What is data leakage in the context of machine learning?

When the test set is larger than the training set

When the model is trained on too much data and overfits

When information from outside the training set is used to create the model, leading to overly optimistic performance estimates

When sensitive information is accidentally exposed in the training data

Which practice is most likely to cause data leakage?

Splitting the data into training and test sets before any preprocessing

Applying feature scaling to the entire dataset before splitting into training and test sets

Removing duplicate rows from the dataset

Using only the training set to compute feature statistics for normalization

Which ensemble method builds multiple models sequentially, where each new model focuses on correcting the errors of the previous ones?

Bagging (Bootstrap Aggregating)

A data scientist is training a binary classifier for a spam detection system. The cost of a false negative (spam email landing in the inbox) is low, but the cost of a false positive (a legitimate email being marked as spam) is very high. Which loss function or metric should the data scientist prioritize to minimize the high-cost error?

Minimize the false negative rate (FNR)

Maximize the recall of the spam class

Minimize the false positive rate (FPR)

CompTIADataX

Domain 3Objective 1

Foundational Concepts DY0-001 Practice Questions (Page 3)

Part of the Machine learning domain, which accounts for 24% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~16–26 in this domain), expect 3–5 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

7concepts

24%of the exam

Questions 11–15

11foundation · easy
What is data leakage in the context of machine learning?
Select an answer first
12foundation · easy
Which practice is most likely to cause data leakage?
Select an answer first
13foundation · easy
Which loss function is most appropriate for a binary classification problem where the model outputs a probability between 0 and 1?
Select an answer first
14foundation · easy
Which ensemble method builds multiple models sequentially, where each new model focuses on correcting the errors of the previous ones?
Select an answer first
15application · medium
A data scientist is training a binary classifier for a spam detection system. The cost of a false negative (spam email landing in the inbox) is low, but the cost of a false positive (a legitimate email being marked as spam) is very high. Which loss function or metric should the data scientist prioritize to minimize the high-cost error?
Select an answer first

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Foundational Concepts DY0-001 Practice Questions (Page 3)

Questions 11–15

What is data leakage in the context of machine learning?

Which practice is most likely to cause data leakage?

Which loss function is most appropriate for a binary classification problem where the model outputs a probability between 0 and 1?

Which ensemble method builds multiple models sequentially, where each new model focuses on correcting the errors of the previous ones?

Keep studying — Machine learning