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

Topic	Details
Topic 1	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 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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	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 5	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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 (Q143-Q148):

NEW QUESTION # 143
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

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

Answer: B

NEW QUESTION # 144
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. Indent the child run blocks within the parent run block
C. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
D. Provide the same name to the run name parameter for all three run blocks
E. Add the nested=True argument to the parent run

Answer: C

NEW QUESTION # 145
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

A. client.list_artifacts(run_id)["feature-importances.csv"]
B. This can only be viewed in the MLflow Experiments UI
C. mlflow.sklearn.load_model(model_uri)
D. client.pyfunc.load_model(model_uri)
E. mlflow.load_model(model_uri)

Answer: E

NEW QUESTION # 146
A Machine Learning Engineer is building a fraud detection model that needs to use both pre- computed features from a feature table and real-time calculated features based on user location data sent with each inference request. The engineer has created a Python UDF called calculate_distance in Unity Catalog at main.fraud_detection.calculate_distance that computes the distance between a transaction location and the user's current location. The feature table main.fraud_detection.user_features contains historical user spending patterns with primary key user_id.
The engineer has written the following code to implement this scenario:

Which benefit of this implementation approach makes it suited to the real-time fraud detection use case?

A. The model automatically performs feature lookup and computation during inference without additional serving code.
B. The FeatureLookup function avoids the need for joining the full table main.fraud_detection.user_features with training_set increasing efficiency.
C. The FeatureFunction caches computed distance values in the online store to improve inference latency for location pairs.
D. The Unity Catalog registry automatically creates REST API endpoints for UDF functions used in feature computation.

Answer: A

Explanation:
By defining both FeatureLookup and FeatureFunction objects in the training set and logging the model with the FeatureEngineeringClient, the feature logic is packaged with the model. During inference, Databricks automatically performs feature lookups from the feature table and computes the on-demand distance feature using request-time inputs, without requiring any additional custom serving or feature-joining code. This makes the approach well suited for real-time fraud detection.

NEW QUESTION # 147
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. There are no necessary changes
B. Replace POST with GET in the call to http request
C. Replace list with webhooks in the endpoint URL
D. Replace POST with PUT in the call to http request
E. Replace list with view in the endpoint URL

Answer: B

NEW QUESTION # 148
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