The answer is to train a preliminary model and prioritize labeling tickets with low prediction confidence. This approach aligns with active learning for labeling data because the core principle is to iteratively select the most informative unlabeled examples—those the model is most uncertain about—so that each labeled instance provides maximum improvement to the model’s performance. On the Salesforce AI Associate exam, this question tests your understanding of how active learning reduces labeling costs by focusing human effort on ambiguous cases rather than random or high-confidence samples. A common trap is choosing to label tickets with the highest confidence, which wastes resources on data the model already handles well. Remember the memory tip: “Low confidence, high impact”—if the model is unsure, that ticket is worth your time.
AI Associate Data for AI Practice Question
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.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Active learning often uses uncertainty sampling, where the model selects instances with the highest entropy or lowest margin between top predicted classes. In text classification, this can be implemented with a probabilistic model like logistic regression or a neural network with softmax output, and the query strategy can be adjusted for batch active learning to label multiple tickets at once. Real-world scenarios include customer support ticket routing, where labeling all tickets is expensive and active learning can achieve high accuracy with only 20-30% of the data labeled.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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