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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	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 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 6	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift

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Databricks Certified Machine Learning Professional Sample Questions (Q122-Q127):

NEW QUESTION # 122
Feature drift occurs when there is a change in which element?

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

Answer: D

Explanation:
Feature drift refers to a change in the distribution of one or more input features over time, even if the target variable and model relationship remain stable. This can degrade model performance because the model was trained on a different feature distribution than it encounters during inference.

NEW QUESTION # 123
A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?

A. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.
B. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.
C. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
D. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.

Answer: B

Explanation:
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

NEW QUESTION # 124
What does an Estimator do in Spark ML?

A. Trains a model using data
B. Evaluates model performance
C. Transforms a dataset
D. Stores features

Answer: A

Explanation:
An Estimator learns from data and produces a Transformer.

NEW QUESTION # 125
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Model Registry
B. Models
C. Experiments
D. Feature Store
E. Model Serving

Answer: E

NEW QUESTION # 126
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 of the following code blocks will accomplish this task inside of an existing MLflow run block?
A)

B)

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

A. Option B
B. Option D
C. Option C
D. Option E
E. Option A

Answer: E

NEW QUESTION # 127
......

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