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Databricks Machine Learning Associate Exam - Topic 3 Question 42 Discussion

A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.Which of the following terms is used to describe this combination of models?
D) Ensemble learning
A) Bootstrap aggregation
B) Support vector machines
C) Bucketing
E) Stacking

Databricks Machine Learning Associate Exam - Topic 3 Question 42 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 42
Topic #: 3
[All Databricks Machine Learning Associate Questions]

A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.

Which of the following terms is used to describe this combination of models?

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Suggested Answer: D

Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.

Reference

Introduction to Ensemble Learning: https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/


by Iluminada at May 05, 2026, 11:22 PM

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Dominga
2 days ago
I think it's ensemble learning. It combines models for better performance.
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Billy
7 days ago
Totally makes sense to use ensemble methods here!
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Franklyn
12 days ago
Bootstrap aggregation is more about averaging, not this.
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Dalene
18 days ago
Wait, can you really combine models like that? Sounds tricky!
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Stevie
23 days ago
I thought it was stacking at first, but I agree with the ensemble idea.
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Malcolm
28 days ago
That's definitely ensemble learning!
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Colby
1 month ago
I keep mixing up bootstrap aggregation and ensemble learning. I think ensemble learning is the right term, but I need to double-check my notes.
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Rickie
3 months ago
I practiced a question similar to this, and I think the term for combining models is ensemble learning. It just makes sense for this scenario.
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Lou
3 months ago
I'm not entirely sure, but I think stacking is also a way to combine models. It could be relevant, but I feel like ensemble learning is more general.
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Viola
3 months ago
I remember studying ensemble learning, which is about combining multiple models to improve performance. That might be the answer here.
upvoted 0 times
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