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

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 pertains to the Data Governance domain, which in DA0-002 includes ensuring data quality and freshness, especially in dashboards. The issue is that the mean response time isn’t updating, while traffic data is current, indicating a potential issue with the data refresh process for the response time metric.

Refactoring the code base (Option A): Refactoring might improve long-term performance but doesn’t directly address verifying data freshness.

Testing for network connectivity issues (Option B): Network issues could cause delays, but since traffic data is updating, connectivity is likely not the issue.

Checking the last time the calculation script ran (Option C): Mean response time is a calculated metric, likely derived from a script. Checking when the script last ran directly verifies if the data refresh process failed, making this the best approach.

Determining the number of calls with no timestamps (Option D): Missing timestamps might indicate data quality issues, but it doesn’t directly verify why the mean response time isn’t updating.

The DA0-002 Data Governance domain focuses on "data quality control concepts," including ensuring data freshness in reporting. Checking the script’s last run time aligns with this objective.

This question falls under the Data Concepts and Environments domain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.

Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.

Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.

Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.

Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.

The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.

This question falls under the Data Concepts and Environments domain, focusing on the purpose of data types in data management. Data types define how data is stored and interpreted.

To provide a generic identifier for files used in analysis (Option A): Data types apply to fields within datasets, not files.

To identify the program needed to open a file (Option B): File extensions (e.g., .csv) identify programs, not data types.

To differentiate the real value of the field in its context (Option C): Data types (e.g., integer, string, date) define how a field’s value is interpreted (e.g., “123” as a number vs. text), ensuring its real meaning in context, making this the correct answer.

To make the addition of individual records simpler (Option D): Data types don’t directly simplify record addition; they ensure proper data handling.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and data types ensure fields are interpreted correctly in their context.

This question falls under the Data Analysis domain, focusing on statistical measures. The mode is the value that appears most frequently in a dataset.

Monday: 455

Tuesday: 346

Wednesday: 382

Thursday: 563

Friday: 887

Saturday: 934

Sunday: 346

The value 346 appears twice (Tuesday and Sunday), while all other values (455, 382, 563, 887, 934) appear once. Thus, the mode is 346.

Option A: 346 – Correct, as it’s the most frequent value.

Option B: 446 – Incorrect, as 446 doesn’t appear in the dataset.

Option C: 455 – Incorrect, as 455 appears only once.

Option D: 559 – Incorrect, as 559 doesn’t appear in the dataset.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the mode is a fundamental measure of central tendency.

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 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 Visualization and Reporting domain, focusing on selecting the appropriate visualization for a specific requirement. The task is to show the composition of total sales across three channels, which involves showing proportions.

Pivot table (Option A): A pivot table summarizes data but isn’t a visualization; it’s more for data exploration.

Pie chart (Option B): A pie chart shows the proportion of total sales for each channel (Digital, Store, Online), which is ideal for displaying composition.

KPI card (Option C): A KPI card displays a single metric, not suitable for showing composition across multiple channels.

Box plot (Option D): A box plot shows data distribution (e.g., quartiles), not proportions.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a pie chart is best for showing the composition of totals.

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 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 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 date calculations. The task is to calculate the difference between dates in a file and the current date, requiring proper date handling.

Convert data to date format and use date functions (Option A): Flat files often store dates as strings (e.g., "2023-01-01"). Converting them to a date format (e.g., using Python’s datetime or SQL’s TO_DATE) allows the use of date functions (e.g., DATEDIFF) to calculate the difference to the current date, which is the best approach.

Validate the date format with logical functions and use date functions to analyze (Option B): Validation is unnecessary if conversion handles format issues, making this overly complex.

Use date functions to analyze the data with no conversion (Option C): Without converting to a date format, date functions may fail if the data is stored as strings.

Transform data to a numerical value and use mathematical functions (Option D): This is inefficient and error-prone compared to using date functions.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and converting to date format followed by date functions is the standard method for such calculations.

This question pertains to the Data Analysis domain, focusing on SQL queries to analyze data relationships. The task is to find customers who have not placed an order, meaning customers in the Customers table without a matching Customer_ID in the Products table.

Option A: SELECT * FROM CUSTOMERS AS C LEFT JOIN PRODUCTS AS P ON C.ID = P.Customer_ID WHERE P.Customer_ID IS NULLA LEFT JOIN includes all customers, even those without orders (where Products columns are NULL). Filtering with WHERE P.Customer_ID IS NULL selects only customers without a match in Products, correctly identifying those who haven’t ordered.

Option B: SELECT * FROM CUSTOMERS AS C INNER JOIN PRODUCTS AS P ON C.ID = C.ID WHERE COUNT(P.*) = 0An INNER JOIN only includes matching records, so it won’t return customers without orders. The join condition C.ID = C.ID is also incorrect, and COUNT requires a GROUP BY, making this invalid.

Option C: SELECT * FROM PRODUCTS AS P INNER JOIN CUSTOMERS AS C ON P.Customer_ID = C.ID WHERE (SELECT COUNT(P.*) = 0)An INNER JOIN excludes customers without orders, and the subquery syntax is incorrect (COUNT needs a GROUP BY or to be part of a HAVING clause).

Option D: SELECT * FROM PRODUCTS AS P LEFT JOIN CUSTOMERS AS C ON P.Customer_ID = C.ID WHERE P.Customer_ID IS NOT NULLThis starts with Products and joins Customers, returning only records with orders (opposite of the task), and IS NOT NULL further excludes non-ordering customers.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and a LEFT JOIN with a NULL check is the standard method for finding non-matching records.

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 falls under the Data Analysis domain, focusing on methods to gather data for process improvement. The IT manager needs to review user concerns about wait times, which requires collecting feedback.

Infographic (Option A): An infographic visualizes data but isn’t a method for gathering feedback.

KPI (Option B): KPIs (e.g., average resolution time) measure performance but don’t directly gather user feedback.

Survey (Option C): A survey collects detailed feedback from users about their experiences, such as wait times, making it the best method for this review.

UAT (Option D): User Acceptance Testing validates software functionality, not support processes.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and surveys are a standard method for collecting user feedback to analyze and improve processes.

This question pertains to the Visualization and Reporting domain, focusing on selecting the appropriate communication method for a report. The requirements emphasize simplicity, clarity, accessibility, and automatic updates, which point to a specific approach.

Executive summary (Option A): An executive summary is a written document that summarizes metrics but isn’t typically updated automatically and may not be accessible to all levels without distribution.

Slide presentation (Option B): A slide presentation can be clear but requires manual updates and isn’t inherently accessible to all company levels.

Key performance indicator dashboard (Option C): A KPI dashboard provides a simple, visual summary of metrics, is clear and concise, can be understood by all levels, and can be set up to update automatically, meeting all requirements.

Open data portal (Option D): An open data portal provides raw data access, which may not be simple or easily understood by all levels.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a KPI dashboard is the best approach for meeting these requirements.

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 pertains to the Data Concepts and Environments domain, focusing on types of server environments. The servers are located at the company’s headquarters, indicating a specific deployment model.

Cloud (Option A): Cloud environments are hosted off-site by third-party providers, not at headquarters.

On-premises (Option B): On-premises environments are located at the company’s physical location (e.g., headquarters), which matches the scenario.

Public (Option C): Public environments are cloud-based and shared across multiple organizations, not located at headquarters.

Hybrid (Option D): Hybrid environments combine on-premises and cloud, but the scenario specifies all servers are at headquarters.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and on-premises describes a server environment located at the company’s site.

This question falls under the Data Analysis domain, focusing on SQL logical functions for query evaluation. The task is to evaluate "all conditions," implying multiple conditions must be true together.

OR (Option A): OR returns true if any condition is true, not ensuring all conditions are met.

NOT (Option B): NOT negates a condition, not suitable for combining multiple conditions.

AND (Option C): AND requires all conditions to be true, which aligns with evaluating "all conditions" in a query.

IF (Option D): IF is a conditional function for decision-making, not for evaluating multiple conditions together.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and AND is the best logical function for ensuring all conditions are met.

This question falls under the Data Concepts and Environments domain of CompTIA Data+ DA0-002, focusing on selecting appropriate data types for a given dataset. The Value column contains decimal numbers (1.5, 24.456, 113), requiring a data type that supports such values.

Double (Option A): Double is a floating-point data type that supports decimals with higher precision than Float, but it’s often overkill for typical datasets unless very high precision is needed, which isn’t indicated here.

Float (Option B): Float is a floating-point data type that supports decimal numbers (e.g., 1.5, 24.456) and is commonly used for such values in databases, making it the best choice.

Boolean (Option C): Boolean is for true/false values, not numeric data.

Integer (Option D): Integer is for whole numbers, but the values (e.g., 1.5, 24.456) have decimals, so Integer is not suitable.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like Float for decimal numeric values.

This question pertains to the Data Analysis domain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.

Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.

Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.

Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.

Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.

This question falls under the Data Governance domain, focusing on tools that support data management and quality control features. The task is to identify a tool with capabilities like versioning, corruption checks, KPIs, and authentication.

Notebook (Option A): Notebooks (e.g., Jupyter) are for data analysis and coding but don’t inherently support versioning, corruption checks, or authentication.

REST API (Option B): REST APIs enable data access but don’t provide versioning or corruption checks as a primary function.

Pipeline (Option C): Data pipelines automate data workflows but don’t typically include versioning or authentication.

Source control (Option D): Source control systems (e.g., Git) support automatic versioning (tracking changes), corruption checks (integrity verification), KPIs (e.g., commit frequency), and user authentication (access control), making this the best fit.

The DA0-002 Data Governance domain includes "data quality control concepts," and source control systems provide the listed capabilities to ensure data integrity and security.

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 Governance domain, focusing on data quality issues that affect analysis. The table contains duplicate rows, which would skew the average score calculation if not addressed.

Student 123: Math (100), Biology (80), Biology (80) – Duplicate Biology score.

Student 234: Math (96), Math (96) – Duplicate Math score.

Student 345: Biology (88) – No duplicates.

Duplication (Option A): The table has duplicate rows (e.g., Student 123’s Biology score of 80 appears twice), which would inflate the average if not removed. The analyst should have checked for duplicates before calculating the average.

Redundancy (Option B): Redundancy refers to unnecessary fields (e.g., storing the same data in multiple columns), not duplicate rows.

Binning (Option C): Binning groups data into categories, not relevant for addressing duplicates in averaging.

Grouping (Option D): Grouping (e.g., GROUP BY in SQL) might be part of the solution, but the issue to identify is duplication.

The DA0-002 Data Governance domain includes "data quality control concepts," and checking for duplication is critical to ensure accurate calculations like averages.

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 question pertains to the Visualization and Reporting domain, focusing on report delivery methods. The requirement for delivery every seven days indicates a scheduled, repeating process.

Recurring (Option A): Recurring delivery schedules reports to be generated and delivered at regular intervals (e.g., weekly), which matches the requirement of every seven days.

Ad hoc (Option B): Ad hoc reports are one-time, on-demand reports, not suitable for scheduled delivery.

Custom (Option C): Custom isn’t a standard delivery method; it might refer to tailored reports but doesn’t imply scheduling.

Snapshot (Option D): A snapshot captures data at a specific point, not suitable for recurring delivery.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and recurring delivery is ideal for weekly reports.

This question falls under the Data Acquisition and Preparation domain, focusing on modifying historical data. The task is to add new classification codes to existing data, which involves adding new rows or columns.

Append (Option A): Appending adds new rows to a dataset, which is suitable if the classification codes are new records (e.g., a new table of codes to combine with historical data). If the codes are a new column, a join or update might be used, but append fits the context of adding new data.

Binning (Option B): Binning groups data into categories, not suitable for adding classification codes.

Parsing (Option C): Parsing breaks down data (e.g., splitting strings), not relevant for adding codes.

Union (Option D): Union stacks tables with identical structures, but the task involves adding new data (codes) to historical data, not combining identical tables.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and appending is a common technique for adding new data to historical datasets.

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 Data Concepts and Environments domain, focusing on documentation for understanding data processes. The analyst needs to understand automation of data components, which involves data movement and processes.

Data flow diagram (Option A): A data flow diagram (DFD) visualizes how data moves through systems, including automated processes, making it the best starting point.

Data explainability report (Option B): This is related to AI/ML model transparency, not data automation.

Data dictionary (Option C): A data dictionary defines data elements, not how they’re automated.

Data lineage (Option D): Data lineage tracks data origin and transformations but doesn’t focus on automation processes.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data flow diagram is key for visualizing automation.

This question falls under the Data Concepts and Environments domain, focusing on understanding file formats and their structures. The task is to identify a file type that uses delimiters to separate data.

XML (Option A): XML uses tags to structure data, not delimiters.

HTML (Option B): HTML is a markup language for web pages, not a data file format using delimiters.

JSON (Option C): JSON uses key-value pairs and nested structures, not delimiters like commas.

CSV (Option D): CSV (Comma-Separated Values) uses delimiters (typically commas) to separate data fields, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as file formats like CSV that use delimiters.

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 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.