AI Associate AI Fundamentals Practice Question
A sales team uses an AI model to prioritize leads. The model's predictions are not improving despite adding more data. Which THREE factors could explain this? (Choose three.)
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
✓
The new data is noisy or of low quality
Poor data quality, irrelevant features, and model underfitting can all cause lack of improvement. Adding more data helps only if it's high-quality; more features can help if relevant, but not necessarily. Overfitting would show good training performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The new data is noisy or of low quality
Why this is correct
Garbage in, garbage out; low-quality data hinders improvement.
- ✓
The model is underfitting and lacks capacity
Why this is correct
Underfitting means the model is too simple to capture patterns, so more data won't help unless model complexity increases.
- ✗
The model is already overfitting to the training data
Why it's wrong here
Overfitting would cause good training performance but poor generalization, not a lack of improvement.
- ✓
The features used are not predictive of lead conversion
Why this is correct
Irrelevant features won't help the model learn.
- ✗
The team has not added enough features to the model
Why it's wrong here
More features might help, but it's not guaranteed; the question is about lack of improvement, not necessarily needing more features.
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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.