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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
A Machine Learning Engineer is using Lakehouse Monitoring to track the performance of ML models deployed in their environment. They want to monitor significant distributional drift in categorical features with a metric bounded on [0,1] for easy interpretation. Which statistical method should they use?

A. Kolmogorov-Smirnov Test
B. Wasserstein Distance
C. Kullback-Leibler Distance
D. Jensen-Shannon Distance

Answer: D

NEW QUESTION # 19
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
B.
C.
D.
E.

Answer: C

NEW QUESTION # 20
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A. Bin-packing
B. Data skipping
C. Tuning the file size
D. Write as a Parquet file
E. Z-Ordering

Answer: C

NEW QUESTION # 21
Why are Delta tables often used to store machine learning features?

A. They allow faster GPU training
B. They support schema enforcement and time travel
C. They reduce model size
D. They replace Spark DataFrames

Answer: B

Explanation:
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.

NEW QUESTION # 22
Concept drift is when there is a change in which element?

A. The distribution of an input variable
B. The relationship between input variables and target variables
C. The distribution of a target variable
D. The distribution of the predicted target given by the model

Answer: B

Explanation:
Concept drift occurs when the underlying relationship between input features and the target variable changes over time. This means that even if the feature and label distributions remain stable, the way features influence the target (the predictive relationship) has shifted, leading to reduced model accuracy unless retraining occurs.

NEW QUESTION # 23
......

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