Databricks-Machine-Learning-Associate Exam Questions - Navigate Your Path to Success

The Databricks Certified Machine Learning Associate Exam (Databricks-Machine-Learning-Associate) exam is a good choice and if the candidate manages to pass Databricks Certified Machine Learning Associate Exam, he/she will earn Databricks Machine Learning Associate Certification. Below are some essential facts for Databricks Databricks-Machine-Learning-Associate exam candidates:

TrendyCerts offers 74 Questions that are based on actual Databricks-Machine-Learning-Associate syllabus.
Our Databricks-Machine-Learning-Associate Exam Practice Questions were last updated on: Mar 09, 2025

Sample Questions for Databricks-Machine-Learning-Associate Exam Preparation

Question 1

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?

AThe home page of the MLflow Model Registry

BThe experiment page in the Experiments observatory

CThe model version page in the MLflow Model Registry

DThe model page in the MLflow Model Registry

Correct : C

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

Options Selected by Other Users:

Question 2

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?

Amlflow.register_model(run_id, 'best_model')

Bmlflow.register_model(f'runs:/{run_id}/model'', 'best_model'')

Cmillow.register_model(f'runs:/{run_id)/model')

Dmlflow.register_model(f'runs:/{run_id}/best_model', 'model')

Correct : B

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