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Google Cloud Professional Machine Learning Engineer Syllabus

Architecting low-code AI solutions

Building models with BigQuery ML and AutoML and building solutions with Google Cloud AI APIs and foundation models.

13%
Weight
8
Questions
13
Marks

BigQuery ML and AutoML

  • Classification, regression, forecasting and clustering in BigQuery ML
  • Feature engineering in BigQuery ML
  • Agent Platform AutoML training
  • Fine-tuning Gemini models using BigQuery

AI APIs and Foundation Models

  • Selecting models from Model Garden
  • Document AI, Vision and Translation APIs
  • Tuning Gemini, Imagen and Veo
  • Optimizing Gemini apps for cost, latency and availability

Collaborating within and across teams to manage data and models

Exploring and preprocessing data, prototyping in notebooks and tracking experiments.

16%
Weight
9
Questions
16
Marks

Data Exploration and Preprocessing

  • Organizing tabular, text and image data
  • BigQuery SQL, Dataflow and Spark preprocessing
  • Agent Platform Feature Store
  • PII handling and data privacy

Prototyping and Experiments

  • Workbench and Colab Enterprise notebooks
  • PyTorch, scikit-learn and JAX
  • Agent Platform Experiments and Kubeflow Pipelines
  • Evaluating gen AI models, including LLM-as-a-judge
  • ML Metadata lineage and versioning

Scaling prototypes into ML models

Choosing model types, training at scale and selecting training hardware.

21%
Weight
12
Questions
21
Marks

Building Models

  • Choosing ARIMA, DNN or LLM approaches
  • AutoML vs. BigQuery ML vs. custom training
  • Interpretability trade-offs
  • Deployment strategy selection

Training Models

  • Custom training and Tabular Workflows
  • Kubeflow on GKE
  • Hyperparameter tuning
  • Fine-tuning foundation models
  • Troubleshooting training failures

Training Hardware

  • CPUs vs. GPUs vs. TPUs
  • Data parallelism
  • Model parallelism
  • Distributed training

Serving and scaling models

Serving models for batch and online inference and scaling online serving.

20%
Weight
12
Questions
20
Marks

Serving Models

  • Batch and online inference on Agent Platform
  • Serving on Cloud Run and GKE
  • Prebuilt and custom containers for PyTorch and XGBoost
  • Model Registry versioning
  • A/B and canary rollouts

Scaling Online Serving

  • Serving features from Feature Store
  • Public and private endpoints
  • CPU, GPU, TPU and edge serving hardware
  • Tuning serving for production

Automating and orchestrating ML pipelines

Developing end-to-end pipelines and automating model retraining.

17%
Weight
11
Questions
17
Marks

End-to-End Pipelines

  • Agent Platform Pipelines
  • Managed Service for Apache Airflow
  • Ray for orchestration
  • Data and model validation
  • Consistent preprocessing for training and serving

Automated Retraining

  • Retraining policies
  • CI/CD/CT with Cloud Build
  • Triggering retraining from monitoring
  • Pipeline artifact tracking

Monitoring AI solutions

Identifying risks to AI solutions and monitoring, testing and troubleshooting them in production.

13%
Weight
8
Questions
13
Marks

AI Risks

  • Data exfiltration and malicious prompting
  • Safety filters and Model Armor
  • Responsible AI and bias monitoring
  • Model explainability

Monitoring and Troubleshooting

  • Model Monitoring for continuous evaluation
  • Training-serving skew
  • Data drift and concept drift
  • Feature attribution drift
  • Evaluating gen AI in production

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Learning Progress

Architecting low-code AI solutions 85%
Collaborating within and across teams to manage data and models 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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