Databricks-Machine-Learning-Professional Web-Based Practice Exam Questions

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

Topic	Details
Topic 1	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 2	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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	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 7	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 8	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 10	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 11	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q62-Q67):

NEW QUESTION # 62
A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
They are using the following code block:

Which change does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace list with delete in the endpoint URL
B. Replace DELETE with POST in the call to http_request
C. Replace list with webhooks in the endpoint URL
D. There are no necessary changes

Answer: A

Explanation:
To delete a webhook using the MLflow REST API, the correct endpoint is:
/api/2.0/mlflow/registry-webhooks/delete
In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.

NEW QUESTION # 63
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark. Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. mlflow.search_runs(exp_id)
B. There is no way to programmatically return row-level results from an MLflow Experiment.
C. spark.read.format("delta").load(exp_id)
D. spark.read.format("mlflow-experiment").load(exp_id)
E. client.list_run_infos(exp_id)

Answer: C

NEW QUESTION # 64
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. Jenson-Shannon distance
B. Two-way Chi-squared Test
C. Kolmogorov-Smirnov (KS) test
D. None of these
E. One-way Chi-squared Test

Answer: E

NEW QUESTION # 65
Which MLflow component is used to log parameters, metrics, and artifacts during model training?

A. MLflow Tracking
B. Model Registry
C. MLflow Projects
D. MLflow Models

Answer: A

Explanation:
MLflow Tracking records experiment runs including:
parameters
metrics
artifacts (models, plots, etc.)

NEW QUESTION # 66
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors allow models to be used with any library/span>
B. Built-in library-specific flavors can only be used for logging models
C. Built-in library-specific flavors allow models to be exported as library objects
D. Built-in library-specific flavors are required for model signature use

Answer: C

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

NEW QUESTION # 67
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