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Google - PMLE Certification Exam Details, Questions and Answers

Certification Provider

Google

Exam

PMLE: Professional Machine Learning Engineer

Number of questions (in our database)

62

Updated on

25 January 2024
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Topics

Data Preparation and Feature Engineering Machine Learning Infrastructure Machine Learning Fundamentals Deployment and Productionization Model Development and Evaluation

Google's pmle: Professional Machine Learning Engineer Certification Exam

About the PMLE Certification Exam

The Professional Machine Learning Engineer certification exam is a test that assesses your ability to apply machine learning concepts and techniques in professional contexts. This exam aims to establish a standard of proficiency for machine learning engineers and serves as a benchmark for employers seeking qualified professionals in this field.

Significance of PMLE Certification

Achieving the PMLE certification demonstrates a strong understanding of machine learning concepts and the ability to apply them in practical scenarios. The certification can aid professionals in advancing their careers and expanding their job opportunities in the tech industry.

Technical Details of the Exam

The exam tests a candidate's ability to design, build, and maintain machine learning models. It also includes questions about the tools and frameworks used in machine learning, such as TensorFlow and scikit-learn.

Measured Skills

  • Understanding of Machine Learning Concepts
  • Proficiency in TensorFlow
  • Experience with scikit-learn
  • Ability to design, build, and maintain ML models
  • Knowledge of data preprocessing and feature engineering

Preparation Advice

Prepare for the PMLE certification exam by gaining hands-on experience with machine learning tools and techniques. Review the exam guide and practice problems, and consider supplementing your study with relevant courses or tutorials. Remember, the exam is designed to test your practical skills, so focus more on applying what you learn rather than just memorizing concepts.

Exam Topics

  • Data Preparation and Feature Engineering (20% - 25%)

    • Data Cleaning
    • Data Transformation
    • Feature Selection
    • Feature Extraction
    • Feature Scaling
  • Machine Learning Infrastructure (15% - 20%)

    • Cloud Computing
    • Distributed Computing
    • Data Storage and Retrieval
    • Model Serving
  • Machine Learning Fundamentals (10% - 15%)

    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
    • Model Evaluation
    • Bias-Variance Tradeoff
  • Deployment and Productionization (10% - 15%)

    • Model Deployment
    • Scalability and Performance
    • Monitoring and Maintenance
    • Ethics and Fairness
  • Model Development and Evaluation (25% - 30%)

    • Model Selection
    • Hyperparameter Tuning
    • Cross-Validation
    • Ensemble Methods
    • Model Interpretability