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Download Google : Professional-Machine-Learning-Engineer Questions & Answers as PDF & Test Software

Updated: Oct 04, 2026

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Google Professional-Machine-Learning-Engineer Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Professional Machine Learning Engineer Certification Exam
Exam Number:Professional-Machine-Learning-Engineer
Available Languages:English, Japanese
Exam Duration:120 minutes
Exam Price:$200 USD (plus tax where applicable)
Real Exam Qty:50-60
Certificate Validity Period:2 years
Exam Format:Multiple select, Multiple choice
Related Certifications:Google Cloud Professional Data Engineer
Google Cloud Professional Cloud Architect
Passing Score:Not officially published, approximately 70%
Recommended Training:Official Exam Guide
Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path
Exam Registration:Google Cloud Certification Registration
Sample Questions:Google Professional-Machine-Learning-Engineer Sample Questions
Exam Way:Online-proctored remote exam or onsite-proctored exam at authorized test centers
Pre Condition:No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions
Official Syllabus URL:https://cloud.google.com/learn/certification/machine-learning-engineer

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

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

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

The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

  • More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
  • 87% of Google Cloud certified individuals are more confident about their cloud skills
  • Professional Cloud Architect was the highest paying certification of 2020 and 2019

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

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

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