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

Professional-Machine-Learning-Engineer actual test
  • Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Updated: Sep 20, 2026
  • Q & A: 412 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

About Google Professional Machine Learning Engineer : Professional-Machine-Learning-Engineer Exam

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Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

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

Professional Machine Learning Engineer - Google Certified salary

The estimated average salary of Professional Machine Learning Engineer - Google is listed below:

  • Europe: 97,000 EURO
  • United States: 114,000 USD
  • England: 87,200 POUND
  • India: 8,580,000 INR

For more info read reference:

Google Web Services Website

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

SectionObjectives
Topic 1: ML model development- Evaluation
  • 1. Model validation strategies
    • 2. Evaluate model performance metrics
      - Model training and tuning
      • 1. Hyperparameter tuning and optimization
        • 2. Train models using TensorFlow / Vertex AI
          Topic 2: Data preparation and processing- Feature engineering
          • 1. Transform and preprocess datasets
            • 2. Feature selection and representation techniques
              - Data ingestion and pipelines
              • 1. Build data pipelines for training and serving
                • 2. Use BigQuery and data processing services
                  Topic 3: Deployment and operations- Monitoring and maintenance
                  • 1. Monitor model drift and performance
                    • 2. Retraining and lifecycle management
                      - Model deployment
                      • 1. Batch and online prediction systems
                        • 2. Deploy models using Vertex AI endpoints
                          Topic 4: ML pipeline automation and orchestration- Pipeline design
                          • 1. Use Vertex AI Pipelines
                            • 2. Build end-to-end ML pipelines
                              Topic 5: Designing ML solutions- Framing ML problems
                              • 1. Translate business problems into ML tasks
                                • 2. Define success metrics and evaluation criteria
                                  - ML architecture design
                                  • 1. Design scalable ML systems on GCP
                                    • 2. Select appropriate ML models and approaches

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