If you get a 70% or better you should be ready to take this exam.  Good Luck

The Cloud Tech Guy

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