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

 A FULL JOIN in SQL combines all rows from two or more tables, regardless of whether a match exists. The result includes all records when there is a match in the joined tables and fills in NULLs for missing matches on either side. Given the two tables in the image, the first table has three rows, and the second table has four rows. The FULL JOIN of these tables will include all rows from both tables, resulting in four rows. Since there are three unique columns in the first table (ID, Title) and three unique columns in the second table(ID, Name, Project_ID), with the common column being ID, the resulting table will have four columns (ID, Title, Name, Project_ID).

 In technical documentation, a glossary is the designated section where definitions for technical terms are provided. It serves as a reference point for readers to understand specialized or uncommon words used within the document. Including descriptions of technical terms in a glossary ensures that readers have a consistent resource to refer to, which can improve comprehension and reduce misunderstandings12.

A system diagram (Option B) is a visual representation of the system’s components and their interactions, not a place for defining terms. User requirements (Option C) outline what end-users expect from the system, and an index (Option D) is an alphabetical list of topics covered in the document, usually with page numbers, but not definitions.

This is a simulation question that requires you to create a dashboard with visualizations that meet the director’s needs. Here are the steps to complete the task:

Drag and drop the visualization that shows the county outages on the top left space of the dashboard. This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages.

Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending.

Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background.

Select appropriate titles and labels for each visualization. Titles and labels may be used more than once. For example, you can use “County Outages” as the title for the map, “Status” as the title for the pie chart, and “Trend” as the title for the line graph. You can also use “County”, “Number of Outages”, “Active”, “Restored”, “Pending”, “Time”, and “Number of Outages” as labels for the axes and legends of the visualizations.

This is because web scraping is a technique that allows the analyst to extract data from web pages, such as articles from a digital news outlet. Web scraping can be done using various tools and methods, such as Python libraries, browser extensions, or online services. The other techniques are not suitable for acquiring data from web pages. Here is why:

Sampling is a technique that involves selecting a subset of data from a larger population, usually for statistical analysis or testing purposes. Sampling does not help the analyst to acquire data from web pages, but rather to reduce the amount of data to be analyzed.

Data wrangling is a technique that involves transforming and cleaning data to make it suitable for analysis or visualization. Data wrangling does not help the analyst to acquire data from web pages, but rather to improve the quality and usability of the data.

ETL stands for Extract, Transform, and Load, which is a process that involves moving data from one or more sources to a destination, such as a data warehouse or a database. ETL does not help the analyst to acquire data from web pages, but rather to store and organize the data.

Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. References: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples

For efficient data manipulation, the ideal order would be to first merge related tables to create a comprehensive set of records, then deduplicate to remove any redundant information. Lastly, appending additional data, such as from another source or table, ensures that all relevant data is included without redundancy before the final analysis. This order prevents unnecessary duplication of effort, such as deduplicating both before and after appending, which would be less efficient.

In the context of the tables provided, merging would likely involve combining customer information from the online and in-store transaction tables with the customer table. Deduplication would remove any redundant customer records that may exist across these tables. Finally, appending would involve adding any additional transaction records to the master file, ensuring a complete dataset for analysis.

“Role-based and group-based requirements” refer to:

Who can see or modify specific data.

Which roles (e.g., analyst, manager, admin) or groups (e.g., HR, Finance) are granted particular permissions.

In CompTIA Data+ terminology, these are Access requirements:

Define which users/roles/groups can access which data, at what level (read, write, update, delete).

Often implemented through role-based access control (RBAC) and group permissions.

Why the other options don’t fit as closely:

Security requirements (A) – broader category: includes encryption, masking, network security, etc.; access is part of security, but the phrase “role-based and group-based requirements” specifically aligns with access control.

Storage requirements (B) – deal with where and how data is stored (capacity, media, performance, backup).

Use requirements (D) – govern how data can be used (allowed purposes, ethical/legal constraints), not who can access it.

Therefore, an organizational document defining role-based and group-based requirements is specifying Access requirements (C).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Data governance: access control, roles, and permissions.

Study discussions of role-based access control (RBAC) and how access requirements are documented.

In hypothesis testing, the p-value represents the probability of observing a test statistic at least as extreme as the one observed, assuming the null hypothesis is true.

Key idea:

A low p-value (typically below a chosen significance level such as 0.05):

Indicates that the observed result is unlikely under the null hypothesis.

Suggests there is evidence against the null hypothesis.

Is therefore interpreted as potentially meaningful / statistically significant.

By contrast:

The test statistics themselves (e.g., t-statistic, F-statistic, chi-square) are usually interpreted such that larger absolute values indicate stronger evidence against the null (and lead to smaller p-values).

The question explicitly asks which test statistic shows that a low value implies meaningful result — that is p-value, which is itself a probability metric used to evaluate significance.

Therefore, the correct choice is p-value (B).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Statistical methods: hypothesis testing, p-values, significance.

