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Last Updated: Jul 25, 2026
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Which of the following rules will retain FIELD1 with the longest last name and TITLE equals to “MR”?
Which of the following is TRUE regarding the use of Critical Fields?
In Standardization Rule Sets, classification table contains what?
Investigation of data using QualityStage is for _____________________
What is DataStage/Quality Stage Administrator client used for?
What does character discrete investigation examine?
What is DataStage/QualityStage Director client used for?
Which one of the following is NOT a survive technique?
Which of the following should be avoided regarding the use of Match Improvement Strategy?
A QualityStage investigate job will NOT ________________
What does the informational content of a QualityStage Match phase measure?
What does the QualityStage Standardization Rule Set contain?
Which one of the following is NOT a necessary step in QualityStage to address data quality issues?
If data value AEJPA 5342G is investigated using an all-C mask, what would the result look like?
Which of the following is NOT true regarding the use of Match Type?
What will a QualityStage match job do?
Which of the following is Weight Override used to add or replace?
Which of the following is NOT a Match Stage comparison method?
Which of the following will be generated by a QualityStage Word Investigate job?
What is Character Concatenate method used for?
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The following are covered in IBM P2090-95 practice exams and IBM P2090-95 practice test:
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| Section | Weight | Objectives |
|---|---|---|
| Survivorship and Job Execution | 15% | - Building and running QualityStage jobs - Outputs and reporting - Survive stage and rules |
| Introduction to InfoSphere QualityStage | 20% | - Purpose and core capabilities - Architecture and components - Data quality concepts and lifecycle |
| Data Matching and Comparison | 25% | - Match stage concepts - Agreement and disagreement weights - Comparison methods and weighting |
| Data Standardization | 20% | - Rule sets and classification - Parsing and formatting data - Standardization concepts and workflow |
| Data Profiling and Investigation | 20% | - Investigation stages and techniques - Tokenization and pattern analysis - Interpreting profiling results |
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