Question

A data analyst deployed a report for public access. A user says the report is not showing the latest information, even though the source data was updated an hour ago.

Which of the following should the data analyst check first?

Options:
A. Event log
B. User privileges
C. Database connection
D. Report corruption


Correct Answer: C. Database connection

Question Restatement

The question asks: If a report isn’t showing fresh data even though the source has been updated, what’s the first thing to verify?

Correct Answer Justification — Why C Is Correct

If a report isn’t showing updated data, the most likely cause is the data source connection.

Key reasons:

• The report might be connected to an old or cached dataset instead of the live database.
• The connection might have failed or timed out, preventing new data from loading.
• Scheduled refresh or direct-query settings may not be configured correctly.

• A. Event log: Logs may provide historical error details but don’t directly verify if the report is connected to the correct, live data source. This is secondary to checking the connection.

• B. User privileges: If a user lacked permissions, they’d likely see errors or restricted fields, not outdated data. The question explicitly says the source was updated, not blocked.

• D. Report corruption: Corruption would cause errors or broken visuals, not just stale data. This is much less likely than a connection or refresh issue.

• Database Connection: The link between a report/dashboard and its underlying data source.
• Data Refresh: The process of pulling updated data from a source into a report or dashboard.
• Caching: Temporarily storing data to improve performance; may show outdated data if not refreshed.
• Direct Query vs. Import Mode: Direct query pulls live data; import mode stores a snapshot that needs refreshing.

• Power BI/Tableau: A dashboard fails to update because the scheduled refresh didn’t run or the credentials expired.
• SQL Reporting: A report points to a test database instead of production, showing old data.
• Cloud Data Source: The analyst must re-authenticate the connection after a password change.

• CompTIA Data+ Exam Objectives: Domain 5 (Data Reporting and Visualization).
• Microsoft Power BI “Gateway and Data Refresh” documentation.
• Tableau “Live vs. Extract” data connection guide.

• Jumping to permissions or corruption before checking the data source connection.
• Forgetting that even an hour-old update requires a refresh or live connection.
• Not differentiating between live data connections and snapshots/extracts in reporting tools.

• Domain 5: Data Reporting and Visualization — managing report refresh and data connectivity.

Question Restatement

A data analyst wants to understand how data components and processes are automated within a system. The question asks: Which type of documentation should the analyst consult first to see this flow clearly?

Correct Answer Justification — A. Data Flow Diagram

A Data Flow Diagram (DFD) visually depicts how data moves through systems, processes, and storage. It’s the first and best documentation to review when you want to see automation points:

• Inputs, outputs, and processes are illustrated.
• Automated tasks (ETL steps, scripts, scheduled jobs) are easy to identify.
• It helps analysts quickly pinpoint dependencies, bottlenecks, and system boundaries.

• B. Data Explainability Report: Used mainly in AI/ML to show why a model made a decision (model transparency). It does not describe automation or data component movement.
• C. Data Dictionary: Lists metadata, field names, data types, and definitions. Great for understanding what data means, but it won’t show how the data flows or is automated.
• D. Data Lineage: Shows where data came from and how it has been transformed over time. It’s excellent for auditability and compliance but less useful than a DFD for seeing current process automation.

• Data Flow Diagram (DFD): A process map of how data enters, moves through, and leaves a system.
• Automation Components: ETL jobs, scripts, APIs, and scheduled processes that handle data automatically.
• System Boundaries: Points where data crosses from one system to another.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 1: “Identify methods of data acquisition and environments.”
• Lucidchart & Visio guides on Data Flow Diagrams (industry-standard tools for DFD creation).
• DAMA-DMBOK Guide (Data Management Body of Knowledge) on Data Architecture and process mapping.

Question Restatement

You have an Individual table with columns FirstName and LastName. The desired output should create a new field FullName that combines these two columns (e.g., John + Doe = JohnDoe). Which action best accomplishes this?

Correct Answer Justification — A. Concatenate
Concatenation is the process of combining two or more strings/fields into one new string/field.

• In SQL, this could look like:

• This directly produces JohnDoe in a new FullName column.
• This matches CompTIA Data+ Domain 2: Data Mining and Domain 3: Data Analysis where creating derived fields or calculated columns is a core skill.

• B. Filter: Filters rows based on conditions (e.g., WHERE FirstName = 'John'). It doesn’t combine fields.
• C. Join: Combines tables, not columns, based on matching keys. Useful for merging datasets, not concatenating fields within one table.
• D. Group: Groups rows by common values to aggregate data (e.g., GROUP BY Region). It’s not about string manipulation.

• Concatenate: The operation of merging multiple text strings into one.
• Derived Field / Calculated Column: A new column created from existing columns (like FullName from FirstName + LastName).

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 3 “Apply data transformation techniques.”
• SQL CONCAT documentation (MySQL, SQL Server, PostgreSQL).
• ETL best practices for creating calculated fields.

