DA0-001 CompTIA Data+ Certification Exam Free Practice Exam Questions (2026 Updated)

A data dictionary is a collection of metadata that describes the data elements in a database or dataset. It can help improve data acquisition by providing information about the data sources, formats, quality, and usage. It can also help remember specifics about data fields, such as their names, definitions, types, sizes, and relationships. Therefore, options B and D are correct.

Option A is incorrect because it is not a reason to create and maintain a data dictionary, but a benefit of doing so.

Option C is incorrect because specifying user groups for databases is not a function of a data dictionary, but a function of a database management system or a security policy.

Option E is incorrect because confining breaches of PHI data is not a function of a data dictionary, but a function of a data protection or encryption system.

Option F is incorrect because reducing processing power requirements is not a function of a data dictionary, but a function of a data compression or optimization system.

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

The first step in the development process of revamping a company-wide dashboard should be to talk to the group that made the request to determine the desired goal. This would help to understand the needs, expectations, and preferences of the stakeholders, as well as the scope, purpose, and objectives of the project. Talking to the group that made the request would also help to establish a clear communication channel, build rapport and trust, and solicit feedback and suggestions.

The median is recognized as the most appropriate measure of central tendency when outliers have been removed from a dataset. This is because the median is less influenced by extreme values compared to the mean. When outliers are present, they can significantly skew the mean, making it an unreliable measure of central tendency. The median, on the other hand, is the middle value of a dataset when ordered from least to greatest and remains unaffected by the extremes. Therefore, it provides a better representation of the central location of the data after outliers have been excluded.

The correct answer is A. UNION ALL.

UNION ALL is a SQL statement that appends two tables that have the same number of columns and compatible data types.UNION ALL preserves all the rows from both tables, including any duplicates12

B. MERGE is not correct, because MERGE is a SQL statement that combines the data of two tables based on a common column.MERGE can perform insert, update, or delete operations on the target table depending on the matching or non-matching rows from the source table34

C. GROUP BY is not correct, because GROUP BY is a SQL clause that groups the rows of a table based on one or more columns.GROUP BY is often used with aggregate functions, such as SUM, AVG, COUNT, etc., to calculate summary statistics for each group56

D. JOIN is not correct, because JOIN is a SQL clause that combines the data of two tables based on a common column or condition. JOIN can produce different results depending on the type of join, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, etc.

The error in merging data from Table 2 into Table 1 using last names could be due to discrepancies in spelling or data type between the two tables. It is essential to ensure that the last names are spelled consistently and that the data types are compatible for a successful merge. Option D suggests reviewing these aspects, which can potentially resolve the error, ensuring that each last name in Table 1 accurately corresponds to the same last name in Table 2, allowing for a successful data pull of addresses.

A telephone number, despite being composed of digits, is not used for calculations and often includes formatting characters such as hyphens (-). Therefore, the most appropriate data type for a telephone number is Text (or VARCHAR in SQL databases), which can accommodate various formats and lengths, and preserve leading zeros that might be present in some phone numbers. Storing phone numbers as numeric data types would strip away any formatting and could lead to the loss of significant leading digits (like zeros in international numbers).

Boolean is a binary data type and only represents true or false values.

Date is a data type used for dates.

Number could technically store phone numbers, but it is not suitable due to the reasons mentioned above.

A name is an example of personally identifiable information (PII), which is any data that can be used to identify someone, either on its own or with other relevant data. A name is a direct identifier, which means that it can uniquely identify a person without the need for any additional information. For example, a full name, such as John Smith, can be used to distinguish or trace an individual’s identity1.

