Databricks-Machine-Learning-Professional Formal Test & New Databricks-Machine-Learning-Professional Exam Topics

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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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	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 4	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 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	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 7	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 8	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 (Q103-Q108):

NEW QUESTION # 103
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. There is no way to accomplish this task with fs
B. fs.get_table
C. fs.read_table
D. fs.create_table
E. fs.write_table

Answer: D

NEW QUESTION # 104
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

A. Concept drift
B. Prediction drift
C. Feature drift
D. Label drift

Answer: D

Explanation:
Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.

NEW QUESTION # 105
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: D

NEW QUESTION # 106
Which of the following is an obstacle related to streaming machine learning applications?

A. None of these
B. Out-of-order data
C. All of these
D. End-to-end fault tolerance

Answer: C

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 107
Which approach is best for scoring large historical datasets?

A. Batch inference with Spark
B. Local Python script
C. Real-time REST API
D. Manual predictions

Answer: A

Explanation:
Spark allows distributed batch scoring across massive datasets.

NEW QUESTION # 108
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