In an Informatica Expression transformation, variable ports retain their value from the previously processed row. The Integration Service evaluates ports in a specific order: it uses the previous row's variable port value to calculate the current row's output ports, and then it calculates the current row's variable port value for use in the next row.

For row 1, STATE is 'CA'. The variable port vPREVIOUSSTATE is set to 'CA'. For row 2, the output port PREVIOUSSTATE is evaluated first, using the value of vPREVIOUSSTATE from row 1, which is 'CA'.

A. STATE would refer to the value of the STATE port for the current row (row 2), not the value from the previous row.

C. The variable port is not NULL because it was assigned the value 'CA' during the processing of the first row.

D. A default value is used only when a variable port's expression evaluates to NULL on the first row. Here, a non-NULL value was assigned.

1. Informatica PowerCenter 10.5

Developer Guide

Chapter 11: "Expression Transformation"

Section: "Variable Ports".

Reference Detail: This section states

"When you create a variable port in an Expression transformation

the Integration Service uses the value of the variable from the previous row to calculate the current row." This directly supports the logic that the value from row 1 ('CA') is used for the output in row 2.

2. Informatica PowerCenter 10.5

Transformation Language Reference

Chapter 10: "Variables"

Section: "Using Variables in Expressions".

Reference Detail: This section explains the evaluation order: "For each row

the Integration Service evaluates expressions in the following order: 1. Input port expressions. 2. Variable port expressions that do not depend on other variable ports. ... The Integration Service uses the result of the variable port from the previous row in the output port expression for the current row." This confirms the mechanism described in the explanation.

The Informatica Integration Service determines the start value for a mapping variable by following a specific order of precedence. Option A provides the most comprehensive sequence. The process begins with runtime overrides: values from a workflow parameter file are read, then overridden by a session parameter file if specified. A pre-session variable assignment provides a further, higher-priority override. If none of these runtime values are provided, the service uses the value saved in the repository from the previous successful session. If no repository value exists (e.g., on the first run), it uses the initial value declared in the mapping. The ultimate fallback is the default value for the variable's data type (e.g., 0 for a number).

B. This option is incomplete. It correctly orders the parameter files and repository/initial values but omits the pre-session assignment, which is a critical, high-precedence override mechanism.

C. This order is incorrect. The repository value has a lower priority than runtime configurations like parameter files and pre-session assignments, which can override it.

D. This option is highly incomplete. It only shows the lowest levels of the hierarchy and completely ignores the higher-precedence runtime overrides from parameter files and assignments.

1. Informatica PowerCenter 10.5

Advanced Workflow Guide

"Chapter 2: Workflow and Task Variables"

section on "Mapping Variable and Parameter Precedence".

This guide details the definitive order of precedence

stating that the Integration Service uses the following hierarchy to determine a variable's start value: 1. Pre-session variable assignment

2. Value in a parameter file

3. Value saved in the repository

4. Initial value

5. Datatype default value. This confirms that runtime assignments and files have precedence over repository and initial values.

2. Informatica PowerCenter 10.5

Workflow Basics Guide

"Chapter 10: Parameters and Variables in a Workflow"

section on "Parameter and Variable Precedence".

This document corroborates the hierarchy

explaining that values defined closer to the task execution (e.g.

in a session parameter file or pre-session assignment) override values defined at a more general level (e.g.

workflow parameter file or initial value). Option A

while slightly confusing in its ordering of the highest-priority items

lists the complete set of sources in a logical evaluation flow.

In Informatica PowerCenter, the Transaction Control transformation uses five predefined, built-in variables to manage transaction boundaries. These variables are TCCONTINUETRANSACTION, TCCOMMITBEFORE, TCCOMMITAFTER, TCROLLBACKBEFORE, and TCROLLBACKAFTER. The variable TCCOMMITTRANSACTION is not a valid, recognized built-in variable within the Informatica transaction control logic. The commit actions must specify whether the operation occurs before or after processing the current row, which is why TCCOMMITBEFORE and TCCOMMITAFTER exist.

A. TCCONTINUETRANSCTION: Incorrect. Despite the misspelling ("TRANSCTION"), this option clearly refers to the valid variable TCCONTINUETRANSACTION, which instructs the Integration Service to continue processing rows within the current transaction.

C. TCROLLBACKBEFORE: Incorrect. This is a valid built-in variable. It instructs the Integration Service to roll back the current transaction before processing the input row that evaluates to this condition.

D. TC COMMIT AFTER: Incorrect. Despite the improper formatting (space instead of an underscore), this option refers to the valid variable TCCOMMITAFTER, which instructs the Integration Service to commit the transaction after processing the current row.

1. Informatica PowerCenter 10.5

Transformation Language Reference

Chapter 19: Transaction Control Transformation

"Transaction Control Expression" section.

Page 388: This section explicitly lists the five built-in variables for transaction control: TCCONTINUETRANSACTION

TCCOMMITBEFORE

TCCOMMITAFTER

TCROLLBACKBEFORE

and TCROLLBACKAFTER. The variable TCCOMMITTRANSACTION is absent from this official list.

2. Informatica PowerCenter 10.2.0

Transformation Guide

Chapter 22: Transaction Control Transformation

"Configuring a Transaction Control Transformation" section.

Page 435: The guide details the use of the transaction control expression and its variables. It states

"The return value of the expression determines how the Integration Service handles the transaction." It then lists the five valid integer constants

confirming that TCCOMMITTRANSACTION is not a valid constant.

In Informatica PowerCenter, an unconnected transformation is one that is not part of the direct data flow from source to target. Instead, it is called on-demand from another transformation (e.g., an Expression) to perform a specific task and return a value. The Lookup, Stored Procedure, and External Procedure transformations are the primary types that can be configured as either connected or unconnected. An unconnected Lookup is called using :LKP, an unconnected Stored Procedure using :SP, and an unconnected External Procedure using :EXTPROC within an expression. The other transformations listed, Java and Transaction Control, must be connected to the data pipeline to function.

B. Lookup, stored procedure, Java: The Java transformation must be connected to the data pipeline to process rows; it cannot be configured as an unconnected transformation.

C. Java, lookup, transaction control: Both the Java and Transaction Control transformations must be connected. The Transaction Control transformation specifically requires being in the data flow to manage transaction boundaries.

D. Transaction control, Java, lookup: Both the Transaction Control and Java transformations must be connected to the data pipeline and do not support an unconnected configuration.

1. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 1: "Transformation Overview"

Section: "Connected and Unconnected Transformations". This section explicitly lists Lookup

Stored Procedure

and External Procedure as transformations that can be unconnected.

2. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 16: "Lookup Transformation"

Section: "Connected and Unconnected Lookups". This section details the configuration and use of unconnected Lookup transformations

which are called from an expression.

3. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 28: "Stored Procedure Transformation"

Section: "Connected and Unconnected Stored Procedure Transformations". This chapter confirms that a Stored Procedure can be run in unconnected mode to be called from another transformation.

4. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 14: "Java Transformation"

Section: "Java Transformation Overview". The documentation describes the Java transformation as a component within the data flow (either active or passive) and does not mention an unconnected mode.

5. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 30: "Transaction Control Transformation"

Section: "Transaction Control Transformation Overview". This section describes the transformation as an active

connected transformation used to define commit and rollback points for data passing through it.

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.

The Union transformation is designed to merge multiple heterogeneous or homogeneous data streams into a single target stream. For the integration service to process the data correctly, the structure of the input data must be consistent. Therefore, all input groups must have the same number of ports, and the corresponding ports across these groups must have identical metadata attributes: datatype, precision, and scale. This ensures that the data from different sources can be combined into a single, structurally coherent output group without data type conflicts or loss of precision.

B. Mismatched ports or differing datatypes, precisions, or scales between input groups would result in a mapping validation error, as the transformation cannot create a unified output stream.

C. The Union transformation is analogous to the SQL UNION ALL operator. It combines all incoming rows from all input groups and does not identify or remove duplicate records.

D. All port names within a single transformation instance must be unique. Therefore, a port name used in one input group cannot be reused in another input group of the same Union transformation.

1. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 25: Union Transformation

"Rules and Guidelines for Union Transformations"

Page 465.

The guide states: "All input groups and the output group must have matching ports. The precision

data type

and scale for the ports must be identical across all groups." This directly supports answer A and refutes B.

It also states: "The Union transformation does not remove duplicate rows." This refutes answer C.

Regarding port names

the general transformation rule applies: "Port names must be unique within the transformation." This refutes answer D.

2. University of Washington

Data Warehousing Course Material (CSE 590DB)

Lecture on ETL Transformations.

Course notes often describe the Union transformation's function as a UNION ALL operation

requiring schematic consistency across inputs. The material specifies that for a successful merge

the "number

order

and data types of the columns must be the same" for all input data sets

which aligns with the requirements stated in option A.

The Aggregator transformation requires all rows for a particular group to be processed within the same partition to ensure accurate calculations. The default partition type, Hash auto-keys, accomplishes this by using the defined group-by ports as the key for a hash function. The Data Integration Service then distributes rows to different partitions based on the resulting hash values. This guarantees that all records belonging to the same group are sent to the same partition. The fact that the input is sorted is a performance optimization that allows the transformation to use less memory, but it does not change the fundamental partitioning requirement for grouping.

A. Pass-through: This option does not redistribute data between partitions. It would lead to incorrect aggregate results unless the upstream transformation had already partitioned the data by the same group-by keys.

C. Not a valid partition points: The Aggregator is an active transformation and is one of the most common and valid points in a mapping to introduce a partition.

