A Machine Learning Engineer needs to deploy a production ML workflow that includes an MLflow experiment for tracking model training runs, a registered model in Unity Catalog for version management, and a model serving endpoint for real-time inference. The team requires a unified configuration approach that ensures consistent deployment across development and production environments while adhering to infrastructure-as-code best practices. Which approach should the Machine Learning Engineer use to define all three components together?

• A.Use MLflow's deployment tools to create individual deployment configurations for each component, then orchestrate them using Databricks Jobs.
• B.Use Terraform to provision the infrastructure and then manually configure each ML component through the Databricks UI.
• C.Define experiments, registered_models, and model_serving_endpoints resources in a Databricks Asset Bundle (DAB) configuration file.
• D.Create separate REST API calls for each component and run them sequentially using a shell script.

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What does an Estimator do in Spark ML?

• A.Evaluates model performance
• B.Trains a model using data
• C.Transforms a dataset
• D.Stores features

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A machine learning engineer is migrating a machine learning pipeline to use Data bricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs.
They are using the following code block as part of their solution:

Assuming that this is the first time that the model is being run, which statement describes the impact of the registered_model_name=model_name parameter?

• A.It registers the new version of the existing model_name model in the MLflow Model Registry.
• B.It registers a new model called model_name in the MLflow Model Registry.
• C.It avoids the need to specify the model name in the subsequent required call to mflow.register_model.
• D.It identifies the name of the logged model in the MLflow Experiment.

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After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

• A.DESCRIBE HISTORY
• B.TIMESTAMP
• C.HISTORY
• D.VERSION
• E.DESCRIBE

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A Machine Learning Engineer is responsible for maintaining a fraud detection model deployed on Databricks. They want to implement a retraining pipeline that automatically starts when the model's F1 score drops below a threshold or when input feature distributions change significantly.
Which two actions should the engineer take to implement this automated retraining? (Choose two.)

• A.Schedule a recurring query on the Lakehouse monitoring table.
• B.Use MLflow to manually log metrics and retrain the model offline.
• C.Set up a manual retraining schedule to run every week regardless of alerts.
• D.Configure these alerts to send webhook notifications that trigger the model training job.
• E.Use Databricks SQL to create alerts on model performance and data drift metrics stored in Delta tables.

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