- A
Manually increase the lead scores for all leads from that postal code
Why wrong: Manual adjustments are not scalable and may introduce new biases. The model should be corrected at the training data level, not by post-hoc score manipulation.
- B
Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential
Auditing identifies bias; correcting the labels (e.g., re-marketing campaign data as neutral) and retraining addresses the root cause without losing the feature's legitimate predictive power.
- C
Remove the postal code field from the model training data entirely
Why wrong: Removing the field may still allow correlated features (e.g., income) to proxy for postal code, and it eliminates potentially useful signal. Better to correct the underlying data bias.
- D
Use the model as-is because Salesforce AI is certified to be fair
Why wrong: No AI model is automatically fair; fairness must be actively evaluated and ensured using domain expertise and auditing tools.
AI Associate Ethical AI and Data Privacy Practice Question
This AI Associate practice question tests your understanding of ethical ai and data privacy. 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 sales operations manager wants to use Einstein Lead Scoring to prioritize leads. They have historical data showing that leads from a certain postal code have a low conversion rate. However, they suspect the low conversion is due to a past marketing campaign that was poorly targeted, not the demographics of that area. What is the BEST way to ensure the AI model does not unfairly penalize leads from that postal code?
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 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
Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential
Bias in historical data can lead to unfair predictions. The best approach is to audit the model for bias and retrain with corrected labels or additional features that capture the true drivers of conversion, rather than simply omitting the feature or adjusting scores manually.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually increase the lead scores for all leads from that postal code
Why it's wrong here
Manual adjustments are not scalable and may introduce new biases. The model should be corrected at the training data level, not by post-hoc score manipulation.
- ✓
Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential
Why this is correct
Auditing identifies bias; correcting the labels (e.g., re-marketing campaign data as neutral) and retraining addresses the root cause without losing the feature's legitimate predictive power.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Remove the postal code field from the model training data entirely
Why it's wrong here
Removing the field may still allow correlated features (e.g., income) to proxy for postal code, and it eliminates potentially useful signal. Better to correct the underlying data bias.
- ✗
Use the model as-is because Salesforce AI is certified to be fair
Why it's wrong here
No AI model is automatically fair; fairness must be actively evaluated and ensured using domain expertise and auditing tools.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related AI Associate OSPF questions on adjacency and route selection.
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Ethical AI and Data Privacy — study guide chapter
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Ethical AI and Data Privacy practice questions
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential — Bias in historical data can lead to unfair predictions. The best approach is to audit the model for bias and retrain with corrected labels or additional features that capture the true drivers of conversion, rather than simply omitting the feature or adjusting scores manually.
What should I do if I get this AI Associate question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related AI Associate OSPF questions on adjacency and route selection.
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?
OSPF neighbours must agree on key parameters.
About these practice questions
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Last reviewed: Jul 4, 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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