CompTIA DY0-001 Exam Dumps

Get All CompTIA DataX Certification Exam Questions with Validated Answers

DY0-001 Pack
Vendor: CompTIA
Exam Code: DY0-001
Exam Name: CompTIA DataX Certification Exam
Exam Questions: 85
Last Updated: December 19, 2025
Related Certifications: CompTIA DataX
Exam Tags: Expert Data ScientistsMachine Learning Engineers
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Free CompTIA DY0-001 Exam Actual Questions

Question No. 1

A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?

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Correct Answer: A

An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the timeseries history is available.


Question No. 2

Which of the following methods should a data scientist use just before switching to a potential replacement model?

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Correct Answer: A

A/B testing lets you compare the current model against the candidate in parallel, measuring performance on live data, before fully switching to the new model.


Question No. 3

During EDA, a data scientist wants to look for patterns, such as linearity, in the dat

a. Which of the following plots should the data scientist use?

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Correct Answer: C

Scatter plots display pairs of numeric values on two axes, letting you visually assess relationships and patterns, such as linear trends, between variables.


Question No. 4

A data analyst is examining the correlation matrix of a new data set to identify issues that could adversely impact model performance. Which of the following is the analyst most likely checking for?

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Correct Answer: B

Examining a correlation matrix helps identify predictors that are highly correlated with each other, which can inflate variance in coefficient estimates and degrade model reliability - i.e., multicollinearity.


Question No. 5

Which of the following explains back propagation?

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Correct Answer: D

Back propagation computes the gradient of the loss (error) with respect to each weight by propagating the error signal backward through the network, then uses those gradients to adjust weights and biases.


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