Quiz Databricks-Machine-Learning-Professional - Databricks Certified Machine Learning Professional Pass-Sure Latest Test Testking

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

Section	Weight	Objectives
Topic 1: Model Deployment	12%	- Custom model serving - Deployment strategies - Model rollout and version management
Topic 2: Model Development	44%	- Feature Store and automated feature pipelines - Advanced MLflow usage - Scalable ML pipelines with SparkML - Distributed training and hyperparameter tuning
Topic 3: ML Ops	44%	- Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies - Automated retraining workflows - Environment management with Databricks Asset Bundles

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Databricks Certified Machine Learning Professional Sample Questions (Q166-Q171):

NEW QUESTION # 166
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. Write unit tests only after deployment to avoid slowing down development.
B. Place all functions and their corresponding unit tests in a single notebook or script.
C. Organize functions and unit tests together in multiple notebooks, grouping them by feature or component.
D. Separate functions into dedicated modules and place all unit tests in a separate test suite or notebook.

Answer: D

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 # 167
How can you save a trained Spark ML PipelineModel?

A. pipeline.store()
B. pipeline.persist()
C. pipeline.save()
D. pipelineModel.write().save()

Answer: D

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")

NEW QUESTION # 168
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 Unity Catalog registry automatically creates REST API endpoints for UDF functions used in feature computation.
B. The model automatically performs feature lookup and computation during inference without additional serving code.
C. The FeatureFunction caches computed distance values in the online store to improve inference latency for location pairs.
D. The FeatureLookup function avoids the need for joining the full table main.fraud_detection.user_features with training_set increasing efficiency.

Answer: B

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 # 169
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.
B.
C.
D. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
E.

Answer: E

NEW QUESTION # 170
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

A. The Artifacts section of the MLflow Experiment page
B. Logged data visualizations cannot be viewed in Databricks
C. The MLflow Model Registry Model paqe
D. The Figures section of the MLflow Run page
E. The Artifacts section of the MLflow Run page

Answer: D

NEW QUESTION # 171
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