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What is the duration, language, and format of Google Professional Data Engineer Exam

Format: Multiple choices, multiple answers
Length of Examination: 120 minutes
Number of Questions: 50-60
Language: English (U.S.), Japanese, Spanish, and Portuguese
Cost: $200
Passing score: 80%

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

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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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Google Professional-Data-Engineer Exam Syllabus Topics:

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