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

TopicDetails
Topic 1
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 2
  • 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 3
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
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 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 live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q120-Q125):

NEW QUESTION # 120
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?

Answer: D

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 # 121
Why is Apache Spark useful for machine learning training?

Answer: D

Explanation:
Spark processes large distributed datasets efficiently.


NEW QUESTION # 122
A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which of the following types of drift is present in the above scenario?

Answer: A


NEW QUESTION # 123
A Machine Learning Engineer is conducting hyperparameter tuning for multiple XGBoost models using Ray Tune on Databricks. They want to integrate MLflow tracking to monitor their experiments and need to ensure proper authentication. The engineer has Ray 2.41 installed and wants to use both Ray Tune and MLflow together in their distributed tuning workflow. They have to configure Databricks to run the hyperparameter optimization with MLflow integration. Which set of configuration steps will do this?

Answer: D

Explanation:
When using Ray Tune with MLflow on Databricks, Ray workers must be able to authenticate back to the Databricks workspace to log runs to MLflow Tracking. Setting the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before initializing the Ray cluster ensures all Ray processes can securely communicate with Databricks and correctly log MLflow experiments during distributed hyperparameter tuning.


NEW QUESTION # 124
A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

Answer: D

Explanation:
The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.


NEW QUESTION # 125
......

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