DA0-002 CompTIA Data+ Exam (2025) Questions and Answers

This question pertains to the Visualization and Reporting domain, focusing on report delivery frequencies. The report is described as unscheduled, meaning it’s a one-time request.

Annual (Option A): Annual frequency implies a scheduled report every year, not suitable for an unscheduled request.

Daily (Option B): Daily frequency implies a scheduled report each day, not suitable.

Weekly (Option C): Weekly frequency implies a scheduled report each week, not suitable.

Ad hoc (Option D): Ad hoc reports are generated on-demand for one-time or unscheduled needs, which matches the project manager’s request.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and ad hoc is the best frequency for an unscheduled report.

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 question pertains to the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores raw, unformatted data.

Data warehouse (Option A): A data warehouse stores structured, processed data in a predefined schema, not raw data.

Data silo (Option B): A data silo is an isolated repository, often structured, not designed for raw data storage.

Data mart (Option C): A data mart is a subset of a data warehouse, also storing structured data.

Data lake (Option D): A data lake stores raw, unformatted data in its original format (structured, semi-structured, or unstructured), making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed for raw data storage.

This question falls under the Visualization and Reporting domain of CompTIA Data+ DA0-002, focusing on selecting the appropriate visualization for a specific dataset. The task is to show availability zones over a 15-year period (past and future), which involves a geographical element since availability zones are typically location-based.

Bar chart (Option A): Bar charts are good for comparing categorical data but don’t effectively show geographical locations or time-based trends across zones.

Mosaic plot (Option B): Mosaic plots display relationships between categorical variables, not suitable for geographical or time-series data.

Map (Option C): A map can display availability zones geographically, with annotations or layers to show changes over time (past 10 years and future 5 years), making it the most appropriate visualization.

Pie chart (Option D): Pie charts show proportions of a whole, not suitable for geographical or time-based data.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a map is the best choice for displaying geographical availability zones over time.

This question falls under the Data Analysis domain, focusing on selecting the appropriate function type to filter data in a query. The task is to flag customers who made a purchase in January, which involves a conditional check.

Statistical (Option A): Statistical functions (e.g., AVG, STDEV) analyze data distributions, not suitable for flagging specific months.

Logical (Option B): Logical functions (e.g., WHERE Month = 'January' in SQL) are used to apply conditions and flag rows based on criteria, which fits the task.

Mathematical (Option C): Mathematical functions (e.g., SUM, ROUND) perform calculations, not conditional flagging.

Date (Option D): Date functions (e.g., MONTH()) manipulate dates, but the Month column is already in text format, so a logical comparison is sufficient.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and logical functions are best for conditional flagging.

This question falls under the Data Governance domain, focusing on troubleshooting data freshness issues in dashboards. The dashboard shows stale data despite a successful refresh, and the server migration updated naming conventions, suggesting a potential mismatch.

Confirm the dashboard is pointed to the newest dataset (Option A): The server migration updated dataset naming conventions, so the dashboard might still be pointing to an old dataset name, causing stale data. Confirming the dataset connection is the first step.

Filter the data in the dashboard (Option B): Filtering might adjust the view but doesn’t address the root cause of stale data.

Escalate user permissions on the server (Option C): Permissions issues would likely prevent access, not cause stale data, especially since the dataset refreshed successfully.

Verify that the dashboard subscription is not expired (Option D): An expired subscription might prevent access, but the dashboard is accessible, just showing stale data.

The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring dashboards connect to the correct, updated datasets after changes like server migrations.

This question pertains to the Data Analysis domain, focusing on statistical measures. The middle 50% of a dataset refers to a specific statistical concept related to data distribution.

Interquartile range (Option A): The interquartile range (IQR) is the range between the first quartile (Q1, 25th percentile) and the third quartile (Q3, 75th percentile), representing the middle 50% of the data, which matches the description.

The difference between mode and median (Option B): This measures the spread between two central tendency metrics but doesn’t represent the middle 50% of the data.

Mean variance (Option C): Variance measures data dispersion around the mean, not the middle 50%.

Skewness from the slope (Option D): Skewness measures data asymmetry, and "slope" is irrelevant here.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the IQR is the standard measure for the middle 50% of a dataset.

