Which of the following is a common technique to detect outliers in a univariate dataset?

Sorting the data in ascending order

Counting the number of unique values in the dataset

Calculating the mean and standard deviation of the entire dataset

Using the interquartile range (IQR) to identify values beyond 1.5 times the IQR from the quartiles

A tourism board collects daily visitor counts. They notice a pattern: visitor numbers spike every summer and drop every winter. They want to forecast monthly visitor numbers. What is the most important data issue to address?

Sparsity, by filling in missing winter data.

Outliers, by removing the summer spikes.

Seasonality, by including month as a feature or using a seasonal model.

Granularity, by converting daily data to hourly.

A data scientist is building a model to predict energy consumption based on temperature. The relationship is U-shaped: consumption is high at very low and very high temperatures, and low in between. A linear model performs poorly. What is the most appropriate approach?

Convert temperature to a binary variable (hot vs. cold).

Remove the extreme temperature observations.

Add a squared temperature term to the model.

Use a simple linear regression with temperature only.

A data analyst is building a model to predict daily website traffic. The dataset has many days with zero traffic (e.g., holidays) and other days with high traffic. The zero-traffic days are legitimate but create a sparse distribution. What is the most appropriate way to handle this?

Remove the zero-traffic days as outliers.

Use a model that can handle zero-inflated data, such as a zero-inflated Poisson model.

Aggregate the data to weekly totals.

Replace the zeros with a small positive value.

CompTIADataX

Domain 2Objective 2

Data Issues DY0-001 Practice Questions (Page 5)

Part of the Modeling, analysis, and outcomes 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 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

5concepts

24%of the exam

Questions 21–25

21foundation · easy
Which of the following is a common technique to detect outliers in a univariate dataset?
Select an answer first
22application · medium
A tourism board collects daily visitor counts. They notice a pattern: visitor numbers spike every summer and drop every winter. They want to forecast monthly visitor numbers. What is the most important data issue to address?
Select an answer first
23application · medium
A data scientist is building a model to predict energy consumption based on temperature. The relationship is U-shaped: consumption is high at very low and very high temperatures, and low in between. A linear model performs poorly. What is the most appropriate approach?
Select an answer first
24foundation · easy
A data scientist is working with a customer purchase history table where most customers have no recorded purchases for most months. Which term best describes this data characteristic?
Select an answer first
25application · medium
A data analyst is building a model to predict daily website traffic. The dataset has many days with zero traffic (e.g., holidays) and other days with high traffic. The zero-traffic days are legitimate but create a sparse distribution. What is the most appropriate way to handle this?
Select an answer first

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