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

  • Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Updated: Oct 03, 2026     Q & A: 412 Questions and Answers

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Professional Machine Learning Engineer - Google Certified salary

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

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

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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

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

  • Identifying data sources
  • Defining business problems
  • Identifying nonML solutions
  • Defining problem type (classification, regression, clustering, etc.)
  • Success metrics
  • Defining outcome of model predictions
  • Managing incorrect results
  • Aligning with Google AI principles and practices (e.g. different biases)
  • Determination of when a model is deemed unsuccessful
  • Define ML problem
  • Defining the input (features) and predicted output format
  • Identify risks to feasibility and implementation of ML solution. Considerations include:
  • Assessing ML solution readiness
  • Assessing data readiness
  • Define business success criteria
  • Defining output use
  • Assessing and communicating business impact
  • Key results

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing

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

  • Feature selection
  • Data privacy and compliance
  • Data ingestion
  • Handling outliers
  • Data leakage and augmentation
  • Handling missing data
  • Batching and streaming data pipelines at scale
  • Transformations (TensorFlow Transform)
  • Design data pipelines
  • Feature engineering
  • Data validation
  • Database migration
  • Statistical fundamentals at scale
  • Managing large samples (TFRecords)
  • Feature crosses
  • Visualization
  • Class imbalance
  • Encoding structured data types
  • Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
  • Evaluation of data quality and feasibility
  • Build data pipelines
  • Monitoring/changing deployed pipelines
  • Data exploration (EDA)
  • Streaming data (e.g. from IoT devices)

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

SectionObjectives
Topic 1: 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 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: Designing ML solutions- ML architecture design
                  • 1. Select appropriate ML models and approaches
                    • 2. Design scalable ML systems on GCP
                      - Framing ML problems
                      • 1. Translate business problems into ML tasks
                        • 2. Define success metrics and evaluation criteria
                          Topic 4: ML model development- Model training and tuning
                          • 1. Train models using TensorFlow / Vertex AI
                            • 2. Hyperparameter tuning and optimization
                              - Evaluation
                              • 1. Evaluate model performance metrics
                                • 2. Model validation strategies
                                  Topic 5: ML pipeline automation and orchestration- Pipeline design
                                  • 1. Use Vertex AI Pipelines
                                    • 2. Build end-to-end ML pipelines

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