This question falls under the Visualization and Reporting domain, focusing on tailoring reports for different audiences. The same data is used for three distinct groups (supervisors, city council, public), requiring differentiation.

Persona type (Option A): Persona types define the audience’s needs and preferences (e.g., supervisors need operational details, the city council needs summaries, the public needs simplified data), making this the best way to differentiate the reports.

Detail levels (Option B): Detail levels are a result of persona types, not the method of differentiation.

Accessibility (Option C): Accessibility ensures access for all users (e.g., screen readers), but it’s not the primary way to differentiate content.

Sensitivity (Option D): Sensitivity determines data access (e.g., confidential vs. public), but the scenario implies all reports use the same data, just presented differently.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and persona types guide report differentiation for diverse audiences.

This question falls under the Data Governance domain of CompTIA Data+ DA0-002, focusing on troubleshooting data quality issues in dashboards. The dashboard isn’t loading properly, indicating a potential issue with the data connection or configuration.

Review the data layer and data source (Option A): The data layer (e.g., queries, connections) and data source (e.g., database) are the foundation of a dashboard. If the information isn’t loading, the issue likely lies in the data connection or query configuration, making this the first step.

Validate that the database is up-to-date (Option B): While this might be a subsequent step, it assumes the connection is working, which should be confirmed first.

Check that the program is updated to the latest version (Option C): Software updates might fix bugs, but this isn’t the most immediate cause of data not loading.

Ensure the correct filters are displaying on the dashboard (Option D): Filters affect data display, but if the data isn’t loading at all, the issue is more fundamental.

The DA0-002 Data Governance domain includes "data quality control concepts," and reviewing the data layer and source is the primary step in troubleshooting dashboard loading issues.

This question pertains to the Data Concepts and Environments domain, focusing on the purpose of data lineage. Data lineage involves tracking the lifecycle of data.

To see the steps and path of data flow through different systems (Option A): This describes a data flow diagram, not data lineage, which focuses on transformations rather than just flow.

To better understand the granularity of data variable relationships (Option B): This relates to data modeling, not the purpose of data lineage.

To track data transformations from acquisition through reporting (Option C): Data lineage tracks the journey of data, including transformations (e.g., cleaning, aggregation) from its source to its final use in reporting, which is its primary purpose.

To look up data definitions, ensuring consistent use across business units (Option D): This describes a data dictionary, not data lineage.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and data lineage specifically tracks transformations across the data lifecycle.

This question falls under the Data Acquisition and Preparation domain, which includes techniques for manipulating and filtering data, such as using regular expressions (RegEx) to identify specific patterns in text data. The task is to filter items containing the word "Chocolate."

Chocolate! (Option A): In RegEx, "!" is not a valid pattern for matching a word like "Chocolate." It typically denotes negation in some contexts, but here it’s incorrect.

Chocolate$ (Option B): The "$" in RegEx anchors the pattern to the end of the string, meaning it matches "Chocolate" at the end of an item name (e.g., "Milk Chocolate"). This is the most appropriate pattern for identifying items ending with "Chocolate," which applies to the relevant items in the list.

%Chocolate& (Option C): "%" and "&" are not standard RegEx anchors; they’re often used in SQL LIKE patterns, not RegEx, making this incorrect.

#Chocolate#$ (Option D): "#" is not a standard RegEx anchor, and this pattern would look for "Chocolate" surrounded by "#", which doesn’t match the data.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation" , and RegEx is a common technique for filtering text data. The pattern "Chocolate$" correctly identifies items like "Chocolate Bars," "Chocolate Wafers," "Chocolate Cake," and "Milk Chocolate."

This question falls under the Data Concepts and Environments domain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.

Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.

Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.

Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.

Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they’re not specifically designed for relational keys in a data warehousing context.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.

This question is related to the Data Governance domain of DA0-002, which includes understanding processes like User Acceptance Testing (UAT) to ensure data-related applications meet governance and quality standards. UAT is a critical step in ensuring software aligns with user needs and organizational requirements.

To begin the software application development process to enhance user experience (Option A): UAT occurs near the end of development, not at the beginning.

To ensure all parts of the software application work together after each sprint (Option B): This describes integration testing, not UAT, which focuses on user validation.

To review software application crashes, create patches, and deploy to users (Option C): This refers to post-deployment maintenance, not UAT.