Question Restatement

A data analyst must provide a weekly sales report specifically for the Chief Financial Officer (CFO). Which delivery method would be most appropriate for a senior executive who likely wants quick, strategic insights rather than raw details?

Correct Answer Justification — D. A High-Level Email
Senior executives like a CFO typically need summary-level, high-impact insights rather than deep operational details. A high-level email:

• Summarizes key metrics and trends (week-over-week sales, major changes, risks).
• Presents actionable takeaways quickly without overwhelming the recipient.
• Aligns with the CompTIA Data+ Domain 4 (“Visualization” & “Communication”) objectives, which emphasize tailoring delivery to your audience’s needs and level of detail.
• Supports business decision-making by focusing on strategic-level KPIs rather than granular data.

• A. A granular daily report in a dashboard: Too detailed and frequent for a CFO; would overwhelm instead of inform. Dashboards are great for analysts or managers but not for executive summaries.
• B. A detailed text document: Useful for technical or compliance reviews, but not efficient for a CFO’s quick decision-making.
• C. A spreadsheet with raw data: Gives unfiltered, raw numbers—requires time to analyze, which a CFO typically doesn’t have. Not actionable without summarization.

• Executive Reporting: The practice of tailoring content to senior leadership with concise, relevant metrics.
• Data Storytelling: Turning numbers into a clear narrative for decision-makers.
• Audience-Centered Communication: Matching the reporting format to the recipient’s role and decision-making needs.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 4.2 (“Use the appropriate type of data presentation”).
• Harvard Business Review — “How to Present Data to Executives.”
• Data Storytelling for Business by Cole Nussbaumer Knaflic (on tailoring data presentations to audiences).

Question Restatement

A data analyst wants to combine the data from two tables (Employees and Managers) into a single output without duplicates. Which SQL statement is best suited for this task?

Correct Answer Justification — B. UNION

• The SQL UNION operator combines the results of two or more SELECT statements and removes duplicates automatically.
• Key Point: By default, UNION returns distinct rows only (deduplicated).

• This is the correct choice for merging tables while eliminating duplicates without extra filtering.

• A. UNION ALL: Combines rows but keeps duplicates. This is faster but not suitable when deduplication is required.
• C. JOIN: Combines rows based on a matching condition between tables (e.g., ON Employees.ID = Managers.ID). Joins do not inherently remove duplicates and are not used simply to stack data vertically.
• D. CROSS JOIN: Produces the Cartesian product of the two tables (every row of Employees combined with every row of Managers). This massively increases rows and is unrelated to deduplication.

• UNION: Combines two result sets into one and removes duplicates.
• UNION ALL: Combines two result sets and keeps duplicates.
• JOIN: Combines data horizontally based on matching columns.
• CROSS JOIN (Cartesian Join): Matches each row from one table with all rows from another.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 3.3: “Combine data from multiple sources using appropriate methods.”
• SQL Standards (ANSI SQL): UNION vs. UNION ALL.
• W3Schools SQL UNION Reference.

Question Restatement

A developer creates an online survey where all questions must be answered (no question can be skipped). Which type of data inconsistency does this requirement prevent?

Correct Answer Justification — A. Missing Values
When survey questions are mandatory, the system ensures that each data field receives a response. This directly prevents missing values (nulls) in the dataset:

• Missing values occur when no response is recorded for a field.
• By making questions required, the developer enforces data completeness at the point of entry.
• This aligns with CompTIA Data+ Domain 2 (Data Quality Concepts), which covers ensuring data accuracy and completeness.

• B. Duplication: Mandatory answers do not stop duplicate responses (e.g., the same person answering twice). Deduplication requires separate logic.
• C. Data Corruption: This refers to data being altered incorrectly during storage or transmission — requiring security/integrity controls, not required fields.
• D. Completeness: While completeness is the overall goal, the specific issue being addressed here is missing values. “Completeness” is a data quality dimension, not an inconsistency type.

• Missing Values (Nulls): Fields left blank or unanswered, reducing data usability.
• Data Completeness: A dimension of data quality measuring the presence of all required values.
• Validation Rules: Constraints or settings (like “required fields”) to enforce data entry standards.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 2.2: “Apply data quality control processes.”
• DAMA-DMBOK Data Quality Framework — Missing Data Handling.
• NIST Data Quality Guidelines (for ensuring completeness at collection time).

Question Restatement

You’re asked which tool or system supports features like automatic versioning, corruption checks, KPIs, and user authentication. Which option best fits?

Correct Answer Justification — D. Source Control
Source Control (also called Version Control) systems — such as Git, GitHub, or Azure DevOps — are designed to:

• Automatically version files/code: Every change creates a new version, enabling rollbacks.
• Check for corruption or conflicts: Integrity checks prevent accidental corruption.
• Provide KPIs (Key Performance Indicators): Many source control platforms track commits, issues, and deployment metrics.
• Offer user authentication and permissions: Access controls ensure only authorized users can commit or deploy changes.

