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

TopicDetails
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
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 4
  • 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 5
  • 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 6
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q152-Q157):

NEW QUESTION # 152
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

Answer: A


NEW QUESTION # 153
What is the purpose of ParamGridBuilder?

Answer: B

Explanation:
ParamGridBuilder creates parameter search grids for tuning.


NEW QUESTION # 154
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: 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 # 155
Which of the following lists all of the model stages are available in the MLflow Model Registry?

Answer: B


NEW QUESTION # 156
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: C


NEW QUESTION # 157
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