• Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Certification Provider: Google
  • Corresponding Certification:Google Cloud Certified
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To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:

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  • Step 2: Read the instruction carefully
  • Step 3: Follow the given steps
  • Step 4: Apply for the Professional Machine Learning Engineer Exam

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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:

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

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

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

SectionWeightObjectives
Train and deploy models20%- Use Vertex AI deployment features and infrastructure
- Implement generative AI deployment patterns
- Deploy models for online, batch, and streaming prediction
- Configure training jobs and environments
Monitor and optimize AI solutions16%- Monitor model performance, fairness, and drift
- Troubleshoot and maintain production systems
- Monitor data quality and pipeline health
- Optimize cost, latency, and resource usage
Scale prototypes into AI models18%- Work with foundation models and generative AI techniques
- Select appropriate model architectures and frameworks
- Optimize model performance and generalization
- Design and run experiments
Automate and orchestrate ML pipelines18%- Design end-to-end ML workflows
- Implement CI/CD for ML systems
- Use Vertex AI Pipelines, TFX, and other orchestration tools
- Automate retraining and model updates
Architect low-code AI solutions12%- Identify use cases for low-code/no-code AI tools
- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder
- Apply responsible AI principles to low-code designs
Collaborate to manage data and models16%- Manage datasets and features in Vertex AI
- Organize and prepare enterprise data
  • 1. Work with structured, unstructured, and semi-structured data
    • 2. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools
      - Address data privacy, compliance, and governance

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