Google Professional-Data-Engineer Value Pack (Frequently Bought Together)

Convenient to revision

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

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

Well-organized layout

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What is the duration, language, and format of Google Professional Data Engineer Exam

• Passing score: 80%
• Language: English (U.S.), Japanese, Spanish, and Portuguese
• Cost: $200
• Format: Multiple choices, multiple answers
• Number of Questions: 50-60
• Length of Examination: 120 minutes

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.

Google Professional-Data-Engineer Exam Syllabus Topics:

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

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