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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	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	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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	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 6	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 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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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Databricks Certified Machine Learning Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function provides no benefits over the built-in library-specific model flavors
B. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
C. python_function can be used to deploy models without worrying about which library was used to create the model
D. python_function can be used to store models in an MLmodel file
E. python_function can be used to deploy models in a parallelizable fashion

Answer: E

NEW QUESTION # 32
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Experiments
B. Model Serving
C. Feature Store
D. Model Registry
E. Models

Answer: D

NEW QUESTION # 33
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A.
B.
C. mlflow.log_data(importance_path, "feature-importance.csv")
D. mlflow.log_artifact(importance_path, "feature-importance.csv")
E. None of these code blocks tan accomplish the task.

Answer: A

NEW QUESTION # 34
A data scientist wants to remove the star_rating column from the Delta table at the location path.
To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

A. spark.read.table(path).drop("star_rating")
B. spark.read.format("delta").load(path).drop("star_rating")
C. spark.read.format("delta").table(path).drop("star_rating")
D. Delta tables cannot be modified
E. spark.sql("SELECT * EXCEPT star_rating FROM path")

Answer: A

NEW QUESTION # 35
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

A. All of these statements must be true prior to deploying the new model
B. The new model must perform better than the original model on the most recently available data
C. The now model must perform better than the original model on all of the available data
D. The new model must perform better than the original model on a random subset of the available data

Answer: B

Explanation:
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

NEW QUESTION # 36
......

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