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

This question pertains to theData Concepts and Environmentsdomain, 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 theData Concepts and Environmentsdomain of CompTIA Data+ DA0-002, which involves understanding different types of data repositories and their characteristics. The task is to identify a repository that can store both unstructured and structured data.

Data store (Option A): A data store is a general term for any data repository, but it’s not specific enough to confirm it stores both unstructured and structured data.

Data silo (Option B): A data silo is an isolated data repository, often structured, and not typically designed for unstructured data.

Data mart (Option C): A data mart is a subset of a data warehouse, focused on structured data for specific business areas, not unstructured data.

Data lake (Option D): A data lake is a centralized repository that stores raw data in its native format, including both structured (e.g., tables) and unstructured (e.g., text, images) data, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is specifically designed to handle both unstructured and structured data.

This question falls under theData Concepts and Environmentsdomain, 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 falls under theVisualization and Reportingdomain 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 falls under theData Acquisition and Preparationdomain of CompTIA Data+ DA0-002, focusing on data manipulation techniques. The task is to combine FirstName and LastName into a single FullName field (e.g., "JohnDoe").

Concatenate (Option A): Concatenation combines two or more strings into one (e.g., usingCONCAT in SQL or "+" in Python), which is the correct method to create FullName from FirstName and LastName.

Filter (Option B): Filtering selects specific rows based on conditions, not suitable for combining fields.

Join (Option C): Joining combines data from multiple tables, but the task involves manipulating data within a single table.

Group (Option D): Grouping (e.g., GROUP BY in SQL) is for aggregation, not for combining fields into a new column.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and concatenation is the standard technique for combining fields like FirstName and LastName into FullName.

This question pertains to theData Acquisition and Preparationdomain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from varioussources, 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 theVisualization and Reportingdomain, 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.

This question falls under theData Analysisdomain, 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 falls under theData Governancedomain, 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 notificationcan 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.