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Google Cloud Exam Syllabus

Professional Machine Learning Engineer syllabus, skills measured, and exam topics

A Professional Machine Learning (ML) Engineer builds, evaluates, productionizes, and optimizes AI solutions using Google Cloud capabilities and knowledge of conventional ML approaches. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The

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Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
Section 1: Architecting low-code AI solutions 13%
Section 3: Scaling prototypes into ML models 21%
Section 4: Serving and scaling models 20%
Section 5: Automating and orchestrating ML pipelines 18%
Section 6: Monitoring AI solutions 13%

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

Section 1: Architecting low-code AI solutions (~13% of the exam)

  • 1.1 Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform.
  • Considerations include:
  • Building models in BigQuery ML or Agent Platform AutoML (e.g., classification,
  • regression, forecasting, and clustering) based on the business problem
  • Performing feature engineering or selection using BigQuery ML
  • Generating predictions using BigQuery ML
  • Training models using Agent Platform AutoML
  • Fine-tuning Gemini models using BigQuery
  • 1. 2 Building AI solutions using Google Cloud AI APIs or foundational models. Considerations
  • include:
  • Evaluating and selecting the appropriate model for a given task from Gemini Enterprise
  • Agent Platform Model Garden

Section 2: Collaborating within and across teams to manage data and models

  • (~16% of the exam)
  • 2.1 Exploring and preprocessing data for ML. Considerations include:
  • Organizing and exploring different data types (e.g., tabular, text, and images) for
  • efficient experimenting, training, and serving
  • Choosing the right tool for data preprocessing based on scale and complexity (e.g.,
  • BigQuery [SQL], Dataflow, Apache Spark, and in-memory Python frameworks)
  • Creating and consolidating features in Gemini Enterprise Agent Platform Feature Store
  • Ensuring data privacy and handling sensitive information (e.g., personally identifiable
  • information [PII])
  • 2.2 Model prototyping using notebooks (e.g., Gemini Enterprise Agent Platform Workbench and
  • Colab Enterprise). Considerations include:
  • Applying collaboration and security best practices when setting up and running

Section 3: Scaling prototypes into ML models (~21% of the exam)

  • 3.1 Building models given the task considering cost, complexity, latency, and scalability.
  • Considerations include:
  • Choosing the model type (e.g., ARIMA, DNN, and LLM)
  • Choosing the product (e.g., Agent Platform AutoML, BigQuery ML, and Agent Platform
  • Pipelines)
  • Choosing the deployment strategy
  • Modeling techniques given interpretability requirements
  • 3.2 Training models. Considerations include:
  • Organizing training data (e.g., tabular, text, speech, images, and videos) on Google
  • Cloud (e.g., Cloud Storage and BigQuery)
  • Ingesting structured and unstructured data from various sources into training pipelines
  • Model training using different software development kits (SDKs) (e.g., Agent Platform

Section 4: Serving and scaling models (~20% of the exam)

  • 4.1 Serving models. Considerations include:
  • Deploying models for batch and online inference using appropriate services (e.g., Agent
  • Platform, Model Garden, Cloud Run, and GKE)
  • Packaging and serving models from different frameworks (e.g., PyTorch and XGBoost)
  • using prebuilt and custom containers
  • Organizing and versioning models in Gemini Enterprise Agent Platform Model Registry
  • Implementing model rollout strategies (e.g., A/B testing and canary deployments) to
  • compare model versions
  • Developing solutions for inference preprocessing and postprocessing
  • 4.2 Scaling online model serving. Considerations include:
  • Managing and serving features using Agent Platform Feature Store
  • Deploying models to public and private endpoints

Section 5: Automating and orchestrating ML pipelines (~18% of the exam)

  • 5.1 Developing end-to-end ML pipelines. Considerations include:
  • Validating data and models
  • Building and orchestrating pipelines using managed or unmanaged services and from
  • templates or custom solutions (e.g., Agent Platform Pipelines, Managed Service for
  • Apache Airflow, and Ray on Gemini Enterprise Agent Platform)
  • Ensuring consistent data preprocessing between training and serving
  • 5.2 Automating model retraining. Considerations include:
  • Determining an appropriate retraining policy
  • Deploying models in continuous integration, continuous delivery, and continuous
  • training (CI/CD/CT) pipelines (e.g., Cloud Build)

Section 6: Monitoring AI solutions (~13% of the exam)

  • 6.1 Identifying risks to AI solutions. Considerations include:
  • Building secure AI systems by protecting against unintentional exploitation and leaks of
  • data or models (e.g., data exfiltration, malicious prompting, and sharing sensitive data
  • with LLMs) using the appropriate security tool (e.g., Regex, safety filters, and Model
  • Aligning with responsible AI practices (e.g., monitoring for bias)
  • Model explainability on Agent Platform (e.g., Agent Platform Inference)
  • 6.2 Monitoring, testing, and troubleshooting AI solutions. Considerations include:
  • Configuring and using Model Monitoring on Gemini Enterprise Agent Platform to
  • establish continuous evaluation metrics for production models
  • Monitoring for common issues (e.g., training-serving skew, data drift, concept drift, and
  • feature attribution drift)
  • Monitoring, testing, and evaluating gen AI solutions