Authentic CompTIA Certification Exam DA0-002 Questions Answers

This question falls under the Data Concepts and Environments domain, focusing on types of databases. The task is to identify a NoSQL database among the options.

PostgreSQL (Option A): PostgreSQL is a relational (SQL) database, not NoSQL.

MySQL (Option B): MySQL is a relational (SQL) database, not NoSQL.

Oracle (Option C): Oracle Database is a relational (SQL) database, not NoSQL.

MongoDB (Option D): MongoDB is a NoSQL database that uses a document-based model, storing data in JSON-like structures, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and MongoDB is a well-known NoSQL database.

This question pertains to the Data Analysis domain, focusing on statistical measures. The middle 50% of a dataset refers to a specific statistical concept related to data distribution.

Interquartile range (Option A): The interquartile range (IQR) is the range between the first quartile (Q1, 25th percentile) and the third quartile (Q3, 75th percentile), representing the middle 50% of the data, which matches the description.

The difference between mode and median (Option B): This measures the spread between two central tendency metrics but doesn’t represent the middle 50% of the data.

Mean variance (Option C): Variance measures data dispersion around the mean, not the middle 50%.

Skewness from the slope (Option D): Skewness measures data asymmetry, and "slope" is irrelevant here.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the IQR is the standard measure for the middle 50% of a dataset.

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.