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What Databricks ML Associate covers: Machine Learning Workflows (45%) • Feature Engineering (22%) • MLflow Tracking (10%)
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Databricks Certified Machine Learning Associate
A. Correct: AutoML because it automatically builds high-quality models with minimal code using automated feature engineering and hyperparameter tuning.
B. Incorrect: Foundation Model Fine-tuning because it involves customizing foundation models, not building them from scratch with automated tools.
C. Incorrect: Vector Search because it stores and queries embedding vectors for RAG applications, not for model training automation.
D. Incorrect: Distributed Training because it refers to examples of distributed deep learning using frameworks like Ray or DeepSpeed, not automating the creation of high-quality models.
A. Incorrect: MLflow Tracking records experiments, parameters, metrics, and artifacts to manage the complete lifecycle of model development but does not prepare data for training.
B. Correct: Data preparation involves cleaning, transforming, and organizing raw data before model training.
C. Incorrect: Model deployment involves serving models in production environments, but it does not prepare data for training.
D. Incorrect: Feature engineering transforms raw data into useful model inputs but does not involve the initial steps of data preparation.
A. Incorrect: This because having MLflow version 3 installed is not a necessary condition for using Unity Catalog Model Registry, although it enhances its functionality.
B. Correct: This because Unity Catalog must be enabled in your workspace to use the Unity Catalog Model Registry for managing machine learning models.
C. Incorrect: This because using the Workspace Model Registry (legacy) is an alternative when Unity Catalog is not available, not a necessary condition.
D. Incorrect: This because creating a new registered model is one of the actions you can perform after enabling Unity Catalog and setting up the prerequisites.
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A. Correct: Unity Catalog Models because it extends the benefits of Unity Catalog to ML models, including centralized access control and auditing.
B. Incorrect: Dedicated Compute Mode because it refers to a compute resource requirement rather than a feature for managing model lifecycle.
C. Incorrect: MLflow Tracking Server because it is used for tracking experiments but does not provide centralized access control or auditing features.
D. Incorrect: Workspace Model Registry because Databricks recommends Unity Catalog Models over the Workspace Model Registry for governing and deploying models.
A. Incorrect: "MLflow Tracking is a package delivery company." does not satisfy the stem as directly as MLflow Tracking records experiments, parameters, metrics, and artifacts. and reflects a different concept.
B. Incorrect: "Artifacts are unrelated to ML experiments." does not satisfy the stem as directly as MLflow Tracking records experiments, parameters, metrics, and artifacts. and reflects a different concept.
C. Correct: "MLflow Tracking records experiments, parameters, metrics, and artifacts." best answers the stem and aligns with the key idea behind MLflow Tracking records experiments, parameters, metrics, and artifacts.
D. Incorrect: "Experiment metrics should never be recorded." does not satisfy the stem as directly as MLflow Tracking records experiments, parameters, metrics, and artifacts. and reflects a different concept.
A. Incorrect: A model registry does more than just serve as a keyboard shortcut; it manages model versions and lifecycle stages.
B. Incorrect: Lifecycle stages directly relate to the readiness of models for deployment, indicating their status in different phases like testing or production.
C. Correct: This option accurately describes how Databricks Model Registry assists with version control and stage management.
D. Incorrect: Tracking model versions is essential for maintaining a clear history and ensuring proper deployment.
A. Correct: Using appropriate algorithms, evaluation metrics, and validation practices ensures the best possible outcome during model training.
B. Incorrect: Ignoring validation data can lead to overfitting or underfitting of the model.
C. Incorrect: Considering training as merely a storage account name misunderstands the purpose and process of machine learning training.
D. Incorrect: Algorithms and metrics play a critical role in determining the effectiveness and accuracy of a trained model.
A. Correct: MLflow Tracking is used to log parameters, metrics, and artifacts during model development, ensuring reproducibility and traceability of experiments.
B. Incorrect: Databricks Feature Store handles feature engineering tasks but does not track experiments or log parameters/metrics/artifacts.
C. Incorrect: Unity Catalog manages data lineage and metadata for tables but does not handle tracking of ML experiments.
D. Incorrect: AI Playground allows no-code development and testing of generative AI models, unrelated to experiment tracking.
A. Correct: The Databricks Feature Store ensures consistency between training and inference by handling all feature computation tasks, eliminating any discrepancies.
B. Incorrect: Automatic scaling for real-time applications is a benefit of Mosaic AI Model Serving, not the Feature Store.
C. Incorrect: Direct access to models from SQL queries is a capability of Mosaic AI Model Serving, not the Feature Store.
D. Incorrect: Integration with Unity Catalog provides governance and lineage tracking but does not directly ensure consistent feature computations.
A. Incorrect: Automatic scaling based on serverless compute because Mosaic AI Model Serving automatically scales up or down to meet demand changes, saving costs and optimizing latency performance.
B. Correct: Manual scaling based on user input because the service automatically handles scaling without requiring manual intervention.
C. Incorrect: Fixed infrastructure with no scaling capabilities because Model Serving uses serverless compute which allows for automatic scaling.
D. Incorrect: Scaling based on scheduled time intervals because Mosaic AI Model Serving scales dynamically in response to real-time demand changes.
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