Data Integration & Synchronization Techniques Flashcards
7 cards from real MDM practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Data Integration & Synchronization Techniques flashcards as text
What is the significance of 'match rules' in Informatica MDM, and where are they configured?
Answer: They specify the logic for identifying duplicate records and are configured in the Match Rule Set editor within Schema Manager
Match rules define the criteria (field comparisons, fuzzy logic, weights) used to identify duplicate records and are configured in the Match Rule Set editor in the MDM Hub Console.
In Informatica MDM, what does 'pending merge' status indicate for a record pair?
Answer: The match process identified the records as potential duplicates, but a data steward must review and approve the merge
Pending merge status means the match engine flagged a record pair as probable duplicates within a configurable threshold, requiring human review before the merge is executed.
Which Informatica MDM feature enables an organization to maintain separate 'views' of a golden record for different consuming applications without changing the underlying master data?
Answer: Data Views or Subject Area configurations
Subject Areas and data views allow different subsets or presentations of the golden record to be exposed to different consumers without altering the underlying base object data.
When integrating Informatica MDM with an upstream ERP system, what is the recommended approach to handle large initial data loads (millions of records)?
Answer: Use bulk batch loading via PowerCenter or IICS pipelines into staging tables, followed by batch consolidation jobs
Large initial loads should use bulk ETL tools (PowerCenter or IICS) to populate staging tables efficiently, followed by batch consolidation to process matches and build golden records.
What is the purpose of the 'reject table' generated during the MDM Stage process?
Answer: Capturing records that failed validation or cleansing rules and could not be staged successfully
The reject table stores records that could not pass staging validation or cleansing, allowing integration teams to investigate data quality issues without blocking the main pipeline.
In a hub-and-spoke MDM integration architecture, what is the 'hub' responsible for and what are the 'spokes'?
Answer: The hub is the central repository managing golden records; the spokes are the source and target systems that feed data to or consume data from the hub
In hub-and-spoke MDM, the Hub is the central master data repository, and each spoke is an operational source or consuming system exchanging data with the Hub.
An MDM project team observes that match rates are very low despite obvious duplicates existing in the source data. Which corrective action should be investigated first?
Answer: Review and improve the cleanse functions and match rule configurations, including token generation and match column weights
Low match rates typically indicate that cleanse functions are not standardizing data adequately or that match rules and token configurations are not aligned with the actual data patterns.