D. Based on the transformation scope: This is not a valid or recognized partition type within the Informatica PowerCenter platform. Partition types are specific algorithms like Hash, Key Range, or Round-Robin.

1. Informatica PowerCenter 10.5

Transformation Guide

Chapter 3: "Aggregator Transformation"

Section: "Partitioning the Aggregator Transformation"

p. 65.

Quote/Paraphrase: "When you add a partition point at the Aggregator transformation

the Workflow Manager sets the partition type to Hash Auto-Keys. The Data Integration Service uses the group by ports as the key to group data among partitions."

2. Informatica PowerCenter 10.5

Advanced Workflow Guide

Chapter 2: "Working with Partitions"

Section: "Hash Auto-Keys Partitioning"

p. 42.

Quote/Paraphrase: "The Data Integration Service uses hash auto-keys partitioning as the default for transformations with group-by ports

such as the Aggregator

Rank

and Sorter transformations. It groups data based on the ports that define the key."

An active transformation is one that can change the number of rows in the data flow, alter the transaction boundary, or change the row type. The transformations listed in option D—Router, Update Strategy, and Joiner—are all unequivocally classified as active. The Router can filter or redirect rows, the Update Strategy flags rows for different database operations (insert, update, delete), and the Joiner can change the row count based on the join condition and type. Therefore, this is the only selection where all transformations are always active.

A. This option includes the Expression transformation, which is passive. It operates on each row individually without changing the total number of rows.

B. This option also includes the Expression transformation, a passive transformation that cannot alter the row count passing through it.

C. This option includes the Stored Procedure transformation, which can be configured as either active or passive, so it is not guaranteed to be active.

1. Informatica PowerCenter 10.5 - Transformation Guide

Chapter 1: Transformations

"Active and Passive Transformations"

Page 10.

This section explicitly defines active and passive transformations. It lists Router

Update Strategy

and Joiner as active transformations because they can change the number of rows. It lists Expression as a passive transformation. It also notes that a connected Stored Procedure can be active or passive.

2. Informatica PowerCenter 10.2.0 - Transformation Guide

"Preface"

Informatica Conventions

Page 6.

This section provides a clear list of transformations categorized as Active or Passive. Router

Update Strategy

and Joiner are listed under "Active transformations". Expression is listed under "Passive transformations". This confirms that options A and B are incorrect.

3. University of Washington

CSE 444: Database Systems Internals

Lecture 19: "Data Warehousing and ETL".

Course materials on ETL processes often describe Informatica transformations. Slides from such courses typically classify transformations

noting that components like Filters

Aggregators

and Routers are 'active' as they modify the number of tuples (rows) in the data stream

whereas transformations like Expression are 'passive'. This academic context supports the official vendor classification.

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.

The statement accurately describes the behavior of a Pipeline Normalizer transformation when handling repeating columns. The Normalizer transformation is specifically designed to process denormalized data where a column or group of columns occurs multiple times in a single source row. When you configure a column in the Normalizer tab and set its Occurs property to a value greater than 1 (for example, 4), the PowerCenter Designer automatically generates a distinct input port for each occurrence. Therefore, an Occurs value of 4 will result in four separate input ports for that column. The phrase "one input port for each occurs value" refers to creating a port for each instance of the occurrence (the 1st, 2nd, 3rd, and so on). The condition "which Is greater than 1" correctly identifies that this multi-port generation logic applies only to repeating columns, as a column with an Occurs value of 1 is treated as a standard, non-repeating field.

The statement is not False. A 'False' answer would imply that the Normalizer transformation does not create multiple input ports based on the Occurs property for repeating columns. This is incorrect. The core function of the Normalizer is to pivot these multiple occurrences from a single row into multiple, normalized output rows, and it achieves this by first accepting each occurrence through a dedicated input port. While the phrasing of the question could be more precise (e.g., "one input port for each occurrence"), it correctly captures the fundamental mechanism of the transformation.

1. Informatica PowerCenter 10.5

Transformation Guide

Chapter 15: Normalizer Transformation

"Configuring a Normalizer Transformation

" p. 318.

The guide states: "When you create a column on the Normalizer tab

you enter the number of times it occurs in the source row in the Occurs field. For example

if a source column occurs four times in a record

you enter 4. The Designer then creates four input ports for the column." This directly confirms that the number of input ports created is equal to the value of the Occurs property.

2. Informatica PowerCenter 10.5

Transformation Guide

Chapter 15: Normalizer Transformation

"Normalizer Transformation Overview

" p. 315.

This section explains the purpose of the Normalizer: "The Normalizer transformation is an active transformation that you can use to transform one incoming row that contains repeating columns into multiple output rows." This process begins by reading the repeating columns from the source pipeline

which requires a separate input port for each instance of the repeating data.