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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
  • 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 3
  • 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 4
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
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
  • 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 7
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 8
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur

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Databricks-Machine-Learning-Professional Test Lab Questions, Demo Databricks-Machine-Learning-Professional Test

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Databricks Certified Machine Learning Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which statement is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

Answer: E


NEW QUESTION # 13
A machine learning engineer has detected that concept drift is occurring in a production machine learning application. Which result is the impact of concept drift?

Answer: D

Explanation:
Concept drift occurs when the relationship between input features and the target variable changes over time. This leads to a decrease in the model's efficacy, as the model is no longer accurately capturing the underlying data patterns it was trained on.


NEW QUESTION # 14
A Machine Learning Engineer wants to use on-demand features to train a model. They have a Python UDF, which relies on data fetched from a FeatureLookup. However, in online serving, the pipeline falls when the lookup ID is not found. Which solution will resolve this issue?

Answer: A

Explanation:
When an on-demand feature relies on a FeatureLookup, missing lookup keys can result in None values during online serving. Updating the Python UDF to explicitly handle None and NaN values ensures the feature computation is robust to missing lookups, preventing runtime failures while preserving correct behavior during both training and online inference.


NEW QUESTION # 15
What tool helps avoid feature mismatch between training and inference?

Answer: B

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.


NEW QUESTION # 16
Which approach is best for scoring large historical datasets?

Answer: D

Explanation:
Spark allows distributed batch scoring across massive datasets.


NEW QUESTION # 17
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