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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Section	Weight	Objectives
ML Ops	44%	- Automated retraining workflows - Testing and validation strategies - Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring
Model Development	44%	- Advanced MLflow usage - Distributed training and hyperparameter tuning - Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines
Model Deployment	12%	- Deployment strategies - Custom model serving - Model rollout and version management

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Databricks Certified Machine Learning Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
A team is building a machine learning pipeline, which includes feature engineering, model training, model evaluation, and model deployment. Over the last year, the project has grown a lot, with multiple contributors working on it. Currently, they are manually testing their pipelines and functions. However, they are looking to build out a MLOps process and want to organize their unit tests. Which approach to organizing functions and unit tests should they take?

A. Organize functions and unit tests together in multiple notebooks, grouping them by feature or component.
B. Separate functions into dedicated modules and place all unit tests in a separate test suite or notebook.
C. Place all functions and their corresponding unit tests in a single notebook or script.
D. Write unit tests only after deployment to avoid slowing down development.

Answer: B

Explanation:
Separating production code into dedicated modules and organizing unit tests in a separate test suite follows standard software engineering and MLOps best practices. This structure improves maintainability, enables automated testing in CI/CD pipelines, supports collaboration among multiple contributors, and scales effectively as the project grows.

NEW QUESTION # 13
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

A. Concept drift
B. Label drift
C. Feature drift
D. Prediction drift

Answer: B

Explanation:
Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.

NEW QUESTION # 14
A Machine Learning Engineer needs a continuous deployment pipeline for their models hosted on Databricks Model Serving. The deployment automation should execute after a model is trained and registered using MLflow. The goal of the automation is to deploy the latest version of the model from the MLflow Model Registry only if the model can meet the company's strict latency requirements (P95 < 300ms) while serving production traffic. How can the engineer validate that new models meet their latency requirements when served in production?

A. A/B test the latest model with Databricks Model Serving so that the latest model receives 5% of production traffic and the current model receives the rest. Use inference tables to calculate P95 latency and verify it is less than 300ms.
B. Serve the latest model on Databricks Model Serving and use a load testing client to generate requests at the production request rate. Use the load testing client to calculate P95 latency and verify it is less than 300ms.
C. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the inference_latency metric is less than 300ms.
D. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the model_latency metric is less than 300ms.

Answer: A

Explanation:
Validating latency requirements must be done under real production serving conditions. Routing a small percentage of live production traffic to the new model using Databricks Model Serving allows accurate measurement of end-to-end serving latency. Inference tables capture request latency metrics, enabling calculation of the P95 latency and ensuring it meets the strict production threshold before full rollout.

NEW QUESTION # 15
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

A. Two-way Chi-squared Test
B. Kolmogorov-Smirnov (KS) test
C. None of these
D. One-way Chi-squared Test
E. Jenson-Shannon distance

Answer: D

NEW QUESTION # 16
A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which type of drift is present in the above scenario?

A. None of these
B. Concept drift
C. Feature drift
D. Label drift
E. Prediction drift

Answer: C

NEW QUESTION # 17
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