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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	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 6	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 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 11	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a run with run ID run_id for the experiment with experiment ID exp_id, the data scientist wants to programmatically return the logged metrics for run_id. They have an active MLflow Client client and an active Spark session spark. Which lines of code can be used to return the logged metrics for run_id?

A.
B. client.qet_run(exp_id.run_id).data.metrics
C. client.get_run(run_id).data.metrics
D. spark.read.format("mlflow-run").load(run_id)

Answer: C

Explanation:
The correct way to retrieve logged metrics for a specific run using the MLflow Client is client.get_run(run_id).data.metrics. This returns a dictionary of all metrics logged for that run. The method in the image (mlflow.search_runs(...)) is for querying across multiple runs, not for accessing a specific run's metrics.

NEW QUESTION # 51
A Machine Learning Engineer has trained a credit scoring model and needs to evaluate fairness metrics across different customer segments while maintaining different levels of granularity for business reporting. They need to compute metrics like precision, recall, and demographic parity at the individual feature level (credit_score_range, income_bracket) as well as intersectional slices (combinations of features). The model outputs are stored in a Delta table with prediction probabilities and actual default labels. The engineer wants to systematically evaluate model performance across these various feature slices and granularities. Which approach will do this?

A. Use Unity Catalog metric views with dimensions defined for each feature and measures for the fairness metrics.
B. Create Lakehouse Monitoring with slicing expressions for individual features and intersection conditions.
C. Build separate Spark DataFrames for each slice and compute metrics using standard DataFrame operations.
D. Implement custom MLflow evaluation functions that iterate through all possible feature combinations.

Answer: B

Explanation:
Lakehouse Monitoring supports defining slicing expressions on one or more columns, allowing metrics such as precision, recall, and fairness indicators to be computed at both individual feature levels and intersectional combinations. This provides a systematic, scalable, and governed way to evaluate model performance and fairness across multiple granularities directly from Delta tables without custom metric pipelines.

NEW QUESTION # 52
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. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.
B. Write basket data to a feature table and publish to the online store for low-latency access.
C. Store basket data in a Delta table and configure automatic feature lookup during model serving.
D. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.

Answer: A

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 # 53
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. Two-way Chi-squared Test
B. None of these
C. One-way Chi-squared Test
D. Jenson-Shannon distance
E. Kolmogorov-Smirnov (KS) test

Answer: C

NEW QUESTION # 54
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?

A. MLflow Client
B. Databricks REST APIs
C. MLflow APIs
D. Jobs cannot be created programmatically
E. AutoML APIs

Answer: B

NEW QUESTION # 55
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