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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q187-Q192):

NEW QUESTION # 187
Which of the following is a drawback associated with using Jensen-Shannon (JS) distance for numeric feature drift detection?

A. None of these reasons
B. All of these reasons
C. JS is not robust when working with large datasets
D. JS requires a manual threshold or cutoff determinations

Answer: D

Explanation:
A key drawback of using Jensen-Shannon (JS) distance for numeric feature drift detection is that it requires manual threshold tuning to determine what level of divergence indicates significant drift. This makes automation and consistent interpretation challenging, especially across features or datasets with differing distributions.

NEW QUESTION # 188
A data scientist wants to examine the data in the Feature Store table table from the database dev as a Spark DataFrame. They have access to Feature Store Client fs. Which line of code can be used to gel the data from table as a Spark DataFrame?

A. fs.read_table("dev.table")
B. fs.create_table("dev.table")
C. fs.get_table("table)
D. fs.get_table("dev.table")

Answer: A

Explanation:
To read data from a Feature Store table as a Spark DataFrame, the correct method is fs.read_table("dev.table"). This retrieves the contents of the table in the dev database using the Feature Store Client (fs) and returns it as a Spark DataFrame.

NEW QUESTION # 189
Which MLflow function logs files like plots or datasets?

A. mlflow.log_metric()
B. mlflow.save_model()
C. mlflow.log_artifact()
D. mlflow.log_param()

Answer: C

Explanation:
Artifacts include:
charts
model files
datasets.

NEW QUESTION # 190
Which of the following is an obstacle related to streaming machine learning applications?

A. None of these
B. Out-of-order data
C. End-to-end fault tolerance
D. All of these

Answer: D

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

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: A

NEW QUESTION # 192
......

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