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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which statement describes streaming with Spark as a model deployment strategy?

A. The inference of batch processed records as soon as a Spark job is run
B. The inference of all types of records in real-time
C. The inference of batch processed records as soon as a trigger is hit
D. The inference of incrementally processed records as soon as a Spark job is run
E. The inference of incrementally processed records as soon as trigger is hit

Answer: D

NEW QUESTION # 44
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

A. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
B. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
C. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
D. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
E. The context parameter allows the user to document the performance of a model after it has been deployed

Answer: C

NEW QUESTION # 45
A Machine Learning Engineer wants to deploy a new change to their existing model training pipeline for a social media app. Currently, they use the Databricks Feature Store to create a training set to train their LightGBM model, which is then used in a Pandas UDF for batch inference to suggest potential friends to users. They have updated the feature engineering pipeline to include an additional feature function, which now computes the number of mutual friends a user has. Which test should they add to quickly inform them if something has broken in the test environment?

A. Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on a fake dataset.
B. Implement an integration test that runs the feature engineering pipeline in the production environment.
C. Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on live data.
D. Implement an integration test that runs the entire pipeline from feature engineering to model inference in the production environment.

Answer: A

Explanation:
A unit test targeting the new feature function provides the fastest and most reliable signal that the recent change behaves correctly. By validating the mutual friends calculation on a controlled, fake dataset in the test environment, the engineer can quickly detect logic errors without the cost, risk, or latency of running full pipeline or production-based tests.

NEW QUESTION # 46
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

A. JS does not require any manual threshold or cutoff determinations
B. All of these reasons
C. None of these reasons
D. JS is not normalized or smoothed
E. JS is more robust when working with large datasets

Answer: E

NEW QUESTION # 47
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_param
B. mlflow.log_model
C. mlflow.log_metric
D. mlflow.log_artifact
E. There is no way to store values like this.

Answer: C

NEW QUESTION # 48
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Databricks Databricks-Machine-Learning-Professional Exam Prep Material Are Available In Multiple Formats​

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

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Databricks Certified Machine Learning Professional Sample Questions (Q43-Q48):

Databricks Databricks-Machine-Learning-Professional Exam Prep Material Are Available In Multiple Formats