Question 166 of 506
Ethical Considerations of AImediumMultiple ChoiceObjective-mapped

Quick Answer

The correct action is to retrain the model because the equal opportunity score is below the bias threshold. Interpreting fairness metrics like equal opportunity requires checking whether the true positive rate for each demographic group meets the established threshold—here, a score of 0.72 falls below the 0.8 minimum, signaling that the model treats one group less favorably in identifying positive outcomes. On the Salesforce AI Associate exam, this concept tests your ability to distinguish between different fairness metrics: demographic parity checks overall selection rates, while equal opportunity focuses on true positive parity. A common trap is assuming all metrics must fail before retraining, but the exam emphasizes that a single metric below threshold is sufficient to require action. Memory tip: think “Equal Opportunity = Equal True Positives,” and if that score dips below 0.8, it’s time to retrain.

AI Associate Ethical Considerations of AI Practice Question

This AI Associate practice question tests your understanding of ethical considerations of 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.

Exhibit

{
  "model_name": "LeadScoring_v2",
  "features": ["lead_source", "company_size", "industry", "email_engagement"],
  "fairness_metrics": {
    "demographic_parity": 0.85,
    "equal_opportunity": 0.72
  },
  "bias_threshold": 0.8,
  "current_performance": {"accuracy": 0.91, "f1_score": 0.88}
}

Refer to the exhibit. A Salesforce admin is reviewing an AI model's fairness report. Which action should the admin take?

Question 1mediummultiple choice
Full question →

Exhibit

{
  "model_name": "LeadScoring_v2",
  "features": ["lead_source", "company_size", "industry", "email_engagement"],
  "fairness_metrics": {
    "demographic_parity": 0.85,
    "equal_opportunity": 0.72
  },
  "bias_threshold": 0.8,
  "current_performance": {"accuracy": 0.91, "f1_score": 0.88}
}

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

Retrain the model because the equal opportunity score is below threshold.

Option B is correct because the equal opportunity score (0.72) is below the bias threshold (0.8), indicating potential unfairness in true positive rates across groups. Option A is wrong because demographic parity is above threshold but equal opportunity is not, so not all metrics exceed threshold. Option C is wrong because removing features may not address the root cause. Option D is wrong because increasing the threshold would mask the problem.

Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Remove the email_engagement feature to improve fairness.

    Why it's wrong here

    Removing features may not fix the imbalance.

  • Retrain the model because the equal opportunity score is below threshold.

    Why this is correct

    The low equal opportunity score indicates bias that needs mitigation.

    Related concept

    CIDR notation defines the prefix length.

  • Increase the bias threshold to 0.9.

    Why it's wrong here

    Raising the threshold would hide the issue.

  • Deploy the model because all metrics exceed the threshold.

    Why it's wrong here

    Equal opportunity is below the threshold.

Common exam traps

Common exam trap: usable hosts are not the same as total addresses

Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.

Detailed technical explanation

How to think about this question

Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.

KKey Concepts to Remember

  • CIDR notation defines the prefix length.
  • Block size helps identify subnet boundaries.
  • Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
  • The required host count determines the smallest suitable subnet.

TExam Day Tips

  • Write the block size before choosing the subnet.
  • Check whether the question asks for hosts, subnets or a specific address range.
  • Do not confuse /24, /25, /26 and /27 host counts.

Key takeaway

Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.

Real-world example

How this comes up in practice

A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.

What to study next

Got this wrong? Here's your next step.

Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI Associate subnetting questions on CIDR, address ranges, and subnet selection.

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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 — CIDR notation defines the prefix length..

What is the correct answer to this question?

The correct answer is: Retrain the model because the equal opportunity score is below threshold. — Option B is correct because the equal opportunity score (0.72) is below the bias threshold (0.8), indicating potential unfairness in true positive rates across groups. Option A is wrong because demographic parity is above threshold but equal opportunity is not, so not all metrics exceed threshold. Option C is wrong because removing features may not address the root cause. Option D is wrong because increasing the threshold would mask the problem.

What should I do if I get this AI Associate question wrong?

Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI Associate subnetting questions on CIDR, address ranges, and subnet selection.

What is the key concept behind this question?

CIDR notation defines the prefix length.

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Last reviewed: Jun 23, 2026

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