Mass Ingestion and Data Synchronization Flashcards
6 cards from real IICS Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Mass Ingestion and Data Synchronization flashcards as text
What is 'Data Synchronization' in IICS, and how does it differ from Data Integration mappings?
Answer: Data Synchronization is a wizard-driven task for simple source-to-target sync without coding; Data Integration uses full mapping design
Data Synchronization tasks use a simplified wizard interface for straightforward data sync scenarios, while Data Integration provides a full mapping canvas for complex transformations.
In IICS Mass Ingestion, what is a 'deployment' and why is it needed?
Answer: The step that activates and persists a Mass Ingestion task configuration, making it ready to run
Deploying a Mass Ingestion task activates its configuration, creates necessary objects at the target, and prepares it for execution.
Which IICS Mass Ingestion option allows you to select specific tables from a source database rather than ingesting all tables?
Answer: Table filters / table selection in the task configuration
The Mass Ingestion task configuration includes a table selection panel where you can include or exclude specific tables for ingestion.
In IICS Mass Ingestion Databases with CDC, what is 'Apply latency'?
Answer: The delay between when a change occurs at the source and when it appears in the target
Apply latency measures how long it takes for changes captured from the source transaction log to be applied to the target, indicating near-real-time lag.
In IICS, what file formats are typically supported for Mass Ingestion Files source inputs?
Answer: Delimited (CSV), fixed-width, JSON, Parquet, and Avro formats
Mass Ingestion Files supports a range of formats including delimited (CSV), fixed-width, JSON, Parquet, and Avro for source file ingestion.
What is the purpose of 'partitioning' in IICS Mass Ingestion for large tables?
Answer: Splitting source data into parallel chunks to improve ingestion throughput
Partitioning the source table into chunks allows Mass Ingestion to process multiple partitions in parallel, significantly improving throughput for large tables.