Aggregate functions, such as SUM(), AVG(), or COUNT(), are designed to operate on a group of rows to produce a single summary value. This calculation is finalized only after the Integration Service has processed all rows within a group. Variable ports in an Aggregator transformation are evaluated for each input row as it is processed, and their values can be used for row-by-row calculations. Because of this fundamental difference in processing logic (group-level vs. row-level), aggregate functions cannot be used within variable port expressions. They are exclusively used in the output ports of an Aggregator transformation.

B. This is incorrect. Aggregate functions in Informatica support an optional filter condition, allowing the function to process only those rows that satisfy the specified criteria.

C. This is incorrect. The argument for an aggregate function can be any valid expression, which can include arithmetic operators (e.g., SUM(PRICE QUANTITY)).

D. This is incorrect. This statement describes the core purpose of an aggregate function: to consolidate data from multiple rows within a group into a single, representative value.

1. Informatica PowerCenter - 10.4.0 Transformation Guide

"Chapter 3: Aggregator Transformation"

"Rules and Guidelines for an Aggregator Transformation"

Page 58.

The documentation explicitly states: "You can use aggregate functions only in output ports. You cannot nest aggregate functions or use them in input or variable ports." This directly supports that option A is not true.

2. Informatica PowerCenter - 10.4.0 Transformation Guide

"Chapter 3: Aggregator Transformation"

"Aggregate Expressions"

Page 60.

This section details the syntax for aggregate functions

including the optional filter condition: AGGREGATEFUNCTION( port | expression

[filtercondition] ). This confirms that option B is a valid capability. The ability to use an expression as the first argument confirms option C is also valid.

3. Informatica PowerCenter - 10.4.0 Transformation Guide

"Chapter 3: Aggregator Transformation"

"Aggregator Transformation Overview"

Page 54.

The guide states

"The Aggregator transformation lets you perform aggregate calculations

such as averages and sums

against groups of data." This confirms the fundamental principle described in option D.