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

Section	Weight	Objectives
Topic 1: ML Ops	44%	- Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies - Automated retraining workflows
Topic 2: Model Development	44%	- Scalable ML pipelines with SparkML - Distributed training and hyperparameter tuning - Advanced MLflow usage - Feature Store and automated feature pipelines
Topic 3: Model Deployment	12%	- Deployment strategies - Model rollout and version management - Custom model serving

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Databricks Certified Machine Learning Professional Sample Questions (Q190-Q195):

NEW QUESTION # 190
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A. None of these code blocks tan accomplish the task.
B. mlflow.log_artifact(importance_path, "feature-importance.csv")
C. mlflow.log_data(importance_path, "feature-importance.csv")
D.
E.

Answer: D

NEW QUESTION # 191
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

Answer: B

NEW QUESTION # 192
Which MLflow feature helps reproduce training runs?

A. Model Serving
B. MLflow Tracking
C. MLflow Projects
D. Delta Sharing

Answer: B

Explanation:
Tracking logs:
parameters
code version
metrics
artifacts
making runs reproducible.

NEW QUESTION # 193
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console. Which of the following lines of code can they use to accomplish this task?

A. mlflow.sklearn.autolog()
B. mlflow.spark.autolog()
C. mlflow.autolog()
D. spark.conf.set("autologging", True)
E. It is not possible to automatically log MLflow runs.

Answer: D

NEW QUESTION # 194
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

A. Starting a manual child run within the objective function
B. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
C. Startinq a manual parent run before calling fmin
D. Ensuring that a built-in model flavor is used for the model logging
E. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt

Answer: C

NEW QUESTION # 195
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