Free Practice Questions for Google Professional Machine Learning Engineer Certification

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

Considerations include:

- Building applications by using ML APIs from Model Garden - Building applications by using industry-specific APIs (e.g., Document AI API, Retail API) - Implementing retrieval augmented generation (RAG) applications by using Vertex AI Agent Builder

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 by using AutoML - Configuring and debugging trained models

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]) - Ingesting different data sources (e.g., text documents) into Vertex AI for inference

Considerations include:

- Choosing the appropriate Jupyter backend on Google Cloud (e.g., Vertex AI Workbench, Colab Enterprise, notebooks on Dataproc) - Applying security best practices in Vertex AI Workbench - Using Spark kernels - Integrating code source repositories - Developing models in Vertex AI Workbench by using common frameworks (e.g., TensorFlow, PyTorch, sklearn, Spark, JAX) - Leveraging a variety of foundation and open-source models in Model Garden

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 - Evaluating generative AI solutions

Considerations include:

- Choosing ML framework and model architecture - Modeling techniques given interpretability requirements

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 - Fine-tuning foundation models (e.g., Vertex AI, Model Garden)

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)

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

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)

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)

Considerations include:

- Determining an appropriate retraining policy - Continuous integration and continuous delivery (CI/CD) model deployment (e.g., Cloud Build, Jenkins)

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

Considerations include:

- Building secure AI systems by protecting against unintentional exploitation of data or models (e.g., hacking) - Aligning with Google’s Responsible AI practices (e.g., monitoring for bias) - Assessing AI solution readiness (e.g., fairness, bias) - Model explainability on Vertex AI (e.g., Vertex AI Prediction)

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 - Monitoring for common training and serving errors