Section 1: Architecting low-code ML solutions (~12% of the exam)
1.1 Developing ML models by using BigQuery ML. Considerations include:
●  Building the appropriate BigQuery ML model (e.g., linear and binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem
●  Feature engineering or selection by using BigQuery ML
●  Generating predictions by using BigQuery ML
1.2 Building AI solutions by using ML APIs. Considerations include:
●  Building applications by using ML APIs (e.g., Cloud Vision API, Natural Language API, Cloud Speech API, Translation)
●  Building applications by using industry-specific APIs (e.g., Document AI API, Retail API)
1.3 Training models by using AutoML. Considerations include:
●  Preparing data for AutoML (e.g., feature selection, data labeling, Tabular Workflows on AutoML)
●  Using available data (e.g., tabular, text, speech, images, videos) to train custom models
●  Using AutoML for tabular data
●  Creating forecasting models using AutoML
●  Configuring and debugging trained models
Section 2: Collaborating within and across teams to manage data and models (~16% of the exam)
2.1 Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop). Considerations include:
●  Organizing different types of data (e.g., tabular, text, speech, images, videos) for efficient training
●  Managing datasets in Vertex AI
●  Data preprocessing (e.g., Dataflow, TensorFlow Extended [TFX], BigQuery)
●  Creating and consolidating features in Vertex AI Feature Store
●  Privacy implications of data usage and/or collection (e.g., handling sensitive data such as personally identifiable information [PII] and protected health information [PHI])
2.2 Model prototyping using Jupyter notebooks. Considerations include:
●  Choosing the appropriate Jupyter backend on Google Cloud (e.g., Vertex AI Workbench, notebooks on Dataproc)
●  Applying security best practices in Vertex AI Workbench
●  Using Spark kernels
●  Integration with code source repositories
●  Developing models in Vertex AI Workbench by using common frameworks (e.g., TensorFlow, PyTorch, sklearn, Spark, JAX)
2.3 Tracking and running ML experiments. Considerations include:
●  Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Vertex AI Experiments, Kubeflow Pipelines, Vertex AI TensorBoard with TensorFlow and PyTorch) given the framework
Section 3: Scaling prototypes into ML models (~18% of the exam)
3.1 Building models. Considerations include:
●  Choosing ML framework and model architecture
●  Modeling techniques given interpretability requirements
3.2 Training models. Considerations include:
●  Organizing training data (e.g., tabular, text, speech, images, videos) on Google Cloud (e.g., Cloud Storage, BigQuery)
●  Ingestion of various file types (e.g., CSV, JSON, images, Hadoop, databases) into training
●  Training using different SDKs (e.g., Vertex AI custom training, Kubeflow on Google Kubernetes Engine, AutoML, tabular workflows)
●  Using distributed training to organize reliable pipelines
●  Hyperparameter tuning
●  Troubleshooting ML model training failures
3.3 Choosing appropriate hardware for training. Considerations include:
●  Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)
●  Distributed training with TPUs and GPUs (e.g., Reduction Server on Vertex AI, Horovod)
Section 4: Serving and scaling models (~19% of the exam)
4.1 Serving models. Considerations include:
●  Batch and online inference (e.g., Vertex AI, Dataflow, BigQuery ML, Dataproc)
●  Using different frameworks (e.g., PyTorch, XGBoost) to serve models
●  Organizing a model registry
●  A/B testing different versions of a model
4.2 Scaling online model serving. Considerations include:
●  Vertex AI Feature Store
●  Vertex AI public and private endpoints
●  Choosing appropriate hardware (e.g., CPU, GPU, TPU, edge)
●  Scaling the serving backend based on the throughput (e.g., Vertex AI Prediction, containerized serving)
●  Tuning ML models for training and serving in production (e.g., simplification techniques, optimizing the ML solution for increased performance, latency, memory, throughput)
Section 5: Automating and orchestrating ML pipelines (~21% of the exam)
5.1 Developing end-to-end ML pipelines. Considerations include:
●  Data and model validation
●  Ensuring consistent data pre-processing between training and serving
●  Hosting third-party pipelines on Google Cloud (e.g., MLFlow)
●  Identifying components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
●  Orchestration framework (e.g., Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer)
●  Hybrid or multicloud strategies
●  System design with TFX components or Kubeflow DSL (e.g., Dataflow)
5.2 Automating model retraining. Considerations include:
●  Determining an appropriate retraining policy
●  Continuous integration and continuous delivery (CI/CD) model deployment (e.g., Cloud Build, Jenkins)
5.3 Tracking and auditing metadata. Considerations include:
●  Tracking and comparing model artifacts and versions (e.g., Vertex AI Experiments, Vertex ML Metadata)
●  Hooking into model and dataset versioning
●  Model and data lineage
Section 6: Monitoring ML solutions (~14% of the exam)
6.1 Identifying risks to ML solutions. Considerations include:
●  Building secure ML systems (e.g., protecting against unintentional exploitation of data or models, hacking)
●  Aligning with Google›s Responsible AI practices (e.g., biases)
●  Assessing ML solution readiness (e.g., data bias, fairness)
●  Model explainability on Vertex AI (e.g., Vertex AI Prediction)
6.2 Monitoring, testing, and troubleshooting ML solutions. Considerations include:
●  Establishing continuous evaluation metrics (e.g., Vertex AI Model Monitoring, Explainable AI)
●  Monitoring for training-serving skew
●  Monitoring for feature attribution drift
●  Monitoring model performance against baselines, simpler models, and across the time dimension
●  Common training and serving errors