Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 101 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 101

Single answer

You are training a machine learning model and want to manually log metrics, artifacts, and the trained model to MLflow using a single run. Which of the following code snippets correctly logs these components to an MLflow run?

  1. A

    mlflow.start_run(); mlflow.log_metric('accuracy', 0.95); mlflow.log_artifact('model_description.txt'); mlflow.sklearn.log_model(model, 'model'); mlflow.end_run()

  2. B

    with mlflow.start_run(): mlflow.log_metric('accuracy', 0.95); mlflow.log_artifact('model_description.txt'); mlflow.sklearn.log_model(model, 'model')

  3. C

    mlflow.start_run(): mlflow.log_metric('accuracy', 0.95); mlflow.log_artifact('model_description.txt'); mlflow.sklearn.log_model(model, 'model')

  4. D

    mlflow.start_run(); mlflow.log_metric('accuracy', 0.95); mlflow.log_artifact('model_description.txt'); mlflow.sklearn.log_model(model, 'model')

Show answer and explanation

Correct answer: B

Explanation

The with mlflow.start_run() context manager is the recommended and simplest way to manage an MLflow run. It ensures that the run is properly started and ended, avoiding any potential issues with forgetting to end the run manually. Within the with block, you can log metrics, artifacts, and models to the MLflow tracking server. Other approaches either have syntax errors or require additional steps to manage the run lifecycle.

  • A. Incorrect.

    This option is invalid because mlflow.end_run() is not required when using the with statement. Additionally, using mlflow.start_run() without the with statement requires additional management of ending the run.

  • B. Correct.

    This option is correct because it uses the with mlflow.start_run() context manager, which automatically starts and ends the MLflow run. It correctly logs the metric, artifact, and model within the same run.

  • C. Incorrect.

    This option is invalid because the syntax mlflow.start_run(): is incorrect. A colon cannot be used with a function call.

  • D. Incorrect.

    This option is invalid because it does not use the with statement or call mlflow.end_run() to properly manage the lifecycle of the MLflow run.

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