Question 369 of 506

Ethical Considerations of AI →hardMultiple ChoiceObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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

Refer to the exhibit.

```json
{
  "modelName": "LeadScoring_v2",
  "features": ["LeadSource", "Industry", "CompanySize", "EmailDomain", "NumberOfEmployees"],
  "target": "Converted",
  "trainingData": {
    "source": "Salesforce_Leads_2019-2021",
    "recordCount": 50000,
    "classBalance": {"Converted": 5000, "NotConverted": 45000}
  },
  "evaluationMetrics": {
    "accuracy": 0.92,
    "precision": 0.85,
    "recall": 0.30
  }
}
```

An AI Associate reviews the Lead Scoring model exhibit. What is the primary ethical concern with this model?

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 1hardmultiple choice

Full question →

Exhibit

Refer to the exhibit.

```json
{
  "modelName": "LeadScoring_v2",
  "features": ["LeadSource", "Industry", "CompanySize", "EmailDomain", "NumberOfEmployees"],
  "target": "Converted",
  "trainingData": {
    "source": "Salesforce_Leads_2019-2021",
    "recordCount": 50000,
    "classBalance": {"Converted": 5000, "NotConverted": 45000}
  },
  "evaluationMetrics": {
    "accuracy": 0.92,
    "precision": 0.85,
    "recall": 0.30
  }
}
```

A
The model uses too many features.
Why wrong: Number of features is not an ethical concern by itself.
B
The model has low recall, potentially missing minority class leads.
Low recall can lead to underrepresentation of certain groups.
C
The model is not explainable.
Why wrong: The model type is not specified; interpretability is not directly addressed.
D
The training data is imbalanced.
Why wrong: Imbalance is a technical concern, but the ethical concern is the impact of low recall.

Full breakdown with real-world context →

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

✓

The model has low recall, potentially missing minority class leads.

The primary ethical concern is that the model has low recall, meaning it fails to identify a significant portion of actual positive leads (the minority class). In a lead scoring context, this can result in missed business opportunities and potential bias against certain customer segments, as the model systematically overlooks valuable leads that do not fit the majority pattern.

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.

✗
The model uses too many features.
Why it's wrong here
Number of features is not an ethical concern by itself.
✓
The model has low recall, potentially missing minority class leads.
Why this is correct
Low recall can lead to underrepresentation of certain groups.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model is not explainable.
Why it's wrong here
The model type is not specified; interpretability is not directly addressed.
✗
The training data is imbalanced.
Why it's wrong here
Imbalance is a technical concern, but the ethical concern is the impact of low recall.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Salesforce often tests the distinction between a technical problem (like imbalanced data) and its ethical consequence (like low recall causing unfair outcomes), so candidates mistakenly pick the technical cause (D) instead of the ethical impact (B).

Detailed technical explanation

How to think about this question

Low recall in a lead scoring model means that many actual high-value leads are incorrectly classified as low-value, which can systematically disadvantage certain demographics or behaviors that are underrepresented in the training data. This is a fairness issue because the model's high false negative rate may reflect or amplify existing biases in the historical data, leading to unequal treatment of potential customers. In practice, tuning the decision threshold or using cost-sensitive learning can improve recall, but the ethical evaluation must consider the real-world impact of missed opportunities on specific groups.

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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.

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.

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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 — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The model has low recall, potentially missing minority class leads. — The primary ethical concern is that the model has low recall, meaning it fails to identify a significant portion of actual positive leads (the minority class). In a lead scoring context, this can result in missed business opportunities and potential bias against certain customer segments, as the model systematically overlooks valuable leads that do not fit the majority pattern.

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: "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?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 30, 2026

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