To validate and verify that a software application meets the needs and requirements of users (Option D): UAT is specifically designed to ensure the software meets user requirements and functions as intended in a real-world scenario, aligning with governance standards for quality.

The DA0-002 Data Governance domain emphasizes "data quality control concepts" (similar to DA0-001, web ID: 1), which include ensuring applications meet user needs through processes like UAT.

This question falls under the Data Acquisition and Preparation domain, focusing on combining data from multiple tables. The tables have different content but share a common field, indicating a join operation.

Match the records of the related columns and merge the tables (Option A): This describes a join operation, where records are matched on the common field (e.g., a key like Customer_ID) and the tables are merged, which is the most efficient method.

Create a cluster to facilitate data integration between the tables (Option B): Clustering is a machine learning technique, not a method for joining tables.

Explode both tables to identify unique values and reorder the fields in one table (Option C): Exploding is used in nested data (e.g., JSON arrays), and this approach is overly complex and unnecessary.

Append the values of the matching columns and concatenate the other data fields (Option D): Appending stacks tables vertically, and concatenation applies to text, neither of which is appropriate for joining tables with a common field.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables using a common field.

This question falls under the Data Analysis domain, focusing on SQL query correction. The query uses an aggregate function (SUM) but has two issues: it uses "SORT BY" (incorrect syntax) and lacks a GROUP BY clause for non-aggregated columns.

The query selects firstName, lastName, and SUM(income), but firstName and lastName are not aggregated, requiring a GROUP BY clause.

"SORT BY" is incorrect; the correct syntax is "ORDER BY."

Option A: SELECT firstName, lastName, SUM(income) FROM companyRoster HAVING SUM(income) > 10000000This adds a HAVING clause but doesn’t fix the GROUP BY issue, so it’s still invalid.

Option B: SELECT firstName, lastName, SUM(income) FROM companyRoster GROUP BY firstName, lastNameThis adds the required GROUP BY clause for firstName and lastName, fixing the aggregation error. While it removes the ORDER BY, the query will run without it, addressing the primary error.

Option C: SELECT firstName, lastName, SUM(income) FROM companyRoster ORDER BY firstName, incomeThis fixes "SORT BY" to "ORDER BY" but doesn’t address the missing GROUP BY, so the query remains invalid.

Option D: SELECT firstName, lastName, SUM(income) FROM companyRosterThis removes the ORDER BY but still lacks the GROUP BY clause, making it invalid.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and adding GROUP BY fixes the aggregation error, allowing the query to run.

This question falls under the Data Analysis domain, focusing on analyzing discrepancies in data reports. The total distinct messages are fewer than the total emails sent, indicating a specific issue.

The aggregation did not include all emails (Option A): If the aggregation missed emails, the total sent would be lower, not the distinct count.

Some emails were not delivered (Option B): Undelivered emails would reduce the total sent, but the scenario implies the total sent is accurate.

The report failed to run properly (Option C): A report failure would likely cause broader issues, not a specific discrepancy between distinct and total counts.

A recipient received duplicate emails (Option D): If recipients received duplicates, the total emails sent would be higher than the distinct messages (unique email content), explaining the difference.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and identifying duplicates is a common analysis task to explain such discrepancies.

This question is part of the Data Acquisition and Preparation domain, which involves combining data from multiple sources. The files have the same structure but different values, meaning they need to be stacked vertically into one dataset.

Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn’t indicated here since the files only differ in values, not keys.

Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.

Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.

Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.

This question falls under the Visualization and Reporting domain of CompTIA Data+ DA0-002, which involves understanding the components necessary for designing a report. The given requirements cover data, visuals, and design, but a key aspect of report planning is missing.

Source validation (Option A): Source validation ensures data accuracy, but it’s typically part of the data preparation phase, not a report design requirement.

Design elements (Option B): Color branding, labels, and terminology are design elements, so this is already included.

Delivery method (Option C): The delivery method (e.g., recurring, ad hoc, self-service) specifies how the report will be distributed or accessed, which is a critical requirement missing from the list.

Report type (Option D): Suggested charts and tables imply the report type (e.g., summary, dashboard), so this is indirectly covered.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and the delivery method is a key component of report planning that’s missing here.

This question falls under the Data Analysis domain of CompTIA Data+ DA0-002, focusing on SQL queries for data analysis. The task requires sorting cellular numbers by the carrier of the called number (called_number_carrier) and calculating the total call durations (SUM(call_duration)).

Option A: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number ORDER BY called_number_carrierThis query groups by cellular_number only, but called_number_carrier is in the SELECT clause without being in the GROUP BY, which is invalid in SQL (it would raise an error in most databases).

Option B: SELECT cellular_number, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrierThis query doesn’t include called_number_carrier in the SELECT clause, so it cannot be used in the ORDER BY clause, making it invalid. Grouping by call_duration also doesn’t align with the task.

Option C: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number, called_number_carrier ORDER BY called_number_carrierThis query correctly groups by both cellular_number and called_number_carrier (since both are in the SELECT clause), calculates the total call duration with SUM(call_duration), and sorts by called_number_carrier as required.

Option D: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrierGrouping by call_duration is incorrect because cellular_number and called_number_carrier are in the SELECT clause but not in the GROUP BY, making this query invalid.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and Option C correctly aggregates and sorts the data as specified.

This question pertains to the Data Concepts and Environments domain, focusing on selecting the appropriate repository for structured data across an entire company. The requirement for a predefined structure narrows the options.

Data mart (Option A): A data mart stores structured data for a specific business area (e.g., sales), not the whole company.

Data warehouse (Option B): A data warehouse is designed to store structured data from across the entire company in a predefined schema, optimized for analytics and reporting.

Data silo (Option C): A data silo is an isolated repository, often structured, but not designed for company-wide integration.

Data lake (Option D): A data lake stores raw data (structured and unstructured) without a predefined structure, not suitable for this requirement.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data warehouse is ideal for company-wide structured data.

This question falls under the Data Analysis domain, focusing on calculating new values from existing data. The task is to determine the total cost per transaction, which involves multiplying Quantity by Item Price.

Parsing (Option A): Parsing involves breaking down data (e.g., splitting a string), not calculating totals.

Scaling (Option B): Scaling adjusts numerical values to a common range (e.g., normalization), not relevant for calculating totals.

Compressing (Option C): Compressing reduces data size, not applicable to calculating costs.

Deriving (Option D): Deriving involves creating new data fields by performing calculations on existing ones (e.g., Total Cost = Quantity × Item Price), which fits the task.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as deriving new fields through calculations to analyze data.

This question pertains to the Data Concepts and Environments domain, focusing on documentation for understanding datasets. The analyst needs variable-level details (e.g., field definitions), which points to a specific type of documentation.

Data lineage (Option A): Data lineage tracks data flow and transformations, not variable definitions.

Data dictionary (Option B): A data dictionary provides detailed information about variables (e.g., name, type, description), which is exactly what the analyst needs.

Data flow diagram (Option C): A data flow diagram shows data movement, not variable-level details.

Data versioning (Option D): Data versioning tracks changes to datasets, not variable definitions.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is the primary tool for variable-level information.

This question is part of the Visualization and Reporting domain, focusing on report delivery methods. The requirement for a report that can be generated and accessed at any time suggests user-driven access.

Ad hoc (Option A): Ad hoc reports are one-time, on-demand reports, not designed for anytime access by the user.

Dynamic (Option B): Dynamic reports allow interactivity but don’t necessarily imply user-generated access at any time.

Self-service (Option C): Self-service reporting allows users (e.g., the senior manager) to generate and access reports on-demand at any time, fitting the requirement.

Static (Option D): Static reports are fixed and don’t allow on-demand generation by the user.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and self-service is ideal for anytime access.

This question falls under the Data Governance domain, focusing on data access and security troubleshooting. The analyst reset their password, but the recurring analysis failed to log in, indicating a mismatch.

The company changed its database authentication method (Option A): This would affect all users, not just the analyst, and there’s no indication of a broader change.

The password expiration process locked the account (Option B): The analyst reset the password as required, so the account isn’t likely locked due to expiration.

The analyst did not change the password used to launch the report (Option C): Recurring analyses often use stored credentials. If the analyst updated their password but didn’t update the stored credentials for the analysis, the login would fail, making this the most likely reason.

The company is experiencing issues with password replication (Option D): This is possible but less likely without evidence of broader system issues.

The DA0-002 Data Governance domain includes "data privacy concepts," and ensuring stored credentials match updated passwords is a common issue in recurring analyses.

