MDM Data Integration & Synchronization Techniques 5 — Questions and Answers
Question 1: What is the significance of 'match rules' in Informatica MDM, and where are they configured?
- They define which source systems are trusted and are configured in the Trust Framework editor
- They specify the logic for identifying duplicate records and are configured in the Match Rule Set editor within Schema Manager (Correct answer)
- They control which records are published to downstream systems and are set in the Publication editor
- They determine the sequence of batch jobs and are defined in the Job Scheduler
Correct 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.
Question 2: In Informatica MDM, what does 'pending merge' status indicate for a record pair?
- The records have been auto-merged but are awaiting publication to downstream systems
- The match process identified the records as potential duplicates, but a data steward must review and approve the merge (Correct answer)
- The records are queued for the next batch consolidation run but have not yet been matched
- The records failed cleansing and are pending re-submission from the source system
Correct 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.
Question 3: 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?
- Hierarchy Manager profiles
- Data Views or Subject Area configurations (Correct answer)
- Base object partitioning
- Staging table aliases
Correct 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.
Question 4: When integrating Informatica MDM with an upstream ERP system, what is the recommended approach to handle large initial data loads (millions of records)?
- Use SIF API calls to insert records one at a time
- Use bulk batch loading via PowerCenter or IICS pipelines into staging tables, followed by batch consolidation jobs (Correct answer)
- Manually import CSV files through the Hub Console's import wizard
- Directly insert records into the base object tables using SQL scripts
Correct 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.
Question 5: What is the purpose of the 'reject table' generated during the MDM Stage process?
- Storing records that were deliberately excluded by survivorship rules
- Capturing records that failed validation or cleansing rules and could not be staged successfully (Correct answer)
- Archiving golden records that were replaced by newer versions
- Holding records in a pending state while waiting for hierarchy assignments
Correct 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.
Question 6: In a hub-and-spoke MDM integration architecture, what is the 'hub' responsible for and what are the 'spokes'?
- 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 (Correct answer)
- The hub is the ETL transformation engine; the spokes are the match and merge microservices
- The hub is the message broker; the spokes are the individual staging tables per source
- The hub is the data steward workflow engine; the spokes are the automated batch consolidation jobs
Correct 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.
Question 7: 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?
- Increase the Hub Server JVM heap size to allow more records in memory during matching
- Review and improve the cleanse functions and match rule configurations, including token generation and match column weights (Correct answer)
- Add more staging tables to distribute the load across multiple batch processes
- Reduce the trust score of all source systems to allow more records to be considered for matching
Correct 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.
MDM Data Integration & Synchronization Techniques 5
What is the significance of 'match rules' in Informatica MDM, and where are they configured?