Question 2 of 506
Ethical Considerations of AImediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is that the primary ethical gap is allowing LLM for summarization without requiring human review for high-risk summaries. This is correct because the policy fails to address the critical need for human oversight for high-risk AI outputs, such as medical or legal summaries, where errors could have severe consequences. On the Salesforce AI Associate exam, this question tests your understanding of the ethical principle that high-risk AI use cases demand mandatory human-in-the-loop review, even when the AI’s task seems routine like summarization. A common trap is assuming that fairness audits or content generation permissions cover all risks, but the gap lies specifically in the absence of oversight for sensitive outputs. Remember the memory tip: “Summaries can be risky—always check the high-risk list before skipping the human.”

AI Associate Ethical Considerations of AI Practice Question

This AI Associate practice question tests your understanding of ethical considerations of ai. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.

Exhibit

{
  "EinsteinLlmPolicy": {
    "allowedUseCases": ["summarization", "contentGeneration"],
    "blockedUseCases": ["credit decisions", "employment decisions"],
    "humanReviewRequired": ["clinicalDiagnosis", "legalAdvice"],
    "fairnessAudits": {
      "frequency": "monthly",
      "metrics": ["equalAccuracy", "demographicParity"]
    }
  }
}

Refer to the exhibit. A company has the Einstein LLM policy shown. What is the primary ethical gap in this policy?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

  • Clue: "primary"

    Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

Question 1mediummultiple choice
Full question →

Exhibit

{
  "EinsteinLlmPolicy": {
    "allowedUseCases": ["summarization", "contentGeneration"],
    "blockedUseCases": ["credit decisions", "employment decisions"],
    "humanReviewRequired": ["clinicalDiagnosis", "legalAdvice"],
    "fairnessAudits": {
      "frequency": "monthly",
      "metrics": ["equalAccuracy", "demographicParity"]
    }
  }
}

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

It allows LLM for summarization without requiring human review for high-risk summaries

Option B is correct because the policy allows using LLM for summarization without specifying oversight for sensitive areas like medical summaries. Option A is wrong because fairness audits are covered. Option C is wrong because content generation is allowed. Option D is wrong because human review is required for some use cases.

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.

  • It blocks employment decisions but allows content generation which could be biased

    Why it's wrong here

    Content generation is allowed but with fairness audits.

  • It does not include fairness audits for allowed use cases

    Why it's wrong here

    Fairness audits are included for all use cases.

  • It requires human review for clinical diagnosis but not for legal advice

    Why it's wrong here

    Legal advice does require human review.

  • It allows LLM for summarization without requiring human review for high-risk summaries

    Why this is correct

    Summarization could include sensitive content; missing human review is a gap.

    Clue confirmation

    The clue word "primary" in the question point toward this answer.

    Related concept

    OSPF neighbours must agree on key parameters.

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.

Related practice questions

Related AI Associate practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

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FAQ

Questions learners often ask

What does this AI Associate question test?

Ethical Considerations of AI — This question tests Ethical Considerations of AI — OSPF neighbours must agree on key parameters..

What is the correct answer to this question?

The correct answer is: It allows LLM for summarization without requiring human review for high-risk summaries — Option B is correct because the policy allows using LLM for summarization without specifying oversight for sensitive areas like medical summaries. Option A is wrong because fairness audits are covered. Option C is wrong because content generation is allowed. Option D is wrong because human review is required for some use cases.

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: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

What is the key concept behind this question?

OSPF neighbours must agree on key parameters.

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Last reviewed: Jun 23, 2026

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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.