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
Exam Details
The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.
Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Ensuring privacy (e.g., Data Loss Prevention API)
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Ensuring scalability and efficiency
- Ensuring flexibility and portability
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Ensuring reliability and fidelity
- Choosing between ACID, idempotent, eventually consistent requirements
- Pipeline monitoring (e.g., Stackdriver)
- Mapping to current and future business requirements
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Data staging, cataloging, and discovery
- Verification and monitoring
- Resizing and autoscaling resources
- Designing for security and compliance
- Building and running test suites
- Identity and access management (e.g.,Cloud IAM)
- Data security (encryption, key management)
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
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Ensuring Solution Quality
The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
- 1. Optimizing queries and workloads
- 2. Diagnosing performance issues
- Testing and validating data systems
- 1. Performance and scalability testing
- 2. Data quality validation
|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
- 1. Optimizing model performance and cost
- 2. Model serving and monitoring
- Preparing data for ML
- 1. Handling structured and unstructured data
- 2. Feature engineering and data preparation
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
- 1. Monitoring and logging data processes
- 2. Managing infrastructure and resources
- Building data pipelines
- 1. Ingesting data from various sources
- 2. Orchestrating data workflows
- 3. Transforming and cleaning data
|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
- 1. Implementing access control and data protection
- 2. Ensuring data privacy and compliance
- Designing for business requirements
- 1. Selecting appropriate storage solutions
- 2. Designing for reliability and fault tolerance
- 3. Designing for scalability and elasticity
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
- 1. Automating deployment and updates
- 2. Implementing CI/CD for data systems
- Resource optimization
- 1. Choosing appropriate compute and storage options
- 2. Cost management and resource allocation
|