Databricks Exam Databricks-Machine-Learning-Associate Topic 2 Question 27 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam

Question #: 27
Topic #: 2

[All Databricks-Machine-Learning-Associate Questions]

A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

AThe F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.

BThe F1 score should be utilized over accuracy when there are greater than two classes in the target variable.

CThe F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

DThe F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.

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Suggested Answer: D

If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.

In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.

Model Ensemble Techniques

by Omer at Mar 23, 2025, 10:38 AM

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23 days ago

I think the team should include option C in their guidelines.

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