Other examples of direct identifiers include:

Social Security Number

Passport number

Driver’s license number

Email address

Phone number

The next step after understanding a business requirement for a data analysis report is to determine the data necessary for the analysis. This step involves identifying the data sources, variables, metrics, and dimensions that are relevant and sufficient to answer the business question or problem. This step also involves assessing the availability, quality, and accessibility of the data, and planning how to collect, clean, and prepare the data for analysis. The other options are not the next steps after understanding a business requirement, but rather subsequent steps in the data analysis process. Rephrasing the business requirement is a step that can help clarify and refine the business question or problem before determining the data necessary for the analysis. Building a mock dashboard/presentation layout is a step that can help design and visualize the report before performing the data analysis. Performing exploratory data analysis is a step that can help explore and summarize the data before drawing conclusions and recommendations from the data. Reference: Data Analysis Process - DataCamp

When undertaking the task of revamping a long-standing company-wide dashboard, it's imperative to begin by understanding the specific needs and objectives of the stakeholders who requested the update. This ensures that the revamped dashboard aligns with current business requirements and provides relevant insights.

Option A:Talk to the group that made the request to determine the desired goal.

Rationale:Engaging with the requesting group allows the analyst to gather detailed information about their expectations, the challenges they face with the current dashboard, and the specific metrics or data visualizations they require. This collaborative approach ensures that the final product is user-centric and meets the actual needs of its users.

The purpose of an executive summary is to provide a concise and informative overview of a longer report, allowing busy stakeholders to quickly understand the key points and findings without reading the entire document. This summary should highlight the most important data, conclusions, and recommendations, and is typically placed at the beginning of the report for easy access12.

In the context of a detailed sales report for senior management, including a summary section with observations and insights would allow the team to quickly grasp the performance across various offices and identify any significant trends or issues that require attention. This approach aligns with best practices for executive reporting, which emphasize the importance of clear and concise summaries that focus on essential KPIs and actionable insights12.

Data encryption and data masking are two actions that can be taken when transmitting data to mitigate the chance of a data leak occurring. Data encryption means transforming data into an unreadable format that can only be decrypted with a key. Data masking means hiding or replacing sensitive data with fictitious or anonymized data. Both methods protect the confidentiality and integrity of the data in transit. References: CompTIA Data+ Certification Exam Objectives, page 13

Outliers are data points that differ significantly from other observations and can skew analysis results, leading to inaccurate conclusions. The main reason to remove them is when their values vary so significantly from the rest of the data that they distort statistical calculations or trends.

CompTIA Data+ Reference:

CompTIA Data+ Study Guide (Exam DA0-001), Chapter 5: Data Cleaning and Preparation, Section “Identifying and Handling Outliers”, Official CompTIA CertMaster Learn for Data+, Module 5.1 “Data Quality Issues: Outliers”.

Explanation

Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. The Snowflake data platform is not built on any existingdatabase technology or “big data” software platforms such as Hadoop.

Missing data is a type of data quality issue that occurs when some values in a data set are not recorded or available. Missing data can affect the validity and reliability of survey results, especially if the missing values are not random or ignorable. Missing data can also reduce the sample size and the statistical power of the analysis12

If 100% participation is needed on survey results, a data analyst would look for missing data first, because missing data would indicate that some participants did not complete or submit the survey, or that some responses were not recorded or transmitted correctly. A data analyst would need to identify the causes and patterns of missing data, and apply appropriate methods to handle or prevent missing data, such as imputation, deletion, weighting, or follow-up12

The best answer is C. Microsoft Power BI.

Microsoft Power BI is a business analytics and business intelligence service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. Power BI can connect to multiple data sources, clean and transform data, create custom calculations, and visualize data through charts, graphs, and tables.Power BI can be accessed through a web browser, mobile device, or desktop application and integrated with other Microsoft tools like Excel and SharePoint12

Python is not correct, because Python is a general-purpose programming language that can be used for various applications, including data analysis and visualization. However, Python is not a dedicated business analytics tool, and it requires coding or programming skills to create reports and dashboards.

R is not correct, because R is a programming language and software environment for statistical computing and graphics. R can be used for data analysis and visualization, but it is not a specialized business analytics tool, and it requires coding or programming skills to create reports and dashboards.

SAS is not correct, because SAS is a software suite for advanced analytics, business intelligence, data management, and predictive analytics. SAS can provide interactive visualizations and business capabilities, but it does not have an interface that is simple enough for end users to create their own reports and dashboards. SAS also requires coding or programming skills to use its features.

The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation. Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a betterunderstanding of the data and its quality, as well as prepare the data for further analysis.