AI Associate Data for AI Practice Question
A company is building a text classification model for customer support tickets. They have a dataset of 10,000 tickets. The team decides to use active learning for labeling. Which approach best aligns with active learning principles?
⚠ Common exam trap
Salesforce often tests the distinction between active learning and passive learning (random sampling) or semi-supervised learning, and the trap here is assuming that any automated labeling (like using a pre-trained model) qualifies as active learning, when in fact active learning requires iterative human feedback based on model uncertainty.
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
✓
Train a preliminary model and prioritize labeling tickets with low prediction confidence.
Active learning iteratively selects the most informative unlabeled data points for labeling, typically those with low prediction confidence from a preliminary model. This minimizes labeling effort while maximizing model performance, which is the core principle of active learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Randomly select 2,000 tickets and label them manually.
Why it's wrong here
Random selection doesn't target uncertainty.
- ✓
Train a preliminary model and prioritize labeling tickets with low prediction confidence.
Why this is correct
Active learning focuses on uncertain samples.
- ✗
Use a pre-trained model to label all tickets automatically.
Why it's wrong here
Automated labeling may introduce noise.
- ✗
Have subject matter experts label all 10,000 tickets.
Why it's wrong here
Labeling all is costly and not active learning.
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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.