QUESTION: 37

A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.
They can check the Databricks Runtime ML box when creating their clusters.
They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
They can set the runtime-version variable in their Spark session to "ml".

Answer(s): C

Explanation:

The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

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QUESTION: 38

A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?
A)

B)

C)

D)

Option A
Option B
Option C
Option D

Answer(s): C

Explanation:

To find the run_id of the run with the best root-mean-square error (RMSE) in an MLflow experiment, the correct line of code to use is:
mlflow.search_runs( experiment_id, order_by=["metrics.rmse"] )["run_id"][0] This line of code searches the runs in the specified experiment, orders them by the RMSE metric in ascending order (the lower the RMSE, the better), and retrieves the run_id of the best-performing run. Option C correctly represents this logic.
Reference
MLflow documentation on tracking experiments:
https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.search_runs

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QUESTION: 39

A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry. From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

The home page of the MLflow Model Registry
The experiment page in the Experiments observatory
The model version page in the MLflow Model Registry
The model page in the MLflow Model Registry

Answer(s): C

Explanation:

The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.
Reference
Databricks documentation on MLflow Model Registry:
https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

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QUESTION: 40

A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".
Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

mlflow.register_model(run_id, "best_model")
mlflow.register_model(f"runs:/{run_id}/model", "best_model")
millow.register_model(f"runs:/{run_id)/model")
mlflow.register_model(f"runs:/{run_id}/best_model", "model")

Answer(s): B

Explanation:

To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:
mlflow.register_model(f"runs:/{run_id}/model", "best_model") This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.
Reference
MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model- registry.html#registering-a-model

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Free Databricks-Machine-Learning-Associate Exam Braindumps (page: 10)

QUESTION: 37

Explanation:

QUESTION: 38

Explanation:

QUESTION: 39

Explanation:

QUESTION: 40

Explanation: