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

Topic	Details
Topic 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
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. One-way Chi-squared Test
B. Jenson-Shannon distance
C. None of these
D. Kolmogorov-Smirnov (KS) test
E. Two-way Chi-squared Test

Answer: A

NEW QUESTION # 67
Which statement describes streaming with Spark as a model deployment strategy?

A. The inference of all types of records in real-time
B. The inference of batch processed records as soon as a Spark job is run
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 # 68
Which of the following describes concept drift?

A. Concept drift is when there is a change in the distribution of an input variable
B. Concept drift is when there is a change in the distribution of a target variable
C. Concept drift is when there is a change in the relationship between input variables and target variables
D. None of these describe Concept drift
E. Concept drift is when there is a change in the distribution of the predicted target given by the model

Answer: E

NEW QUESTION # 69
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They have created a model signature model_signature and a few example input records input_records. Which block of code can be used to log all of this information?

A.
B. There is no way to log a model signature.
C. mlflow.sklearn.log_model(model, "model")
mlflow.log_signature(model_signature)
mlflow.log_input(input_records)
D.

Answer: A

Explanation:
The code block correctly uses the mlflow.sklearn.log_model() function with both signature and input_example parameters.
signature=model_signature defines the expected model input and output schema, ensuring reproducibility and validation during inference.
input_example=input_records provides a sample input that helps users understand the model's expected input format.

NEW QUESTION # 70
What is commonly monitored in deployed ML models?

A. Notebook formatting
B. GPU usage only
C. Model drift and prediction quality
D. Code style

Answer: C

Explanation:
Production ML systems monitor:
data drift
model drift
prediction accuracy.

NEW QUESTION # 71
......

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