Free Practice Questions for CompTIA DA0-001 Exam

R is a statistical programming language that is specifically designed for data analysis and statistical modeling, making it highly suitable for creating a multiple linear regression model. It has extensive libraries such as lm() for linear modeling, which simplifies the process of model creation, diagnostics, and interpretation. R also provides robust tools for data manipulation and visualization, which are essential for preparing data for regression analysis and understanding the results123.

While Microsoft Power BI, SQL, and Tableau have capabilities for regression analysis, they are more limited compared to R. Power BI and Tableau are primarily business intelligence tools that offer some built-in analytics capabilities, but they are not as comprehensive as R. SQL is a database query language that can perform some statistical calculations, but it is not inherently designed for statistical modeling4567.

A word cloud is an ideal choice for visualizing what employees are searching for on an intranet site. It represents the frequency of word occurrence in a visually impactful way, withmore commonly searched terms appearing larger in the cloud. This allows for quick identification of the most popular queries and topics of interest among employees. Unlike histograms, pie charts, or scatter plots, word clouds can effectively display textual data, which is the nature of search queries. They are particularly useful for analyzing text data from surveys or feedback forms, which can be similar to search query data in an intranet environment1234.

 Normalizing the variables means scaling them to a common range, such as 0 to 1 or -1 to 1, so that they have the same weight in the score calculation. Recoding the variables means changing their values or categories, which would alter their meaning and distribution. Calculating the percentiles of the variables means ranking them relative to each other, which would not account for their actual magnitudes. Calculating the standard deviations of the variables means measuring their variability, which would not make them comparable. References: CompTIA Data+ Certification Exam Objectives, page 10

Answer: B. Ordinary least squares

Ordinary least squares (OLS) is a type of linear regression that is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Use when: The relationship between the predictor variable(s) and the response variable is reasonably linear.The response variable is a continuous numeric variable1.

In this case, the data analyst is interested in understanding how ice cream consumption (the response variable) is affected by different attributes, such as cost, temperature, and income level (the predictor variables). Assuming that these variables have a linear relationship, OLS can be used to estimate the coefficients of the regression equation that best fits the data.OLS can also provide measures of goodness-of-fit, such as R-squared and adjusted R-squared, and test the significance of the coefficients using t-tests and F-tests2.

Option A is incorrect, as logistic regression is used to fit a regression model that describes the relationship between one or more predictor variables and a binary response variable.Use when: The response variable is binary – it can only take on two values1. Ice cream consumption is not a binary variable, but rather a continuous numeric variable.

Option C is incorrect, as Cox regression is used to fit a regression model that describes the relationship between one or more predictor variables and a survival time response variable.Use when: The response variable is the time until an event of interest occurs, such as death, failure, or recovery3. Ice cream consumption is not a survival time variable, but rather a continuous numeric variable.

Option D is incorrect, as polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable.Use when: The relationship between the predictor variable(s) and the response variable is non-linear1. If there is no evidence of non-linearity in the data, polynomial regression may not be appropriate, as it may overfit the data and produce unreliable estimates.

Modern APIs (especially RESTful APIs) commonly use JSON (JavaScript Object Notation) as the default data interchange format because:

It is lightweight, text-based, and human-readable.

It maps naturally to key–value pairs, arrays, and nested structures.

Most programming languages and frameworks provide native JSON parsing for API requests and responses.

Why the others are incorrect:

HTML (A) – is a markup language for structuring web pages, not a primary machine-to-machine data format for APIs.

ZIF (C) – not a standard data exchange format in this context.

CSS (D) – is for styling HTML elements, not for data representation.

Therefore, the appropriate format for storing data that is natively used by an API is JSON (B).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Data concepts and environments: data formats such as JSON and their use in APIs.

Study sections discussing structured vs semi-structured formats and how APIs typically consume JSON.

The correct answer is .html.

Short explanation: Web scraping is the process of extracting data from websites by parsing the HTML code of the web pages. HTML stands for HyperText Markup Language and it is the standard markup language for creating web pages and web applications. HTML files have the extension .html and they contain tags, elements, attributes, and content that define the structure and appearance of a web page.Web scraping tools need to parse the HTML files to extract the relevant data from the web pages12

Although the specific table isn’t shown here, this question type in CompTIA-style practice typically presents a score table where some values:

Are outside the valid range (e.g., scores > 100 on a 0–100 test, or negative scores)

Or use non-numeric or nonsensical entries for a score field (e.g., “N/A”, “ABC” where numeric values are required)

When values violate defined business rules or constraints (e.g., “exam scores must be between 0 and 100 and numeric”), they are considered invalid data.

Why the other options are not the best reason:

Redundant data (B): Would mean repeated/duplicate copies of the same information. That doesn’t address bad or impossible values.

Data outliers (C): Outliers are extreme but possible values within the defined range. Here the driver for cleansing is not just extremity but rule violation (e.g., >100 on a test that maxes at 100).

Missing data (D): Would concern null/blank fields, which is a different quality issue.

The key phrase in this type of question is that some values do not conform to the allowed or logical rules of the field. In CompTIA Data+ terms, this is a classic case of Invalid data (A) and is a primary driver for data cleansing.

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Domain: Data Quality (identifying invalid, inconsistent, incomplete data).

CompTIA Data+ Official Study Guide – section on data quality dimensions, highlighting validity as adherence to defined business rules (e.g., allowed ranges for scores).

The term that best describes the action of changing the names of categories that you used for different types of books is recoding. Recoding is a process of transforming or modifying the values of a variable or a category to make them more meaningful, consistent, or accurate. For example, you can recode the names of book genres from “Fiction”, “Non-Fiction”, “Biography”, etc. to “FIC”, “NF”, “BIO”, etc. to make them shorter and easier to use. Reference: Recoding Data - SPSS Tutorials - LibGuides at Kent State University