Preparing for the Google Cloud Professional Data Engineer Examination (GC-PGCPDEE)
This course will help you prepare for Google Cloud's Professional Data Engineer certification exam. This session uses lectures, quizzes, and discussions to help you become familiar with the structure and format of the exam. You'll take part in exam question reasoning and case comprehension, get tips from certified experts, and review topics from the Data Engineering curriculum. At the end of the course, you'll take an ungraded practice exam and then a graded practice exam that will simulate the exam-taking experience.
The training will be delivered in collaboration with PUE as Google Cloud Authorized Training Partner
- Machine Learning
To get the most out of this course, participants should:
- Be familiar with Google Cloud to the level of the Data Engineering on Google Cloud course (suggested, not required)
- Position the Professional Data Engineer Certification.
- Provide information, tips, and advice on taking the exam.
- Review each section of the exam, covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate.
- Connect candidates to appropriate target learning.
This course is intended for the following participants:
- Cloud professionals interested in taking the Data Engineer certification exam.
- Data engineering professionals interested in taking the Data Engineer certification exam
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Understanding the Professional Data Engineer Certification
- Position the Professional Data Engineer certification among the offerings.
- Distinguish between Associate and Professional.
- Provide guidance between Professional Data Engineer and Associate Cloud Engineer.
- Describe how the exam is administered and the exam rules.
- Provide general advice about taking the exam.
Module 2: Designing Data Processing Systems
- Designing data processing systems.
- Designing flexible data representations.
- Designing data pipelines.
- Designing data processing infrastructure.
Module 3: Building and Operationalizing Data Processing Systems
- Building and operationalizing data structures and databases.
- Building and operationalizing flexible data representations.
- Building and operationalizing pipelines.
- Building and operationalizing processing infrastructure.
Module 4: Operationalizing Machine Learning Models
- Analyzing data and enabling machine learning.
- Deploying an ML pipeline.
- Machine learning terminology review.
- Operationalizing Machine Learning Models: Exam Guide Review.
- Modeling business processes for analysis and optimization.
Module 5: Security, Policy, and Reliability
- Designing for security and compliance.
- Performing quality control.
- Ensuring reliability.
- Visualizing data and advocating policy.
- Ensuring Solution Quality: Exam Guide Review.
Module 6: Resources and Next Steps
- Preparation Resources.