• A. Notebook: A notebook (like Jupyter or Databricks) is for data exploration and visualization, not versioning or access control.
• B. REST API: This is a method of connecting to and exchanging data between systems; while it may use authentication, it doesn’t provide automatic versioning or corruption checks by itself.
• C. Pipeline: Refers to automated data processing or ETL workflows. While pipelines may have logging or scheduling, they don’t inherently do version control or manage KPIs for code integrity.

• Source Control / Version Control: A system for tracking and managing changes to code or configurations (Git, Subversion, Mercurial).
• Versioning: Creating and tracking multiple versions of files to ensure historical and rollback capabilities.
• Integrity/Corruption Checks: Mechanisms like hashing or diff-checking to verify files haven’t been damaged.
• Authentication and Authorization: Processes to ensure only verified users access or modify files.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 5: “Implement and maintain data governance.”
• Git Documentation — versioning, access control, and integrity checks.
• NIST Cybersecurity Framework — principles of authentication and integrity for software artifacts.

Question Restatement

A data analyst notices that a report showing employee sales only displays data for the current month, not historical data. What is the most likely cause?

Correct Answer Justification — B. An Error in SQL Code

If a report only shows current month data rather than all available months, the most likely cause is a filter or condition in the SQL query that’s unintentionally restricting the results.

Examples:

• A WHERE clause like WHERE MONTH(SalesDate) = MONTH(CURRENT_DATE)
• Using a join or subquery that only returns current records
• Applying a parameter incorrectly
• This is classic query logic error, not a refresh or permission issue.

• A. Lack of permissions: If permissions were lacking, the analyst likely wouldn’t see any data or certain columns at all — not just current month records.
• C. Report refresh failure: A refresh failure usually results in outdated or stale data, not systematically filtered data for one time period.
• D. Connectivity issues: Connection problems cause errors, timeouts, or no data returned at all — not a perfectly filtered current month dataset.

• SQL WHERE Clause: Filters rows in a query based on conditions.
• Data Filters: Parameters or logic applied to limit returned data.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 3.2: “Apply the appropriate type of query to meet business requirements.”
• W3Schools — SQL WHERE, GROUP BY, HAVING clauses.
• DAMA-DMBOK — Data Quality and Accuracy best practices.

Question Restatement

A grocery store wants to view revenue from the previous year, broken down by individual departments (e.g., Produce, Bakery, Dairy). Which chart type is most appropriate to communicate the composition of revenue by category?

Correct Answer Justification — B. Pie Chart

• A pie chart is designed to show the composition or percentage breakdown of a whole into categories.
• In this scenario, the “whole” is total yearly revenue, and each slice represents a department’s share.
• It is visually clear for stakeholders to see which departments generate more or less revenue.

• A. Gantt: Used for project timelines and scheduling, not for displaying revenue or composition data.
• C. Area: Best for showing trends over time with stacked areas — useful if we wanted to see how each department’s revenue changed monthly, but not for one-year composition.
• D. Line: Shows continuous trends over time. It’s ideal for tracking monthly or quarterly revenue, but not for static composition in one period.

1. Pie Chart: A circular graph divided into slices to illustrate numerical proportions.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 4.1: “Use appropriate data visualization methods.”
• Data Visualization Best Practices — Choosing the Right Chart (Microsoft Power BI / Tableau guides).
• Cole Nussbaumer Knaflic, Storytelling with Data: Visualizing composition data.

Question Restatement

You’re asked which type of data repository stores unformatted data in its original, raw form. The options include data warehouse, data silo, data mart, and data lake.

Correct Answer Justification — D. Data Lake

A data lake is specifically designed to store large volumes of raw, unstructured, and semi-structured data in its native format.

• It can handle structured data (tables), semi-structured data (JSON, XML), and unstructured data (images, videos, logs).
• Data is stored first, then transformed as needed (schema-on-read rather than schema-on-write).
• It supports advanced analytics, machine learning, and big data processing.
• This directly aligns with CompTIA Data+ Domain 1 (Data Concepts and Environments), which covers data storage methods.

• A. Data Warehouse: Stores structured and cleaned data, optimized for analytics and reporting. Data is transformed before loading (schema-on-write).
• B. Data Silo: Refers to an isolated repository controlled by a single department or system, often inaccessible to others. Not necessarily raw or unformatted.
• C. Data Mart: A subset of a data warehouse focused on a specific subject area (e.g., sales or finance). Data here is structured and curated.

• Data Lake: A centralized repository for storing all types of data in their raw form.
• Schema-on-Read: Data structure is applied only when the data is read, allowing flexibility.
• Data Warehouse: A repository for clean, transformed, and structured data ready for business intelligence (BI).
• Data Mart: Departmental or subject-specific slice of a warehouse.
• Data Silo: An isolated data store, often a byproduct of poor data governance.

• CompTIA Data+ (DA0-002) Exam Objectives, Domain 1.3: “Compare and contrast common data structures and file formats.”
• DAMA-DMBOK: Data Storage and Operations best practices.
• Microsoft Azure / AWS / Google Cloud docs on data lakes vs. warehouses.