“Role-based and group-based requirements” refer to:

Who can see or modify specific data.

Which roles (e.g., analyst, manager, admin) or groups (e.g., HR, Finance) are granted particular permissions.

In CompTIA Data+ terminology, these are Access requirements:

Define which users/roles/groups can access which data, at what level (read, write, update, delete).

Often implemented through role-based access control (RBAC) and group permissions.

Why the other options don’t fit as closely:

Security requirements (A) – broader category: includes encryption, masking, network security, etc.; access is part of security, but the phrase “role-based and group-based requirements” specifically aligns with access control.

Storage requirements (B) – deal with where and how data is stored (capacity, media, performance, backup).

Use requirements (D) – govern how data can be used (allowed purposes, ethical/legal constraints), not who can access it.

Therefore, an organizational document defining role-based and group-based requirements is specifying Access requirements (C).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Data governance: access control, roles, and permissions.

Study discussions of role-based access control (RBAC) and how access requirements are documented.

When designing a database field to capture a binary preference, such as whether a customer likes vanilla ice cream, the most appropriate data type is:

Option B:Boolean

Rationale:A Boolean data type is used to represent binary values, typically TRUE or FALSE. In this context, it efficiently captures whether a customer likes (TRUE) or does not like (FALSE) vanilla ice cream.

Option A:Integer

Rationale:While integers represent whole numbers, using them to denote binary choices (e.g., 1 for "likes" and 0 for "dislikes") is less intuitive and can lead to ambiguity without proper context.

Option C:Categorical

Rationale:Categorical data types are used for fields that can take on one of a limited set of values, representing different categories. While "likes" and "dislikes" could be categories, a Boolean is more efficient for binary choices.

Option D:Numeric

Rationale:Numeric data types encompass both integers and floating-point numbers. Using a numeric type for a binary preference is unnecessary and could lead to data integrity issues.

This is because sales year to date refers to the sales that have occurred from the beginning of the current year until the current date. By creating a sales report that identifies sales year to date, the analyst can measure and compare the sales performance and progress of the current year. Since the analyst is asked to create the sales report on the morning of April 9, 2020, and the daily sales data is current through the end of the day, the date range that should be on the report is January 1, 2020 to April 9, 2020. The other date ranges are not correct for identifying sales year to date. Here is why:

January 1, 2020 to April 1, 2020 would not include the sales that occurred in the first eight days of April, which would underestimate the sales year to date.

January 1, 2020 to April 7, 2020 would not include the sales that occurred in the last two days of April, which would also underestimate the sales year to date.

January 1, 2020 to April 8, 2020 would not include the sales that occurred on April 9, which would also underestimate the sales year to date.

In CompTIA Data+ terminology, a derived variable (or derived field) is one that is calculated from other existing variables rather than stored as a raw value.

In this case, volume is not entered directly into the data set.

It is calculated using the formula:

That makes volume a classic example of a derived variable.

Why the other options are incorrect:

Normalized (B): Refers to restructuring data (often in databases) to reduce redundancy and improve integrity (1NF, 2NF, 3NF, etc.), not to a type of variable.

Concatenated (C): Typically refers to joining strings or fields together (e.g., first_name + last_name), not multiplying numeric measures.

Aggregated (D): Refers to combining multiple records, often using summary functions (SUM, AVG, COUNT) across groups. Volume is not a summary across multiple rows; it is a row-level calculated metric.

Therefore, Derived (A) is the correct variable type to describe volume as computed from other dimensions.

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Domain: Data Concepts and Environments (calculated/derived fields).

CompTIA Data+ Official Study Guide – sections describing derived variables/fields created via formulas from other columns.

Explanation

A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.

What is the mode?

The mode is the most commonly occurring value in a distribution.

The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.

When designing a database schema that represents a central entity with multiple levels of related subcategories, it's crucial to choose a structure that efficiently models these relationships.

Option A:Relational

Rationale: A relational database organizes data into tables with rows and columns, using keys to establish relationships between tables. While flexible, the relational model doesn't inherently represent hierarchical relationships, making it less ideal for schemas requiring parent-child data representation.

Option B:Hierarchical

Rationale: The hierarchical database model structures data in a tree-like format, with a single root (central group) and multiple levels of nested subcategories (parent-child relationships). This model is well-suited for scenarios where data is naturally hierarchical, such as organizational charts or file systems.

This is because web scraping is a technique that allows the analyst to extract data from web pages, such as articles from a digital news outlet. Web scraping can be done using various tools and methods, such as Python libraries, browser extensions, or online services. The other techniques are not suitable for acquiring data from web pages. Here is why:

Sampling is a technique that involves selecting a subset of data from a larger population, usually for statistical analysis or testing purposes. Sampling does not help the analyst to acquire data from web pages, but rather to reduce the amount of data to be analyzed.

Data wrangling is a technique that involves transforming and cleaning data to make it suitable for analysis or visualization. Data wrangling does not help the analyst to acquire data from web pages, but rather to improve the quality and usability of the data.

ETL stands for Extract, Transform, and Load, which is a process that involves moving data from one or more sources to a destination, such as a data warehouse or a database. ETL does not help the analyst to acquire data from web pages, but rather to store and organize the data.