Questions for the DA0-001 were updated on : Nov 23 ,2025
Which of the following is an example of a strategy to reduce statistical errors?
B
Explanation:
Adding more data reduces statistical errors by improving the reliability and accuracy of statistical
measures. A larger sample size helps balance variations and minimizes the impact of outliers.
Reference: CompTIA Data+ DA0-001 Official Guide
An analyst computed a new variable of income per day in the household by multiplying the number
of days worked by the number of people working in the household and the income earned per day.
Which of the following is the correct name for this new variable?
A
Explanation:
A derived variable is created through mathematical computations or transformations from existing
data. In this case, "income per day" is derived by combining multiple existing variables.
Reference: CompTIA Data+ DA0-001 Official Guide
A column is being used to store strings of variable lengths. Performance is a concern, so the column
needs to use as little space as possible. Which of the following data types best meets these
requirements?
C
Explanation:
VARCHAR is a data type optimized for storing variable-length strings. It only uses the necessary space
for each value, making it more efficient in terms of storage compared to CHAR, which allocates a
fixed amount of space regardless of the string's actual length.
Reference: CompTIA Data+ DA0-001 Official Guide
Consider two different datasets, one with gas prices and the other with food prices. Which of the
following measures is most affected by outliers?
D
Explanation:
The mean (average) is the most sensitive measure when it comes to outliers. If a dataset contains
extreme values (either very high or very low), they disproportionately affect the mean, making it a
less robust measure of central tendency.
Reference: CompTIA Data+ DA0-001 Official Guide
A data architect is designing a data solution for a retail clothing store chain. Each store has a database
that tracks sales transactions. The data architect needs to create a summary table that will be used
for a senior executive dashboard. The summary table should not contain duplicate store information.
Which of the following should the data architect create?
D
Explanation:
A unique constraint ensures that no duplicate values exist in a specified column of a table. This is
essential for creating summary tables where each store should only appear once in the dataset.
Reference: CompTIA Data+ DA0-001 Official Guide
An analyst wants to determine whether a relationship between an individual's age and voting
preferences exists. Which of the following is the best statistical method for the analyst to use?
B
Explanation:
The Chi-squared test is used to analyze relationships between two categorical variables. In this case,
age groups and voting preferences are both categorical variables, making chi-squared the most
appropriate test.
Reference: CompTIA Data+ DA0-001 Official Guide
An analyst is compiling a series of reports for the new executive board to review. Which of the
following elements provides a snapshot of what is contained in the reports for the executives who do
not have time to focus on the details?
C
Explanation:
Observations and insights provide a concise summary of the key findings in a report. Executives, who
often lack the time to analyze detailed datasets, benefit from these summarized interpretations,
which highlight the most relevant trends, anomalies, and conclusions derived from the data.
Reference: CompTIA Data+ DA0-001 Official Guide
An analyst wants to test the association between the number of doors in a car and the number of
gears in the car. Which of the following is the best test to use?
C
Explanation:
The Chi-squared test is used to determine whether there is a statistical association between two
categorical variables. In this case, both "number of doors" and "number of gears" are categorical
variables, making the chi-squared test the appropriate method.
Reference: CompTIA Data+ DA0-001 Official Guide
A company needs a report that provides executives an overview and regional managers with both an
overview and specifics. Which of the following reporting elements will achieve these results?
C
Explanation:
A drill-down function allows users to start with a high-level summary of the data and then click
through to see more detailed layers of information. This feature is particularly useful in business
intelligence and reporting systems, as it provides executives with a broad overview while enabling
managers to explore specific data points relevant to their regions or operations.
Reference: CompTIA Data+ DA0-001 Official Guide
A dataset requires an analysis for investigating and discovering abnormalities. Which of the following
best describes the nature of the exploratory analysis conducted?
A
Explanation:
Exploratory Data Analysis (EDA) is a technique used to summarize the main characteristics of a
dataset. It often involves using graphical representations and statistical summaries to uncover
patterns, relationships, and anomalies in the data. Analysts perform EDA to detect outliers, missing
values, and trends before proceeding with deeper data processing and modeling.
Reference: CompTIA Data+ DA0-001 Official Guide
Which of the following is concatenate typically used to combine?
B
Explanation:
Concatenation is primarily used to combine columns of data. It is a function commonly used in data
manipulation to merge strings or numerical values from two or more columns into a single column.
This is especially useful in data cleaning and transformation tasks where information from multiple
fields needs to be represented together in a single field, such as merging first names and last names
into a full name.
Reference: CompTIA Data+ DA0-001 Official Guide
Which of the following types of dashboards should a business intelligence engineer develop in order
to provide information about failed data pipelines?
