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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

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

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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