Free Certification Practice Questions

Validates the ability to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.

Validates the ability to build, evaluate, productionize, optimize, and monitor AI and machine learning solutions on Google Cloud, including low-code AI, data and model collaboration, scalable model serving, ML pipelines, generative AI, and responsible AI practices.

Validates the ability to design, implement, and operate AI operations solutions on Azure by building MLOps infrastructure with Azure Machine Learning, managing model training, deployment, and monitoring lifecycles, implementing GenAIOps infrastructure with Microsoft Foundry, and optimizing generative AI quality, observability, and model performance by using GitHub Actions, Bicep, and Azure CLI.

Validates expertise in designing, implementing, deploying, and maintaining machine learning solutions using AWS Cloud services and ML concepts, including data engineering, exploratory data analysis, modeling, and ML operations.

Validates foundational machine learning skills on the Databricks Data Intelligence Platform. Covers Databricks ML capabilities including AutoML, Unity Catalog for ML governance, and core MLflow features such as experiment tracking, the Model Registry, and model serving. Assesses exploratory data analysis, feature engineering, model training, hyperparameter tuning, model evaluation and selection, and model deployment patterns. All machine learning code in this exam is in Python; data manipulation tasks may use SQL.

Assesses the ability to design, implement, and manage enterprise-scale machine learning solutions using advanced Databricks platform capabilities. Covers building scalable ML pipelines with SparkML, implementing distributed training and hyperparameter tuning, leveraging advanced MLflow features, and utilizing Feature Store concepts for automated feature pipelines. Evaluates MLOps practices including testing strategies, environment management with Databricks Asset Bundles, automated retraining workflows, and monitoring using Lakehouse Monitoring for drift detection. Assesses deployment strategies, custom model serving, and model rollout management. All machine learning code in this exam is in Python.