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Databricks Databricks-Machine-Learning-Professional Exam Duration - Valid Databricks-Machine-Learning-Professional Exam Notes

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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 the advantages of using the pyfunc MLflow flavor
Manually log parameters, models, and evaluation metrics using MLflow

Topic 3

Identify which code block will trigger a shown webhook
Describe the basic purpose and user interactions with Model Registry

Topic 4

Identify less performant data storage as a solution for other use cases
Describe why complex business logic must be handled in streaming deployments

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

Identify the requirements for tracking nested runs
Describe an MLflow flavor and the benefits of using MLflow flavors

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

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 9

Identify JIT feature values as a need for real-time deployment
Describe how to list all webhooks and how to delete a webhook

Topic 10

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 11

Describe concept drift and its impact on model efficacy
Describe summary statistic monitoring as a simple solution for numeric feature drift

Databricks Certified Machine Learning Professional Sample Questions (Q61-Q66):

NEW QUESTION # 61
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?

A. Spark UDFs
B. [Structured Streaming
C. MLflow
D Delta Lake
D. AutoML

Answer: A

NEW QUESTION # 62
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

A. JS does not require any manual threshold or cutoff determinations
B. JS is not normalized or smoothed
C. All of these reasons
D. JS is more robust when working with large datasets
E. None of these reasons

Answer: D

NEW QUESTION # 63
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

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

A. Replace POST with GET in the call to http request
B. Replace list with view in the endpoint URL
C. Replace POST with PUT in the call to http request
D. Replace list with webhooks in the endpoint URL
E. There are no necessary changes

Answer: D

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

Answer: E

NEW QUESTION # 65
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

A. None of these deployments
B. Batch
C. Real-time
D. Streaming
E. On-device

Answer: D

NEW QUESTION # 66
......

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