Curious about Actual Databricks Certified Machine Learning Associate Exam Questions?

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

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

Correct : A

The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.

Databricks documentation on Git integration: Databricks Repos

Correct : B

To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach is more efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.

Databricks documentation on Jobs: Jobs in Databricks

Correct : C

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.

Databricks documentation on Random Forest: Random Forest in Spark ML