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

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 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 pertains to the Data Governance domain, focusing on data security and access control principles. The company restricts access to the minimum needed for duties, which aligns with a specific security concept.

Active Directory (Option A): Active Directory is a tool for managing users and permissions, not a concept.

Hierarchical access (Option B): Hierarchical access implies access based on roles in a hierarchy, but it doesn’t specifically focus on minimal access.

Zero Trust (Option C): Zero Trust requires continuous verification for all access, which is broader than just minimal permissions.

Least privilege (Option D): Least privilege ensures users have only the permissions necessary for their duties, which matches the scenario.

The DA0-002 Data Governance domain includes "data privacy concepts," and least privilege is a fundamental principle for secure access control.

This question falls under the Data Acquisition and Preparation domain of CompTIA Data+ DA0-002, which involves acquiring and combining data from different sources, such as a database and a web API, using tools like Python. The task requires joining the data, which in Python often involves using pandas DataFrames.

Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together (Option A): VARCHAR is a database data type for strings, not a format for data exchange or merging in Python, making this incorrect.

Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together (Option B): Web APIs commonly return data in JSON format, and databases can export data as JSON. In Python, JSON data can be easily converted to pandas DataFrames using pandas.read_json() or pandas.DataFrame(), and then merged using pandas.merge() on a common key, making this the best approach.

Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together (Option C): TXT is a generic text format that lacks structure, making it less efficient for merging compared to JSON.

Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together (Option D): Converting to a string format is vague and not a standard approach for structured data merging in Python.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as combining data from APIs and databases, and JSON is a standard format for this purpose in Python.

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.