Merging tables to create a master file that includes all transactions for both online and in-store sales is best described as data consolidation. This process involves combining data from various sources into a single, unified dataset. Data consolidation is essential for providing a comprehensive view of all transactions, which can be used for analysis, reporting, and decision-making purposes.
References: The answer is based on standard data management practices and the definition of data consolidation. No specific external documents were referenced for this response.

Modern APIs (especially RESTful APIs) commonly use JSON (JavaScript Object Notation) as the default data interchange format because:
* It is lightweight, text-based, and human-readable.
* It maps naturally to key-value pairs, arrays, and nested structures.
* Most programming languages and frameworks provide native JSON parsing for API requests and responses.
Why the others are incorrect:
* HTML (A) - is a markup language for structuring web pages, not a primary machine-to-machine data format for APIs.
* ZIF (C) - not a standard data exchange format in this context.
* CSS (D) - is for styling HTML elements, not for data representation.
Therefore, the appropriate format for storing data that is natively used by an API is JSON (B).
CompTIA Data+ Reference (concept alignment):
* DA0-001 Objectives - Data concepts and environments: data formats such as JSON and their use in APIs.
* Study sections discussing structured vs semi-structured formats and how APIs typically consume JSON.

The date parameters that the analyst should use are Q3 2019 vs. Q3 2020, as this will allow the analyst to compare the sales performance of the Q3 2020 promotion with the same period of last year. This will help to eliminate any seasonal or cyclical effects that might affect the sales data. The other options are not relevant for this purpose, as they either compare different quarters or different years. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

Tracking sales revenue against quarterly targets is about understanding:
* How actual results compare to predefined goals, and
* Whether the business is meeting, exceeding, or falling short of expectations.
In CompTIA Data+ terminology, this falls under performance analysis, which focuses on:
* Comparing actual metrics to benchmarks, targets, or KPIs
* Measuring goal attainment (e.g., attainment %, variance vs target)
* Supporting decisions to improve performance where there are gaps
Why other options are not the best fit:
* Trend (A): Focuses on direction and pattern over time (e.g., up or down), but not directly on comparison against targets.
* Link (C): Generally related to relationships or associations between variables, not performance vs goals.
* Scope (D): Refers to the breadth or boundaries of a project or analysis, not measuring goal attainment.
Since the key phrase is "against quarterly targets", this is clearly a Performance (B) analysis use case.
CompTIA Data+ Reference (concept alignment):
* DA0-001 Exam Objectives - Data analysis: tracking KPIs and performance against targets.
* Study material that distinguishes trend analysis (pattern over time) from performance analysis (actual vs target).

Text data type is the most flexible for recording emails, as it can handle any combination of letters, numbers, and special characters (e.g., @, .). Alphanumeric types may restrict special characters, but text fields ensure the required flexibility for accurate email recording.
CompTIA Data+ Reference:
CompTIA Data+ Study Guide (Exam DA0-001), Chapter 2: Data Types and Structures, Section "Using Text Fields for Flexible Input", Official CompTIA CertMaster Learn for Data+, Module 2.1 "Text Data Types".
References Used:
* CompTIA Data+ Study Guide (Exam DA0-001) - Official Sybex/CompTIA Book
* CompTIA CertMaster Learn for Data+ - Official CompTIA Learning Modules
* CompTIA Data+ Objectives (DA0-001)