The Filter transformation's primary function is to test each incoming row against a specified condition. This condition must be an expression that evaluates to a boolean value: TRUE or FALSE. The literal FALSE is a valid boolean constant in the Informatica Transformation Language. When FALSE is used as the filter condition, it is a syntactically valid expression that will evaluate to FALSE for every row that passes through the transformation. Consequently, the Integration Service will drop all rows, which is a valid and predictable filtering behavior.

The option "False" would be incorrect. The requirement for the Filter transformation condition is that it must be a valid expression that returns a boolean result. The keyword FALSE is a recognized boolean constant and therefore constitutes a valid expression. While it results in filtering out all data, it is nonetheless a functionally and syntactically correct condition.

1. Informatica PowerCenter - Transformation Guide

Version 10.5

"Filter Transformation" chapter

Section: "Filter Condition": "The Filter condition is an expression that returns TRUE or FALSE... If the condition evaluates to FALSE

the Integration Service drops the row and reads the next row." This confirms that any expression evaluating to FALSE is processed according to the transformation's rules. The literal FALSE is the most direct expression that meets this criterion.

2. Informatica PowerCenter - Expression Reference

Version 10.5

"Constants" chapter

Section: "Boolean Constants": "You can use the constants TRUE and FALSE in an expression. The Data Integration Service recognizes TRUE and FALSE as 1 and 0

respectively

when you use them in a numeric expression." This document explicitly lists FALSE as a valid constant that can be used in any expression

including a filter condition.

3. Kimball

R.

& Caserta

J. (2011). The Data Warehouse ETL Toolkit. Wiley. Chapter 4

"Data Quality

" Section: "The Data Quality Screen": While not an Informatica-specific manual

this foundational text on ETL principles discusses the implementation of data quality screens and filters. It establishes the concept of applying rules that result in records being passed or rejected. A rule that rejects all records (equivalent to a FALSE condition) is a valid

though extreme

implementation of such a filter

often used for debugging

flow control

or disabling a data path during development.