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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	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	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 4	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 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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	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 (Q124-Q129):

NEW QUESTION # 124
What is the main purpose of the Databricks Feature Store?

A. Replacing Spark ML pipelines
B. Storing raw data
C. Storing trained models
D. Centralizing reusable ML features

Answer: D

Explanation:
Feature Store allows teams to:
share features
avoid training/serving skew
maintain feature lineage.

NEW QUESTION # 125
A team is building a machine learning pipeline, which includes feature engineering, model training, model evaluation, and model deployment. Over the last year, the project has grown a lot, with multiple contributors working on it. Currently, they are manually testing their pipelines and functions. However, they are looking to build out a MLOps process and want to organize their unit tests. Which approach to organizing functions and unit tests should they take?

A. Place all functions and their corresponding unit tests in a single notebook or script.
B. Organize functions and unit tests together in multiple notebooks, grouping them by feature or component.
C. Separate functions into dedicated modules and place all unit tests in a separate test suite or notebook.
D. Write unit tests only after deployment to avoid slowing down development.

Answer: C

Explanation:
Separating production code into dedicated modules and organizing unit tests in a separate test suite follows standard software engineering and MLOps best practices. This structure improves maintainability, enables automated testing in CI/CD pipelines, supports collaboration among multiple contributors, and scales effectively as the project grows.

NEW QUESTION # 126
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. Remove the nested=True argument from the child runs
B. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
C. Provide the same name to the run name parameter for all three run blocks
D. Add the nested=True argument to the parent run
E. Indent the child run blocks within the parent run block

Answer: B

NEW QUESTION # 127
A machine learning engineer is working on a fraud detection machine learning application. When a transaction is made with a credit card, the machine learning application will immediately process the data and make a prediction to determine whether or not to approve the transaction based on the probability that the transaction is fraudulent. Which deployment strategy can be used to meet these requirements?

A. Streaming
B. Edge/on-device
C. Real-time
D. Batch

Answer: C

Explanation:
Real-time deployment is required for applications that must provide immediate predictions, such as fraud detection during a credit card transaction. This ensures that the system can instantly assess risk and approve or deny the transaction without delay.

NEW QUESTION # 128
A Data Scientist is developing a model training pipeline on Databricks and needs to track custom performance metrics during training. They want to log a custom evaluation score (team_score), a single hyperparameter, and a confusion matrix plot as part of their MLflow experiment. Which code snippet correctly logs all three types of information in MLflow?

A. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact(open("confusion_matrix.png"))
B. mlflow.log_metric({"team_score": 0.92})
mlflow.log_param(["max_depth", 5])
mlflow.log_file("confusion_matrix.png")
C. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact("confusion_matrix.png")
D. mlflow.log_param("max_depth", "5")
mlflow.log_metric("team_score", "0.92")
mlflow.log_artifact("confusion_matrix.png")

Answer: C

Explanation:
This snippet correctly uses MLflow's APIs to log each item in its expected form: a single hyperparameter with log_param, a numeric custom metric with log_metric, and a file-based artifact such as a confusion matrix image by passing its file path to log_artifact. This is the standard and correct way to track parameters, metrics, and artifacts in an MLflow experiment.

NEW QUESTION # 129
......

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