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

Topic	Details
Topic 1	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 2	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 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	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 (Q24-Q29):

NEW QUESTION # 24
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_artifact
B. mlflow.log_model
C. mlflow.log_param
D. mlflow.log_metric
E. There is no way to store values like this.

Answer: D

NEW QUESTION # 25
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Provide the same name to the run name parameter for all three run blocks
B. Add the nested=True argument to the parent run
C. Indent the child run blocks within the parent run block
D. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
E. Remove the nested=True argument from the child runs

Answer: D

NEW QUESTION # 26
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data. Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

Answer: B

NEW QUESTION # 27
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 APIs
B. MLflow Client
C. AutoML APIs
D. Jobs cannot be created programmatically
E. Databricks REST APIs

Answer: E

NEW QUESTION # 28
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Which of the following deployment strategies can be used to meet these requirements?

A. Edge/on-device
B. Streaming
C. None of these strategies will meet the requirements.
D. Batch
E. Real-time

Answer: E

NEW QUESTION # 29
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