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Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:

• ML Model Development
• ML Solution Architecture
• ML Pipeline Automation & Orchestration
• Data Preparation and Processing
• ML Solution Monitoring, Optimization, and Maintenance
• ML Problem Framing

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Automation
• Data connections
• Selection of quotas and compute/accelerators with components
• Serving
• Building secure ML systems
• Automation of data preparation and model training/deployment
• Logging/management
• Feature engineering
• A variety of component types - data collection; data management
• Exploration/analysis
• Choose appropriate Google Cloud hardware components
• Identifying potential regulatory issues
• Optimizing data use and storage
• Design reliable, scalable, highly available ML solution
• Design architecture that complies with regulatory and security concerns
• Monitoring
• SDLC best practices
• Choose appropriate Google Cloud software components
• Privacy implications of data usage

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

Design pipeline. Considerations include:

• Google Cloud serving options
• Use CI/CD to test and deploy models
• Hybrid or multi-cloud strategies
• Organization and tracking experiments and pipeline runs
• Tuning compute performance
• A/B and canary testing
• Identification of components, parameters, triggers, and compute needs
• Track and audit metadata
• Storing data and generated artifacts
• Setup of trigger and pipeline schedule
• Hooking models into existing CI/CD deployment system
• Decoupling components with Cloud Build
• Implement serving pipeline
• Implement training pipeline
• Model binary options
• Testing for target performance
• Hooking into model and dataset versioning
• Performing data validation
• Orchestration framework
• Constructing and testing of parameterized pipeline definition in SDK
• Model/dataset lineage

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What is the duration, language, and format of Professional Machine Learning Engineer - Google

• Language of Exam: English, Japanese, Korean
• Duration of Exam: 120 minutes
• No negative marking for wrong answers
• Type of Questions: Multiple choice (MCQs), multiple answers

Google Professional-Machine-Learning-Engineer Exam Syllabus Topics: