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

This question falls under the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores data in its original, unaltered form.

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

Data warehouse (Option B): A data warehouse stores processed, structured data, often transformed for analysis, not unaltered.

Data table (Option C): A data table is a structure within a database, not a repository, and may contain altered data.

Data factory (Option D): A data factory (e.g., Azure Data Factory) is a data integration service, not a repository for storing data.

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

This question pertains to the Visualization and Reporting domain, focusing on selecting the appropriate visualization to show a relationship between two continuous variables (ages and incomes).

Scatter plot (Option A): A scatter plot displays individual data points on two axes (age vs. income), making it ideal for showing the relationship and potential correlation between two continuous variables.

Area plot (Option B): Area plots are used for showing trends over time, not relationships between two variables.

Bar chart (Option C): Bar charts are better for categorical data comparisons, not continuous variable relationships.

Pie chart (Option D): Pie charts show proportions of a whole, not suitable for showing relationships between variables.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a scatter plot is best for showing the relationship between age and income.

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 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 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 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 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 Data Analysis domain, focusing on identifying data quality issues. The table shows counts over time, and the analyst needs to flag an issue in the data.

Completeness (Option A): Completeness refers to missing data, but all rows have values for DateTime and Count.

Outlier (Option B): The count of 245 on 2024-01-02 is significantly higher than other counts (12-13), indicating an outlier that should be investigated for accuracy.

Mismatch (Option C): Mismatch implies inconsistent data types or formats, but the DateTime and Count columns appear consistent except for the last row (addressed separately).

Duplication (Option D): Duplication refers to identical rows, but no rows are identical (same DateTime and Count).

The last row ("00:00:00", 12) has a formatting issue, but the most significant issue for analysis is the outlier (245), as it could skew results. The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as identifying outliers in datasets.

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 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 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 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 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 is part of the Visualization and Reporting domain, focusing on delivery methods for dashboards. The requirement for displaying financial transactions "as they are updated" implies a need for real-time updates and interactivity, which narrows down the options.

Real-time (Option A): Real-time delivery ensures the dashboard reflects the latest data as transactions are updated, meeting the requirement.

Snapshot (Option B): A snapshot provides a static view at a specific point, not suitable for ongoing updates.

Dynamic (Option C): A dynamic dashboard allows for interactivity and can be updated as data changes, complementing real-time delivery.

Static (Option D): Static dashboards don’t update automatically, making this incorrect.

Ad hoc (Option E): Ad hoc delivery is for one-time reports, not ongoing updates.

Time series (Option F): Time series refers to a data type or visualization, not a delivery method.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report or dashboard" with delivery methods Real-time and dynamic methods best support the need for updated financial transaction dashboards.

This question falls under the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a specific data trend. The task is to show sales trends over time (year over year).

Line (Option A): Line charts are ideal for displaying trends over time, such as year-over-year sales, as they clearly show changes and patterns across a continuous time axis.

Donut (Option B): Donut charts show proportions or percentages of a whole, not suitable for time-based trends.

Bubble (Option C): Bubble charts display three dimensions of data (e.g., size, x-axis, y-axis), not ideal for simple time trends.

Hierarchy (Option D): Hierarchy charts (e.g., treemaps) show nested relationships, not time-based trends.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a line chart is best for time-series trends.

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 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 Acquisition and Preparation domain, focusing on combining tables to produce a dataset. The task requires combining the Courses and Students tables to include student names with course details, based on the StudentID.

Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining tables to include names.

Join (Option B): A join operation (e.g., INNER JOIN on StudentID) combines the tables, matching records to produce the requested output with CourseID, SectionNumber, StudentID, FirstName, and LastName.

Group (Option C): Grouping is used for aggregation (e.g., GROUP BY in SQL), not for combining tables.

Filter (Option D): Filtering selects specific rows, not relevant for combining tables.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables to create a unified dataset.

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 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 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 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 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 Governance domain, focusing on data breach response protocols. A data breach requires a structured response to comply with legal and regulatory requirements.

Make an announcement on social media so customers are aware as soon as possible (Option A): Public announcement without internal coordination or regulatory notification can lead to legal issues and loss of trust.

Tell the company management team and then tell regulatory agencies (Option B): This follows best practices: inform internal leadership to coordinate a response, then notify regulatory agencies as required by laws (e.g., GDPR mandates notification within 72 hours).

Keep the incident a secret until the issue is resolved (Option C): This violates regulations requiring timely breach notification.

Inform the entire IT sector, but ask for discretion (Option D): Sharing with the IT sector is vague and risks leaks; regulatory agencies should be prioritized.

The DA0-002 Data Governance domain includes "data privacy concepts," such as proper breach response procedures, emphasizing internal and regulatory notification.

This question pertains to the Data Concepts and Environments domain, focusing on tools for interacting with relational databases. The task is to identify the best method for retrieving and manipulating data.

XML (Option A): XML is a data format, not a language for retrieving or manipulating database data.

SQL (Option B): SQL (Structured Query Language) is specifically designed for querying and manipulating data in relational databases (e.g., SELECT, UPDATE), making it the best choice.

Excel (Option C): Excel can analyze data but isn’t designed for direct database manipulation.

JavaScript (Option D): JavaScript is a programming language for web development, not optimized for relational database operations.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases," and SQL is the standard language for relational database operations.

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 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 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 Acquisition and Preparation domain, focusing on combining data from multiple tables. The analyst needs specific columns from each table, suggesting a method to combine data horizontally based on relationships.

Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining columns from tables.

Union (Option B): Union stacks tables vertically, requiring identical structures, but the analyst needs specific columns, likely based on relationships, not a vertical stack.

Nest (Option C): Nesting is used for hierarchical data (e.g., JSON), not for combining relational tables.

Join (Option D): A join (e.g., INNER JOIN) combines tables horizontally based on a common key, allowing the analyst to select specific columns from each table, which fits the task.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and joining tables is the best method for combining specific columns from multiple tables.

This question is part of the Visualization and Reporting domain, focusing on selecting the appropriate report type for a specific purpose. The manager needs a recurring report on incomplete timesheets to guide outreach, which requires a concise, data-driven report.

Summary (Option A): A summary report aggregates data (e.g., total incomplete timesheets per employee) and presents it concisely, making it ideal for recurring use to guide decisions like employee outreach.

Infographic (Option B): Infographics are visual representations for broad audiences, not typically used for recurring, detailed employee outreach tasks.

Snapshot (Option C): A snapshot report captures data at a specific point in time, but it’s not ideal for recurring analysis of trends or aggregates.

Ad hoc (Option D): Ad hoc reports are one-time, on-demand reports, not suitable for recurring needs.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" , and a summary report best fits the need for recurring, actionable data.