To be eligible for the Google Professional-Data-Engineer exam, candidates must have experience in data engineering, data analytics, and data warehousing. They must also have experience in designing and implementing solutions using Google Cloud Platform's data processing technologies, such as Cloud Dataflow, BigQuery, and Cloud Dataproc. Furthermore, candidates must have excellent knowledge of SQL, Python, and Java programming languages, as well as experience in data modeling and data visualization.

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

The Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate’s ability to design and implement data processing systems, as well as manage data pipelines, data storage, and data security on Google Cloud Platform. Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions, which require candidates to demonstrate their knowledge of various topics such as data modeling, data warehousing, data visualization, and machine learning.

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Google Professional-Data-Engineer certification exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Storing the data (~20% of the exam)	20%	- 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 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. Mapping business requirements 3. Defining architecture to support data access patterns 4. Designing the data model - Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cluster sizing and autoscaling 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cost optimization - 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 pipelines 1. Batch processing 2. Streaming (e.g., windowing, late arriving data) 3. AI data enrichment 4. Processing logic 5. Integrating with new data sources 6. Data acquisition and import
Topic 3: Preparing and using data for analysis (~15% of the exam)	15%	- Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools - Sharing data securely 1. Data sharing and collaboration 2. Publishing datasets
Topic 4: Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Planning for monitoring and alerting 2. Recovering from failures 3. Performing data quality and validation checks - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Workflow orchestration 2. Continuous integration and continuous deployment (CI/CD) 3. Scheduling jobs
Topic 5: 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. Planning for analytical and operational use cases 2. Defining data lifecycle