C
Explanation:
Comprehensive and Detailed In-Depth
Dashboards are visual tools that provide insights into various aspects of business operations. The
type of dashboard developed depends on the intended audience and the nature of information to be
conveyed.
Referencing Dashboard: This term is not standard in the context of dashboard types and doesn't
correspond to a recognized category.
Strategic Dashboard: Designed for senior management, strategic dashboards provide a high-level
overview of key performance indicators (KPIs) aligned with the organization's long-term goals. They
focus on overall performance and strategic objectives, rather than detailed operational issues.
Operational Dashboard: These dashboards monitor the real-time operations of an organization. They
are used to track immediate metrics and processes, allowing teams to respond quickly to issues as
they arise. In the context of data pipelines, an operational dashboard would display the current
status, including any failures, enabling prompt action to resolve issues.
Technical Dashboard: While this could pertain to dashboards focused on technical metrics, it's not a
standard term. Operational dashboards often encompass technical aspects, especially concerning
system operations and processes.
Given the need to provide information about failed data pipelines, an Operational Dashboard is most
appropriate. It offers real-time monitoring and alerts for immediate issues within data processes,
enabling swift identification and resolution of failures.
Reference: CompTIA Data+ Certification Exam Objectives (DA0-001), Domain 4.3: Translate business
requirements to form the appropriate visualization.
Which of the following data types is best for representing count data?
A
Explanation:
Comprehensive and Detailed In-Depth
Count data refers to data that represents the number of occurrences of an event or the number of
items in a set, which are whole numbers (integers). Understanding the nature of data types is crucial
for accurate data analysis and representation.
Discrete Data: This type of data consists of distinct, separate values. Discrete data is countable and
often represents items that can be counted in whole numbers, such as the number of customers,
defects, or occurrences. Since count data involves whole numbers, discrete data is the most
appropriate representation.
Referential Data: This pertains to data that establishes relationships between tables in a database,
often using keys. It is not related to counting occurrences.
Sequential Data: This involves data that follows a specific order or sequence, such as timestamps or
ordered events. While it indicates order, it doesn't inherently represent count data.
Continuous Data: This type of data can take any value within a range and is measurable rather than
countable, such as height, weight, or temperature. Continuous data is not suitable for representing
count data, as counts are discrete by nature.
Therefore, Discrete data is the best choice for representing count data, as it accurately reflects whole
number counts of occurrences or items.
Reference: CompTIA Data+ Certification Exam Objectives (DA0-001), Domain 1.2: Compare and
contrast different data types.
A sales manager requested a report that contains the first name, last name, and phone number of all
the company’s customers and employees. The data engineer needs to return all the records from
several tables, even duplicates. Which of the following is the best way to join the two tables?
D
Explanation:
Comprehensive and Detailed In-Depth
In SQL, different types of joins are used to combine records from two or more tables based on related
columns. The choice of join affects the result set, especially concerning the inclusion of duplicates
and the completeness of data retrieval.
FULL OUTER JOIN: Retrieves all records when there is a match in either left or right table. Non-
matching rows will also be included, with NULLs in place where the join condition is not met.
INNER JOIN: Retrieves only the records that have matching values in both tables.
LEFT OUTER JOIN: Retrieves all records from the left table and the matched records from the right
table. Non-matching rows from the right table will result in NULLs.
CROSS JOIN: Returns the Cartesian product of the two tables, meaning it combines all rows from the
first table with all rows from the second table. This join includes all possible combinations, resulting
in a dataset that contains all records from both tables, including duplicates.
Given the requirement to return all records from several tables, even duplicates, a CROSS JOIN is
appropriate. However, it's essential to note that a CROSS JOIN can produce a very large result set,
especially if the tables have many rows. Therefore, it should be used cautiously and typically with
additional filtering to manage the size of the output.
Reference: CompTIA Data+ Certification Exam Objectives (DA0-001), Domain 2.2: Summarize
methods and techniques for data acquisition.
A data analyst is setting up a data dashboard to monitor several ETL data streams to ensure that data
is complete for later analysis. Which of the following audiences should the analyst target for this
dashboard?
C
Explanation:
Comprehensive and Detailed In-Depth
Dashboards designed to monitor ETL (Extract, Transform, Load) data streams are technical tools that
track data processing workflows, identify errors, and ensure data completeness and accuracy.
Technical Experts: This group includes data engineers, ETL developers, and system administrators
responsible for maintaining data pipelines. They possess the technical expertise to understand,
interpret, and act upon the detailed metrics and alerts provided by the ETL monitoring dashboard.
Executives: While they are key decision-makers, executives typically require high-level summaries
and insights rather than detailed technical metrics.
The Management Team: Managers oversee operations and may require performance indicators but
not the granular technical details of ETL processes.
External Vendors: Unless they are directly involved in the ETL processes