ICC Mappings & Transformations 3

Question 1

What is pushdown optimization in Informatica Cloud Data Integration?

Accepted Answer

A technique that pushes transformation logic to the source database for native processing, reducing data movement

Answer

A feature that compresses data before transmitting it to the target system

Answer

A method that automatically reorders transformations for optimal execution sequence

Answer

A process that detects and removes performance bottlenecks in data pipelines automatically

Question 2

What is a dynamic mapping in Informatica Cloud?

Accepted Answer

A mapping that adapts to source or target schema changes at runtime without requiring redesign

Answer

A mapping that automatically adjusts its execution schedule based on current system load

Answer

A mapping that processes data in real-time streaming mode with sub-second latency

Answer

A mapping that dynamically scales its worker capacity based on data volume

Question 3

In Informatica Cloud, what is the purpose of the Data Masking transformation?

Accepted Answer

To replace sensitive data with realistic but fictitious values for testing or privacy compliance

Answer

To encrypt sensitive data at rest in the target system using AES encryption

Answer

To hide specific columns from appearing in the mapping output schema

Answer

To compress sensitive data fields to reduce storage footprint in the target

Question 4

What is a Union transformation in Informatica Cloud used for?

Accepted Answer

To merge multiple input pipelines with compatible schemas into a single output stream

Answer

To join two data streams based on a matching key condition

Answer

To split one data stream into multiple parallel output pipelines

Answer

To create a set of distinct values by unioning multiple column expressions

Question 5

Which Sorter transformation property in Informatica Cloud specifies where temporary files are stored when data volume exceeds available memory?

Accepted Answer

Work Directory

Answer

Cache Size

Answer

Tracing Level

Answer

Case Sensitive

Question 6

What is the Normalizer transformation in Informatica Cloud designed to handle?

Accepted Answer

To convert denormalized data with repeating column groups into normalized relational rows

Answer

To standardize data formats and values to conform to predefined business rules

Answer

To remove data anomalies and outliers that violate statistical norms

Answer

To ensure all column values adhere to referential integrity constraints

Question 7

In Informatica Cloud mappings, what is the purpose of the Java transformation?

Accepted Answer

To execute custom Java code within a mapping for complex logic not available as built-in transformations

Answer

To convert data from serialized Java objects into a relational column format

Answer

To establish connectivity between Informatica Cloud and Java EE application servers

Answer

To compile and validate Java class definitions as part of the data pipeline