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

  • Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Updated: Aug 03, 2026
  • Q & A: 350 Questions and Answers

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Professional Machine Learning Engineer - Google Certification Path

The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.

Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.

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

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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:

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

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

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

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