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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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 3	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 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 9	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

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Databricks Certified Machine Learning Professional Sample Questions (Q39-Q44):

NEW QUESTION # 39
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.read_table
C. fs.create_table
D. fs.write_table
E. fs.get_table

Answer: C

NEW QUESTION # 40
Which of the following is a benefit of logging a model signature with an MLflow model?

A. The schema of input data will be converted to match the signature
B. The model will be secured by the user that developed it
C. The schema of input data can be validated when serving models
D. The model can be deployed using real-time serving tools
E. The model will have a unique identifier in the MLflow experiment

Answer: A

NEW QUESTION # 41
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?

Answer: B

NEW QUESTION # 42
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. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
D. Indent the child run blocks within the parent run block
E. Remove the nested=True argument from the child runs

Answer: C

NEW QUESTION # 43
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
B. Because the streaming deployment is always on, all types of data must be handled without producing an error
C. None of these statements
D. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
E. All of these statements

Answer: A

NEW QUESTION # 44
......

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2025 Databricks-Machine-Learning-Professional–100% Free Valid Practice Questions | Accurate Exam Databricks-Machine-Learning-Professional Cost

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

Exam Databricks-Machine-Learning-Professional Cost & Latest Databricks-Machine-Learning-Professional Study Materials

Databricks Certified Machine Learning Professional Sample Questions (Q39-Q44):

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