Ensure Solution Quality

Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.

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.

Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cluster sizing and autoscaling 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. Integrating with new data sources 2. Processing logic 3. Data acquisition and import 4. Batch processing 5. AI data enrichment 6. Streaming (e.g., windowing, late arriving data)
Topic 3: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Data encryption 2. Auditing, privacy, and compliance 3. Identity and Access Management (IAM) - 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
Topic 4: Storing the data (~20% of the exam)	20%	- Using a data lake 1. Processing data 2. Monitoring the data lake 3. Managing the lake (data discovery, access, cost controls) - Planning for using a data warehouse 1. Deciding the degree of data normalization 2. Defining architecture to support data access patterns 3. Mapping business requirements 4. Designing the data model - Selecting storage systems 1. Lifecycle management of data 2. Planning for storage costs and performance 3. Analyzing data access patterns - 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 5: Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Recovering from failures 3. Planning for monitoring and alerting - Automating data processes 1. Workflow orchestration 2. Scheduling jobs 3. Continuous integration and continuous deployment (CI/CD) - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage