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

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 Concepts and Environments domain, focusing on types of databases. The task is to identify a NoSQL database among the options.

PostgreSQL (Option A): PostgreSQL is a relational (SQL) database, not NoSQL.

MySQL (Option B): MySQL is a relational (SQL) database, not NoSQL.

Oracle (Option C): Oracle Database is a relational (SQL) database, not NoSQL.

MongoDB (Option D): MongoDB is a NoSQL database that uses a document-based model, storing data in JSON-like structures, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and MongoDB is a well-known NoSQL database.

This question pertains to the Data Acquisition and Preparation domain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from various sources, such as web scraping. Web scraping involves extracting data from websites, and the data gathered is often semi-structured, meaning it has some organizational structure but isn’t fully relational like a database table.

JSON (Option A): JSON (JavaScript Object Notation) is a semi-structured data format commonly used in web applications. Web scraping often retrieves data in JSON format via APIs or embedded scripts, as it’s lightweight and structured with key-value pairs, making it ideal for semi-structured data.

CSV (Option B): CSV (Comma-Separated Values) is a structured format typically used for tabular data. It’s not commonly the direct output of web scraping, though scraped data might be converted to CSV later.

CSS (Option C): CSS (Cascading Style Sheets) is used for styling web pages and isn’t a data format, making it irrelevant for describing scraped data.

HTML (Option D): HTML (HyperText Markup Language) is the structure of web pages and is often the raw format scraped during web scraping. While HTML is semi-structured due to its tag-based hierarchy, it’s primarily a markup language, not a data format, and the actual data extracted is often parsed into formats like JSON.

The DA0-002 Data Acquisition and Preparation domain aligns with the DA0-001 focus on "data acquisition concepts" (web ID: 14), which includes identifying formats like JSON as semi-structured data commonly acquired through web scraping. JSON is the best fit here due to its prevalence in web data exchange.

This question falls under the Data Analysis domain, focusing on SQL queries to identify data issues. The task is to find records with empty fields, which in SQL means NULL values.

WHERE [ColumnName] = NULL (Option A): In SQL, NULL cannot be compared using "="; this syntax is incorrect.

WHERE [ColumnName] IS NULL (Option B): This is the correct SQL syntax to identify NULL values, which represent empty fields.

WHERE [ColumnName] IS NOT NULL (Option C): This finds non-empty fields, the opposite of the requirement.

WHERE [ColumnName] = 'NULL' (Option D): This checks for the string "NULL," not a true NULL value, which is incorrect.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," such as identifying NULL values with IS NULL.

This question pertains to the Data Governance domain, focusing on data security and access control principles. The company restricts access to the minimum needed for duties, which aligns with a specific security concept.

Active Directory (Option A): Active Directory is a tool for managing users and permissions, not a concept.

Hierarchical access (Option B): Hierarchical access implies access based on roles in a hierarchy, but it doesn’t specifically focus on minimal access.

Zero Trust (Option C): Zero Trust requires continuous verification for all access, which is broader than just minimal permissions.

Least privilege (Option D): Least privilege ensures users have only the permissions necessary for their duties, which matches the scenario.

The DA0-002 Data Governance domain includes "data privacy concepts," and least privilege is a fundamental principle for secure access control.

This question falls under the Data Concepts and Environments domain, which in DA0-002 involves understanding database environments, connections, and troubleshooting issues related to data access. The scenario describes a report failing after a database upgrade, indicating a potential issue with the database environment or connectivity.

Ensure the system has permissions for the report service (Option A): While permissions issues can cause report failures, they are less likely to be the first issue after a database upgrade unless explicitly mentioned.

Change the report's refresh rate (Option B): Refresh rate adjustments might address performance issues but won’t resolve a fundamental error preventing data display.

Verify the connection to the database (Option C): A database upgrade often involves changes to connection strings, drivers, or network configurations. Verifying the connection ensures the report can access the database, making this the most logical first step.

Check whether the data structures were modified (Option D): While possible, checking data structures (e.g., schema changes) is a deeper troubleshooting step that should follow after confirming basic connectivity.

The DA0-002 Data Concepts and Environments domain includes understanding database connectivity, similar to DA0-001’s focus on "data schemas and dimensions" and environments (web ID: 1). Verifying the connection is the first recommended step in troubleshooting post-upgrade issues.

This question falls under the Data Analysis domain, focusing on troubleshooting issues in data reports. The report should show all employee sales but is limited to the current month, suggesting a data retrieval issue.

Lack of permissions (Option A): Permissions issues would likely prevent access entirely, not limit data to the current month.

An error in SQL code (Option B): The report likely uses an SQL query to retrieve data, and an error (e.g., a WHERE clause filtering for the current month) could restrict the data to the current month, making this the most likely cause.

Report refresh failure (Option C): A refresh failure would result in outdated data, not specifically current-month data.

Connectivity issues (Option D): Connectivity issues would likely prevent the report from running, not limit it to a specific time frame.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and errors in SQL code are a common cause of incorrect data retrieval in reports.

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 pertains to the Visualization and Reporting domain, focusing on dashboard design for specific audiences. Internal technical audiences (e.g., data analysts, IT staff) need actionable, data-driven insights.

Methodology section (Option A): Methodology is important for research reports, not dashboards, especially for technical audiences who prioritize data.

Dynamic features (Option B): Dynamic features (e.g., interactivity) are useful but not the most critical element for technical audiences.

Key performance indicators (Option C): KPIs provide critical metrics (e.g., system uptime, error rates) that technical audiences need to monitor and act on, making this the most important element.

Company branding (Option D): Branding is more relevant for external audiences, not internal technical ones.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and KPIs are essential for technical dashboards.

This question pertains to the Data Governance domain, focusing on data quality and consistency in survey design. Requiring all questions to have an answer ensures a specific type of data quality.

Missing values (Option A): Requiring answers prevents missing values (NULLs or blanks) in the survey responses, which is the primary inconsistency this setting addresses.

Duplication (Option B): Duplication refers to repeated records, not prevented by requiring answers.

Data corruption (Option C): Data corruption involves damaged or altered data, not related to missing answers.

Completeness (Option D): Completeness is the concept of having all necessary data, but "missing values" is the specific inconsistency prevented here.

The DA0-002 Data Governance domain includes "data quality control concepts," and preventing missing values ensures data integrity in survey responses.

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 types of server infrastructure. The task is to identify an infrastructure where a company owns and maintains all servers.

Private (Option A): A private infrastructure (often on-premises) means the company owns and maintains its own servers, typically in a private data center, which matches the requirement.

Cloud (Option B): Cloud infrastructure is managed by third-party providers, not owned by the company.

Hybrid (Option C): Hybrid combines on-premises and cloud, so not all servers are owned by the company.

Public (Option D): Public infrastructure is a cloud model shared across multiple organizations, not owned by the company.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and a private infrastructure requires the company to purchase and maintain its own servers.

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 pertains to the Visualization and Reporting domain, focusing on report delivery methods for a specific audience. The Chief Financial Officer (CFO) needs a weekly sales report, suggesting a concise, executive-level summary.

A granular daily report in a dashboard (Option A): Daily granularity is too frequent for a weekly report, and a dashboard might be too interactive for a CFO’s needs.

A detailed text document (Option B): A detailed document is too lengthy for a CFO, who typically needs a summary.

A spreadsheet with raw data (Option C): Raw data requires further analysis, which isn’t appropriate for an executive-level report.

A high-level email (Option D): A high-level email provides a concise summary of weekly sales, tailored for an executive like a CFO, making it the most appropriate delivery method.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a high-level email is best for delivering a weekly summary to a CFO.

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 pertains to the Visualization and Reporting domain, focusing on the process of building a reporting deck. After meeting requirements (tracking metrics, visualizing data, and row-level security), the next step involves validation with stakeholders.

Show a mock-up to the team (Option A): Creating a mock-up and presenting it to the team ensures alignment on design and functionality before finalizing, which is a standard next step in report development.

Explain the desired level of reporting detail (Option B): This should have been done earlier during requirements gathering, not after meeting them.

Present an analysis of the data (Option C): Data analysis might inform the deck, but the task focuses on building the deck, not presenting analysis.

Find out the project due date (Option D): The due date should have been established earlier in the project planning phase.

The DA0-002 Visualization and Reporting domain includes "translating business requirements to form the appropriate visualization," and showing a mock-up ensures the reporting deck meets stakeholder expectations.

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 falls under the Data Analysis domain, focusing on analytical methods for dashboards. The analyst wants to identify patterns in historical data for further study, which points to a specific type of analytics.

Predictive (Option A): Predictive analytics forecasts future outcomes, not focused on identifying patterns for further study.

Prescriptive (Option B): Prescriptive analytics provides recommendations, which goes beyond identifying patterns.

Diagnostic (Option C): Diagnostic analytics examines historical data to identify patterns, trends, and correlations, enabling further investigation, which fits the scenario.

Descriptive (Option D): Descriptive analytics summarizes what happened but doesn’t focus on identifying patterns for deeper study.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and diagnostic analytics is best for pattern identification in historical data.

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 pertains to the Data Governance domain, focusing on data quality validation techniques. The task is to validate that the number of records matches expectations, which requires a specific type of assessment.

Source control (Option A): Source control (e.g., Git) manages code versions, not dataset validation.

Unit test (Option B): A unit test checks a specific component of a process, such as verifying that the number of records in a dataset matches the expected count, making it the best fit.

Stress test (Option C): Stress tests evaluate system performance under load, not record counts.

Health check (Option D): A health check monitors system status but isn’t specific to validating record counts.

The DA0-002 Data Governance domain includes "data quality control concepts," and unit tests are a standard method for validating specific data outcomes like record counts.

This question pertains to the Visualization and Reporting domain, focusing on dashboard features for displaying data. The dashboard needs to show aggregated sales by region and allow identification of high-volume sales by salesperson within each region.

Defined parameters (Option A): Parameters set specific values (e.g., a date range), but they don’t directly enable interaction with aggregated data.

Filter options (Option B): Filter options allow the user to select a region and then view salespersons within that region, enabling the identification of high-volume sales per region interactively.

Level of detail (Option C): Level of detail determines the granularity of data shown but doesn’t facilitate interactive exploration.

User persona (Option D): User personas guide dashboard design but aren’t a communication technique for displaying data.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and filter options best enable the interactive analysis required.

This question pertains to the Data Acquisition and Preparation domain, focusing on tools for automating repeatable tasks in data processes. The task is to identify the most efficient programming method.

LLM (Option A): Large Language Models (e.g., GPT) are for text generation, not efficient for repeatable data tasks.

Deep learning (Option B): Deep learning is for complex pattern recognition (e.g., image classification), not efficient for simple repeatable tasks.

NLP (Option C): Natural Language Processing is for text analysis, not general repeatable tasks.

RPA (Option D): Robotic Process Automation (RPA) automates repetitive, rule-based tasks (e.g., data entry, file processing), making it the most efficient for programming repeatable tasks.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and RPA is a widely used method for automating repeatable data tasks.

This question pertains to the Data Analysis domain, focusing on the purpose of a pivot table. Pivot tables are a tool for summarizing and analyzing data, often used in preparation for reporting.

To visualize the data in a dashboard (Option A): Pivot tables summarize data but aren’t visualizations; charts in dashboards might be created from pivot tables.

To retrieve and clean data from several sources (Option B): Retrieving and cleaning data is part of data preparation, not the purpose of a pivot table.

To summarize and analyze the data (Option C): Pivot tables aggregate and summarize data (e.g., by calculating sums, averages) and allow for analysis (e.g., filtering, grouping), which is their primary purpose.

To organize the data for reporting (Option D): While pivot tables can help organize data, their main purpose is summarization and analysis, not just organization.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and pivot tables are a key tool for summarizing and analyzing large datasets.

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 Acquisition and Preparation domain of CompTIA Data+ DA0-002, focusing on data manipulation techniques. The task is to combine FirstName and LastName into a single FullName field (e.g., "JohnDoe").

Concatenate (Option A): Concatenation combines two or more strings into one (e.g., using CONCAT in SQL or "+" in Python), which is the correct method to create FullName from FirstName and LastName.

Filter (Option B): Filtering selects specific rows based on conditions, not suitable for combining fields.

Join (Option C): Joining combines data from multiple tables, but the task involves manipulating data within a single table.

Group (Option D): Grouping (e.g., GROUP BY in SQL) is for aggregation, not for combining fields into a new column.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and concatenation is the standard technique for combining fields like FirstName and LastName into FullName.

This question falls under the Data Governance domain of CompTIA Data+ DA0-002, which includes understanding compliance frameworks for data audits, especially for customer data. The scenario involves a data audit of customer invoice data, which likely contains personal information, making privacy regulations relevant.

PCI DSS (Option A): PCI DSS (Payment Card Industry Data Security Standard) applies specifically to payment card data, not general customer invoice data unless credit card details are involved, which isn’t specified.

GDPR (Option B): GDPR (General Data Protection Regulation) is a comprehensive privacy regulation for handling personal data of EU citizens, including customer invoice data, which may contain PII like names and addresses. It’s the most relevant for a company-wide data audit.

ISO (Option C): ISO standards (e.g., ISO 27001) relate to information security management but are not specific to customer data privacy audits.

PII (Option D): PII (Personally Identifiable Information) is a concept, not a framework or tool for conducting an audit.

The DA0-002 Data Governance domain emphasizes "identifying PII and data privacy concepts," and GDPR is the most appropriate framework for auditing customer data to ensure compliance with privacy laws.

This question pertains to the Data Analysis domain, focusing on issues with forecasting models. The scenario describes a sudden change in demand (viral product) that the model couldn’t predict because it hasn’t seen similar patterns before.

Model bias (Option A): Model bias occurs when a model systematically favors certain outcomes due to flawed training data, but this scenario is about a change in data patterns, not bias.

Data drift (Option B): Data drift occurs when the statistical properties of the data change over time (e.g., sudden high demand due to virality), causing the model to perform poorly because it was trained on different patterns, which fits the scenario.

Incorrect sizing (Option C): This term is vague and not a standard concept in data analysis for this context.

Skewing (Option D): Skewing refers to data distribution asymmetry, not a change in data patterns affecting model performance.

The DA0-002 Data Analysis domain includes understanding "applying the appropriate descriptive statistical methods," and data drift is a key concept in forecasting when data patterns change unexpectedly.

This question pertains to the Data Acquisition and Preparation domain, focusing on combining and deduplicating data using SQL. The task is to remove duplicates between two tables, meaning the analyst needs a unique set of records from both.

SELECT * FROM Employees UNION ALL SELECT * FROM Managers (Option A): UNION ALL combines all rows from both tables, including duplicates, which doesn’t meet the requirement.

SELECT * FROM Employees UNION SELECT * FROM Managers (Option B): UNION combines rows from both tables and automatically removes duplicates, providing a unique set of records, which fits the task.

SELECT * FROM Employees JOIN SELECT * FROM Managers (Option C): This syntax is incorrect; a JOIN requires an ON clause, and it wouldn’t remove duplicates.

SELECT * FROM Employees CROSS JOIN SELECT * FROM Managers (Option D): A CROSS JOIN creates a Cartesian product, resulting in all possible combinations, not removing duplicates.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and UNION is the correct SQL operation for combining tables while removing duplicates.

This question pertains to the Visualization and Reporting domain, focusing on survey design considerations, particularly for accessibility. The survey is for Paralympic athletes, who may have visual impairments, requiring specific design considerations.

Idioms (Option A): Idioms (e.g., colloquial phrases) might confuse non-native speakers, but they’re not a primary survey design concern for Paralympic athletes.

Color contrast (Option B): High color contrast ensures readability for athletes with visual impairments (e.g., color blindness), a critical accessibility consideration for Paralympic surveys.

Refresh speed (Option C): Refresh speed is relevant for dashboards, not static surveys.

Granularity (Option D): Granularity refers to data detail levels, not a survey design consideration.

The DA0-002 Visualization and Reporting domain includes "translating business requirements to form the appropriate visualization," and color contrast is a key accessibility factor in survey design for diverse audiences.

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 falls under the Visualization and Reporting domain, focusing on identifying tools used for business intelligence (BI), which typically involves data visualization and reporting.

SAS (Option A): SAS is a statistical analysis software, not primarily a BI tool focused on visualization.

Python (Option B): Python is a programming language, not a BI software, though it can be used for data analysis with libraries.

Notepad++ (Option C): Notepad++ is a text editor, not a BI tool.

Tableau (Option D): Tableau is a leading BI software designed for data visualization, dashboards, and reporting, making it the correct choice.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report or dashboard," and Tableau is a recognized BI tool for this purpose.

This question falls under the Data Acquisition and Preparation domain of CompTIA Data+ DA0-002, which involves acquiring and combining data from different sources, such as a database and a web API, using tools like Python. The task requires joining the data, which in Python often involves using pandas DataFrames.

Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together (Option A): VARCHAR is a database data type for strings, not a format for data exchange or merging in Python, making this incorrect.

Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together (Option B): Web APIs commonly return data in JSON format, and databases can export data as JSON. In Python, JSON data can be easily converted to pandas DataFrames using pandas.read_json() or pandas.DataFrame(), and then merged using pandas.merge() on a common key, making this the best approach.

Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together (Option C): TXT is a generic text format that lacks structure, making it less efficient for merging compared to JSON.

Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together (Option D): Converting to a string format is vague and not a standard approach for structured data merging in Python.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as combining data from APIs and databases, and JSON is a standard format for this purpose in Python.