Study materials: explanation that small p-values indicate statistically significant results.

A star schema is a type of database schema that consists of one fact table and multiple dimension tables. The fact table contains the measures or metrics of the business process, such as sales, orders, or transactions. The dimension tables contain the attributes or characteristics of the business entities, such as products, customers, or locations. The fact table is connected to the dimension tables by foreign keys that reference the primary keys of the dimension tables. The fact table is located at the center of the schema, while the dimension tables are located at the edges, forming a star-like shape1.

A star schema is an example of a denormalized schema, which means that the dimension tables are not normalized and may contain redundant or repeated data. This is done to improve the performance and simplicity of queries, as there are fewer joins and tables involved. A star schema is suitable for data warehouses and business intelligence applications that require fast and efficient data retrieval2.

SQL (Structured Query Language) is a domain-specific language used in programming, specifically designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. Unlike languages like Python or R, which are general-purpose programming languages, SQL is tailored specifically for database management and manipulation.

Customer segmentation is a type of cluster analysis, which is a method of grouping data points based on their similarities or differences. Cluster analysis can help identify patterns and trends in the data, as well as target specific groups of customers for marketing purposes. One common technique for customer segmentation is RFM analysis, which stands for recency, frequency, and monetary value. This technique assigns a score to each customer based on how recently they bought the product, how often they buy the product, and how much they spend on the product. These scores can then be used to create clusters of customers with different characteristics and preferences. Therefore, the correct answer is D. References: Cluster Analysis - Statistics Solutions, RFM Analysis: The Ultimate Guide for Customer Segmentation

Sampling refers to the process of selecting a representative subset of data from a larger data set or repository. This technique is used when it is impractical or unnecessary to analyze the entire set of data. A representative sample should accurately reflect the characteristics of the larger population, allowing for analysis and inference about the population as a whole12.

Observation (A) generally refers to the act of monitoring or recording data. Delta load (B) is a term used in data warehousing to describe the process of loading only the changes since the last data extraction, rather than the entire data set. Web scraping © is the process of extracting data from websites.

When creating a dashboard to identify trends in data sets, the most appropriate consideration is the data sources and attributes. This is because the quality, reliability, and structure of the data sources directly influence the dashboard’s ability to accurately reflect trends. Attributes, such as the type of data and the time frame it covers, are crucial for trend analysis. A well-designed dashboard should provide a clear and intuitive representation of the data, allowing for easy identification of trends and patterns.

Frequently asked questions (B) can inform the design of the dashboard but are not a direct consideration for the development process itself. A report from the data source © might be an output of the dashboard but does not guide its development. A comparison of data sets (D) could be a feature of the dashboard, but the underlying data sources and attributes must be considered first to ensure accurate and meaningful comparisons.

Explanation

The correct answer is D: Parametrization. Parameterized SQL queries allow you to place parameters in an SQL query instead of a constant value. A parameter takes a value only when the query is executed, allowing the query to be reused with different values and purposes. Parameterized SQL statements are available in some analysis clients, and are also available through the Historian SDK.

For example, you could create the following conditional SQL query, which contains a parameter for the collector's name: SELECT* FROM ExamsDigest WHERE coursename=? ORDER BY tagname SQL Injection is best prevented through the use of parameterized queries.

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.

Explanation

Normalization is the process of reorganizing data in a database so that it meets two basic requirements: There is no redundancy of data, all data is stored in only one place. Data dependencies are logical, all related data items are stored together.

Put simply, data normalization ensures that your data looks, reads, and can be utilized the same way across all of the records in your customer database. This is done by standardizing the formats of specific fields and records within your customer database.

The best delivery method to support the client’s requirement is C. Subscription.

Short explanation: A subscription is a delivery method that allows the client to access the dashboard on-demand, whenever they need it. A subscription can be set up by thedata analyst or the client themselves, and it can be configured to send an email notification when the dashboard is updated or refreshed.A subscription also allows the client to view the dashboard online or download it as a file format of their choice12

A. Email is not the best delivery method because it does not allow the client to access the dashboard on-demand. Email deliveries are sent at a fixed time or frequency, and they may not reflect the latest data or changes in the dashboard.Email deliveries also have limitations on the file size and format of the dashboard attachments1

B. Scheduled is not the best delivery method because it does not allow the client to access the dashboard on-demand. Scheduled deliveries are similar to email deliveries, except that they are triggered by a specific event or condition, such as a data update or a threshold value.Scheduled deliveries also have the same limitations as email deliveries on the file size and format of the dashboard attachments1

D. Static is not the best delivery method because it does not allow the client to access the dashboard on-demand. Static deliveries are one-time deliveries that are manually generated by the data analyst or the client. Static deliveries do not update or refresh automatically, and they may become outdated or irrelevant over time.Static deliveries also have limitations on the file size and format of the dashboard files3