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Last Updated: Sep 11, 2026

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

TopicDetails
Topic 1
  • Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter Notebooks to model prototypes. Lastly, it discusses tracking and running ML experiments.
Topic 2
  • Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
Topic 3
  • Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.
Topic 4
  • Architecting low-code AI solutions: This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.
Topic 5
  • Automating and orchestrating ML pipelines: This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.

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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

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

  • Hardware accelerators
  • Build a model
  • Transfer learning
  • Retraining/redeployment evaluation
  • Model explainability on Cloud AI Platform
  • Distributed training
  • Model performance against baselines, simpler models, and across the time dimension
  • Choice of framework and model
  • Scale model training and serving
  • Modeling techniques given interpretability requirements
  • Model generalization
  • Training a model as a job in different environments
  • Unit tests for model training and serving
  • Tracking metrics during training
  • Productionizing
  • Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
  • Overfitting

Career Bonuses

The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.

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