AI Associate AI Fundamentals Practice Question
A CRM administrator is planning to implement predictive AI for lead scoring. Which TWO actions should be taken to ensure data quality?
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
✓
Ensure lead source values are standardized (e.g., 'Web' not 'website' or 'web form')
Removing outdated or duplicate records and ensuring consistent formatting across fields are key data quality steps. Standardizing lead source values also ensures consistent labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure lead source values are standardized (e.g., 'Web' not 'website' or 'web form')
Why this is correct
Consistent categorical values prevent the model from learning spurious patterns.
- ✓
Remove duplicate leads and leads older than 5 years with no activity
Why this is correct
Cleaning stale and duplicate data improves model accuracy.
- ✗
Use as many fields as possible to increase model complexity
Why it's wrong here
Adding irrelevant fields can introduce noise and cause overfitting.
- ✗
Manually label all historical leads again to remove bias
Why it's wrong here
Relabeling all leads is impractical; focus on cleaning existing labels.
- ✗
Use a smaller dataset to train faster
Why it's wrong here
Smaller datasets may not represent the population, leading to underfitting or bias.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.