CompTIA DA0-002 - CompTIA Data+ Exam (2025)

This question pertains to theData Analysisdomain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal,e.g., ratings), requiring appropriate data attributes.

Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.

Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.

Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.

Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and numerical and ordinal attributes are key for forecasting sales and satisfaction.

This question is part of theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a given dataset. The grocery store wants to view revenue by department, which requires a chart that shows proportions or comparisons across categories.

Gantt (Option A): Gantt charts are used for project scheduling, not for comparing revenue across categories.

Pie (Option B): Pie charts are ideal for showing proportions or percentages of a whole, such as revenue distribution across departments, making this the best choice.

Area (Option C): Area charts are better for showing trends over time, not static categorical comparisons.

Line (Option D): Line charts are used for trends over time, not for comparing discrete categories like departments.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization" , and a pie chart is the most appropriate for showing departmental revenue proportions.

This question falls under theVisualization and Reportingdomain, focusing on selecting the appropriate visualization to identify outliers in a dataset.

Box plot (Option A): A box plot displays the distribution of data, including the median, quartiles, and outliers (data points beyond the whiskers), making it the best choice for identifying outliers.

Scatter plot (Option B): A scatter plot shows relationships between two variables, and while outliers may be visible, it’s not specifically designed for outlier detection.

Gantt chart (Option C): Gantt charts are for project scheduling, not suitable for outlier identification.

Waterfall chart (Option D): Waterfall charts show cumulative changes (e.g., financial contributions), not designed for outlier detection.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a box plot is the standard visualization for identifying outliers.

This question falls under theData Analysisdomain, 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 theData Analysisdomain, 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’tvisualizations; 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 theData Concepts and Environmentsdomain, 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 theVisualization and Reportingdomain, 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 theData Analysisdomain, 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 theData Concepts and Environmentsdomain, 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 pertains to theData Analysisdomain, 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.