This question pertains to the Data Governance domain, focusing on troubleshooting and maintaining data quality in software processes. The analyst has already tried basic debugging and confirmed compatibility, so the next step involves seeking external resources.

Use the old software and preexisting code, since both were functional (Option A): Reverting to old software avoids solving the issue and may introduce other risks (e.g., security vulnerabilities).

Contact the IT department and inform them that the software has a bug (Option B): Assuming a bug without further investigation is premature, especially since compatibility was confirmed.

Escalate to the department manager and ask for assistance (Option C): Escalation is a later step after exhausting technical troubleshooting options.

Research the issue online and see if a solution is available (Option D): Researching online (e.g., forums, documentation) is a logical next step to find solutions or identify known issues, especially after local debugging fails.

The DA0-002 Data Governance domain includes "data quality control concepts," and researching online is a standard troubleshooting step to maintain data process integrity.

This question pertains to the Data Governance domain, focusing on ensuring data quality and reliability in dashboards over time. Daily troubleshooting indicates a recurring issue, and the task is to validate data moving forward.

Inquiring about structure changes (Option A): This might identify past issues but doesn’t provide ongoing validation.

Setting up monitoring alerts (Option B): Monitoring alerts can automatically notify the analyst of data issues (e.g., missing updates, errors), providing a proactive way to validate data continuously.

Reaching out to users daily (Option C): This is inefficient and reactive, not a sustainable validation method.

Rebuilding the dashboard (Option D): Rebuilding might fix current issues but doesn’t ensure future validation.

The DA0-002 Data Governance domain includes "data quality control concepts," such as implementing monitoring to ensure data reliability in dashboards.

This question falls under the Visualization and Reporting domain of DA0-002, which involves selecting appropriate delivery methods for reports. The scenario describes a need for custom reports for future executive meetings, implying a scheduled, repeated delivery.

Ad hoc (Option A): Ad hoc reports are generated on-demand for one-time use, not suitable for ongoing needs.

Real-time (Option B): Real-time delivery provides live data updates, which isn’t necessary for scheduled executive meetings.

Recurring (Option C): Recurring delivery involves scheduling reports to be generated and delivered at regular intervals (e.g., weekly or monthly), which fits the need for future executive meetings.

Self-service (Option D): Self-service allows users to generate reports themselves, but the scenario implies the analyst will create the reports.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report" with delivery methods , and recurring delivery aligns with scheduled reporting needs.

This question falls under the Data Concepts and Environments domain, focusing on selecting appropriate data types for storing specific kinds of data. The task requires storing foreign words, which often include non-native characters (e.g., accents, Unicode characters).

VARCHAR (Option A): VARCHAR stores variable-length strings but typically uses ASCII or single-byte encoding, which may not support all foreign characters.

NVARCHAR (Option B): NVARCHAR (National VARCHAR) stores variable-length strings in Unicode, supporting a wide range of non-native characters, making it the best choice.

CLOB (Option C): CLOB (Character Large Object) is for large text data, but it’s overkill for most foreign words and not specifically designed for Unicode.

CHAR (Option D): CHAR stores fixed-length strings, but like VARCHAR, it often uses single-byte encoding, which may not support foreign characters.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like NVARCHAR for Unicode support.

This question pertains to the Data Concepts and Environments domain, focusing on data sources that facilitate online data access. The task is to identify a source that simplifies online data consumption.

Data mart (Option A): A data mart stores structured data for specific business areas, typically accessed internally, not designed for online consumption.

Web scraping (Option B): Web scraping extracts data from websites but requires parsing and cleaning, which isn’t necessarily "easier."

Database (Option C): Databases store data but aren’t inherently designed for online consumption without an interface.

Application programming interface (Option D): An API provides a structured way to access data online, often in formats like JSON, making data consumption easier for applications and users.

The DA0-002 Data Concepts and Environments domain includes understanding "data sources," and APIs are specifically designed to simplify online data access.

This question is part of the Data Concepts and Environments domain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.

Redundancy (Option A): The TypeID column (e.g., "Full Time 1") redundantly includes the Type value ("Full Time") with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.

Outlier (Option B): Outliers are data points that deviate significantly, which isn’t applicable here.

Missing data (Option C): There are no missing values in the table.

Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and redundancy is a common data quality issue in schema design.