Quick Answer
The answer is to collect diverse training data, as this is the foundational first step to ensure fairness in a Salesforce AI model. Without a dataset that accurately represents all relevant demographic groups, use cases, and edge cases, the model will inevitably learn skewed patterns that encode historical or systemic bias, leading to discriminatory outcomes. This concept is central to the Salesforce AI Associate exam, which tests your understanding that fairness cannot be retroactively fixed through post-processing or monitoring—it must be built in at the data collection stage. A common trap on the exam is choosing “run bias detection tests” or “adjust model weights” as the first step, but those are corrective actions that come only after diverse data is secured. Remember the memory tip: “Garbage in, garbage out—fairness starts with the data you let in.”
This AI Associate practice question tests your understanding of ethical considerations of 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.
A developer creates a custom AI model using Salesforce's AI platform. They want to ensure the model is fair. What should they do first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Use a pre-trained model
Why wrong: Pre-trained models may still have biases from their original data.
Test on a small sample
Why wrong: Testing is important but should come after data collection.
Collect diverse training data
Correct. Diverse data helps prevent systemic bias.
Deploy and monitor
Why wrong: Deployment should follow proper development and testing.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Collect diverse training data
Collecting diverse training data is the foundational step to ensure fairness in an AI model because it helps mitigate bias at the source. Without diverse data representing all relevant groups, the model may learn skewed patterns that lead to discriminatory outcomes, regardless of subsequent testing or monitoring.
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 a pre-trained model
Why it's wrong here
Pre-trained models may still have biases from their original data.
Test on a small sample
Why it's wrong here
Testing is important but should come after data collection.
Collect diverse training data
Why this is correct
Correct. Diverse data helps prevent systemic bias.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Deploy and monitor
Why it's wrong here
Deployment should follow proper development and testing.
Common exam traps
Salesforce often tests the misconception that fairness can be achieved through post-hoc actions like testing or monitoring, rather than through proactive data collection, leading candidates to choose 'Test on a small sample' or 'Deploy and monitor' as the first step.
Detailed technical explanation
Under the hood, fairness in AI models is often measured using metrics like demographic parity or equalized odds, which require a representative dataset to compute accurately. For example, in Salesforce's Einstein platform, the training data's feature distributions directly impact the model's predictions; if the data underrepresents a protected class (e.g., by gender or race), the model may produce biased predictions even with advanced fairness-aware algorithms. A real-world scenario is a hiring model trained only on resumes from a single geographic region, which would fail to generalize fairly to candidates from other regions.
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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What to study next
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
The correct answer is: Collect diverse training data — Collecting diverse training data is the foundational step to ensure fairness in an AI model because it helps mitigate bias at the source. Without diverse data representing all relevant groups, the model may learn skewed patterns that lead to discriminatory outcomes, regardless of subsequent testing or monitoring.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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