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

Topic	Details
Topic 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5	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
Topic 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	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 9	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 10	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 (Q174-Q179):

NEW QUESTION # 174
A Data Scientist needs to analyze drift detection results from Databricks Lakehouse Monitoring.
The system has generated both profile metrics and drift metrics tables. The scientist needs to identify baseline drift in numerical features by comparing current data against a baseline from 6 months ago. Which combination of table columns and values indicates baseline drift in a numerical feature?

A. log_type = "BASELINE" in profile metrics table with population_stability_index > 0.2 in drift metrics table.
B. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
C. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
D. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.

Answer: C

Explanation:
Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.

NEW QUESTION # 175
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

A. DESCRIBE HISTORY
B. DESCRIBE
C. HISTORY
D. VERSION
E. TIMESTAMP

Answer: A

NEW QUESTION # 176
Which of the following is a drawback associated with using Jensen-Shannon (JS) distance for numeric feature drift detection?

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

Answer: C

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 # 177
Which of the following statements describes streaming with Spark as a model deployment strategy?

A. The inference of batch processed records as soon as a trigger is hit
B. The inference of incrementally processed records as soon as a Spark job is run
C. The inference of batch processed records as soon as a Spark job is run
D. The inference of all types of records in real-time
E. The inference of incrementally processed records as soon as trigger is hit

Answer: B

NEW QUESTION # 178
A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

A. Write basket data to a feature table and publish to the online store for low-latency access.
B. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.
C. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.
D. Store basket data in a Delta table and configure automatic feature lookup during model serving.

Answer: B

Explanation:
Real-time basket contents are request-specific and only available at inference time, making them unsuitable for precomputed feature tables or online stores. Implementing a custom pyfunc model allows the predict() method to directly consume and process the basket contents included in the request payload, enabling real-time feature engineering while keeping the logic tightly coupled with model inference in Databricks Model Serving.

NEW QUESTION # 179
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