Learn with Google : Professional-Data-Engineer training material for 100% pass

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)
• Ensuring reliability and fidelity
• Performing data preparation and quality control (e.g., Cloud Dataprep)
• Data security (encryption, key management)
• Data staging, cataloging, and discovery
• Choosing between ACID, idempotent, eventually consistent requirements
• Ensuring scalability and efficiency
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)
• Designing for security and compliance
• Building and running test suites
• Mapping to current and future business requirements
• Verification and monitoring
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Identity and access management (e.g.,Cloud IAM)
• Resizing and autoscaling resources
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
• Ensuring flexibility and portability
• Pipeline monitoring (e.g., Stackdriver)

The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

• Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
• Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
• Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
• Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;

Google Professional-Data-Engineer Exam Overview:

Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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

Google Professional-Data-Engineer Exam Syllabus Topics: