AI Associate Data for AI Practice Question
An organization uses Salesforce Data Cloud to unify customer data from multiple sources. They want to ensure that data lineage is tracked for AI models. Which practice supports data lineage?
⚠ Common exam trap
Salesforce often tests the distinction between data management practices that improve performance or security versus those that specifically support auditability and traceability, leading candidates to confuse data partitioning or access control with lineage tracking.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Maintain metadata that records source, transformations, and dependencies.
Maintaining metadata that records source, transformations, and dependencies is the correct practice because data lineage for AI models requires a complete audit trail of where data originated, how it was transformed, and its dependencies. In Salesforce Data Cloud, this metadata is captured through the Data Catalog and Data Lineage feature, which tracks the flow of data from source objects through calculated insights and segments to AI model inputs, ensuring transparency and reproducibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use data partitioning to improve query performance.
Why it's wrong here
Partitioning affects performance, not lineage.
- ✗
Implement role-based access control on datasets.
Why it's wrong here
Access control doesn't track lineage.
- ✓
Maintain metadata that records source, transformations, and dependencies.
Why this is correct
Metadata enables lineage tracking.
- ✗
Regularly run data profiling to check completeness.
Why it's wrong here
Profiling checks quality, not lineage.
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