A00-260 · Question #58
You want to create a job that checks conditions for your data before that data is loaded into a data warehouse. The job should be able to detect error conditions such as missing data or duplicate…
The correct answer is B. Data Validation. Data Validation (B) is the right tool because it's specifically designed to inspect data before loading - checking for conditions like missing values, duplicates, or out-of-range data, and routing records to error logs or rejection tables based on those conditions. Data…
Question
You want to create a job that checks conditions for your data before that data is loaded into a data warehouse. The job should be able to detect error conditions such as missing data or duplicate data and take appropriate actions like registering error conditions in log, etc. Which SAS Data Integration Studio transformation should you use?
Options
- AData Transfer
- BData Validation
- CData Quality
- DData Extract
How the community answered
(44 responses)- A7% (3)
- B86% (38)
- C5% (2)
- D2% (1)
Explanation
Data Validation (B) is the right tool because it's specifically designed to inspect data before loading - checking for conditions like missing values, duplicates, or out-of-range data, and routing records to error logs or rejection tables based on those conditions. Data Transfer (A) simply moves data between sources and targets without any inspection logic. Data Quality (C) focuses on cleansing and standardizing data (e.g., name parsing, address matching) rather than validation rule enforcement. Data Extract (D) is purely for pulling data from a source, with no built-in condition-checking capability.
Memory tip: Think "Validation = Gatekeeper." Just as a bouncer validates IDs before letting people in, the Data Validation transformation checks your data before it enters the warehouse - and keeps a log of who got turned away.
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