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

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 falls under the Data Analysis domain, focusing on SQL queries to handle duplicates while counting employees. The task is to count unique employees with a salary of $125,000 or more, addressing duplicates in the dataset.

Option A: SELECT DISTINCT Employee_ID FROM Employee WHERE Salary >= 125000This lists unique Employee_IDs but doesn’t provide a count, which the department needs.

Option B: SELECT COUNT(DISTINCT Employee_ID) FROM Employee WHERE Salary >= 125000This counts unique Employee_IDs (using DISTINCT) with a salary of $125,000 or more, correctly addressing duplicates and providing the required count (2 employees: 003 and 004).

Option C: SELECT DISTINCT Employee_ID FROM Employee WHERE Salary > 125000This lists unique Employee_IDs with a salary strictly greater than $125,000 (missing 004), and doesn’t provide a count.

Option D: SELECT COUNT(Employee_ID) FROM Employee WHERE Salary >= 125000This counts all rows without addressing duplicates, resulting in an incorrect count (2 rows, but only 2 unique employees).

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and COUNT(DISTINCT) is the correct method to count unique employees while handling duplicates.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This question is part of the Visualization and Reporting domain, 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 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 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 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 Visualization and Reporting domain, 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.