Quick Answer
The correct practice is maintaining metadata that records source, transformations, and dependencies. This is essential because data lineage tracking for AI models requires a complete audit trail that captures every step data takes from its origin through transformations and into model inputs, ensuring transparency and reproducibility. In Salesforce Data Cloud, this metadata is automatically captured through the Data Catalog and Data Lineage feature, which tracks the flow from source objects through calculated insights and segments to AI model inputs. On the Salesforce AI Associate exam, this question tests your understanding that lineage is about documenting the data journey, not just storing raw data or running queries. A common trap is confusing data lineage with data quality or data storage practices—remember that lineage is about the “family tree” of your data. Memory tip: think “Source, Transform, Depend” as the three pillars of lineage metadata.
This AI Associate practice question tests your understanding of data for ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Use data partitioning to improve query performance.
Why wrong: Partitioning affects performance, not lineage.
Implement role-based access control on datasets.
Why wrong: Access control doesn't track lineage.
Maintain metadata that records source, transformations, and dependencies.
Metadata enables lineage tracking.
Regularly run data profiling to check completeness.
Why wrong: Profiling checks quality, not lineage.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Regularly run data profiling to check completeness.
Why it's wrong here
Profiling checks quality, not lineage.
Common exam traps
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.
Detailed technical explanation
Data lineage in Salesforce Data Cloud is implemented via the Data Lineage Graph, which uses metadata from the Data Catalog to map relationships between data sources (e.g., connectors, data streams), calculated insights (e.g., SQL transformations), and AI model features. Under the hood, lineage is stored as directed acyclic graphs (DAGs) that record each transformation step, including field-level mappings and aggregation logic, enabling impact analysis when source schemas change. In real-world scenarios, this is critical for regulatory compliance (e.g., GDPR right to explanation) and debugging model drift by tracing back to upstream data changes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.
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