The data is presented in a CSV (comma-separated values) file format, which is a plain text format that stores tabular data. Each line of the file is a data record, and each record consists of one or more fields separated by commas. The first line of the file usually contains the names of the fields, also known as the header. In this case, the data has four fields: Name, Age, Gender, and Occupation. Therefore, the correct answer is B.
References: CSV File (What It Is & How to Open One), Comma-separated values - Wikipedia

Explanation
A waterfall chart is a type of data visualization that shows the cumulative impact of sequentially introduced values that are positive or negative. A waterfall chart typically has an initial value and a final value, with intermediate values shown as floating columns that either add to or subtract from the initial value. A waterfall chart can help visualize how different factors contribute to a net change in a value over time.
Therefore, the correct answer is B. References: [Waterfall Chart | Definition & Examples - Investopedia], [Waterfall Charts in Excel | How to Create Waterfall Chart in Excel?]
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Explanation
SPSS Modeler.
QuickSight, Tableau, and Power BI are all powerful analytics and reporting tools that can pull data from a variety of sources. SPSS Modeler is a powerful predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise.

A JSON (JavaScript Object Notation) file is a text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa). JSON files are human- readable and can be interpreted by various programming languages, making them ideal for data interchange123.
JSON files typically contain an array of objects, with each object representing a record with a series of name- value pairs. This structured format is both easy to understand and write by humans and easy for machines to parse and generate4.
References:
* JSON's official definition and syntax rules1.
* A beginner's guide to JSON and its data types2.
* Understanding the JSON file format3.
* Detailed explanation of JSON as a structured data format4.

Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:
* Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis1.
* Invalid Data (D): Invalid data includes entries that are out of range, improperly formatted, or illogical (e.
g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions1.
Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn' t assume a normal distribution. Duplicate data and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been processed to fit into a certain range or format and is typically not a reason for data cleansing.
References:
* Understanding the importance of data quality and the impacts of missing and invalid data on research outcomes1.
* Best practices in data cleansing2.
Data cleansing is required for various reasons, two of which are missing data (B) and invalid data (D). From the table provided, we can infer the necessity of cleansing in the context of ensuring data integrity and consistency. Missing data refers to the absence of data where it is expected, which can hinder analysis due to incomplete information. Invalid data refers to data that is incorrect, out of range, or in an inappropriate format, which can lead to inaccuracies in any analysis or report. Both these issues can significantly affect the outcomes of any data-related operations and thus need to be rectified through the data cleansing process.