Question 212 of 506

Data for AI →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is excluding rows with missing Stage, as this data preparation step introduces selection bias by discarding records where a critical picklist field is absent. In Salesforce AI, the Stage field often correlates with the outcome variable, so removing these rows systematically skews the training data, leading to a model that fails to represent real-world patterns. This question tests your understanding of how data preparation bias emerges during preprocessing, a key concept on the Salesforce AI Associate exam where you must distinguish between safe imputation methods—like filling with median or default values—and risky exclusion. A common trap is assuming all missing data handling is equal, but the exam emphasizes that dropping rows tied to outcome-related fields (like Stage) is far more dangerous than ignoring irrelevant fields (like Description). Memory tip: think of “Stage exclusion” as “stage fright”—if you cut out rows with missing Stage, your model gets scared of incomplete but valid scenarios, biasing its predictions.

AI Associate Data for AI Practice Question

This AI Associate practice question tests your understanding of data for ai. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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

{
  "dataset": "OpportunityPrediction",
  "fields": [
    {"name": "Amount", "type": "currency", "missing": "fill_with_median"},
    {"name": "Stage", "type": "picklist", "missing": "exclude"},
    {"name": "CreatedDate", "type": "date", "missing": "use_default"},
    {"name": "Description", "type": "textarea", "missing": "ignore"}
  ],
  "model": "EinsteinDiscovery",
  "target": "Won"
}

Refer to the exhibit. In the JSON configuration above, which data preparation step could introduce bias?

Question 1mediummultiple choice

Full question →

Exhibit

{
  "dataset": "OpportunityPrediction",
  "fields": [
    {"name": "Amount", "type": "currency", "missing": "fill_with_median"},
    {"name": "Stage", "type": "picklist", "missing": "exclude"},
    {"name": "CreatedDate", "type": "date", "missing": "use_default"},
    {"name": "Description", "type": "textarea", "missing": "ignore"}
  ],
  "model": "EinsteinDiscovery",
  "target": "Won"
}

A
Excluding rows with missing Stage
Excluding rows can systematically remove cases if missing is not random, especially if Stage is related to the target.
B
Ignoring missing Description
Why wrong: Ignoring a text field is acceptable; models often handle missing text as a separate category.
C
Filling missing Amount with median
Why wrong: Median imputation is standard and less likely to introduce bias than exclusion.
D
Using default for missing CreatedDate
Why wrong: Using a default (e.g., current date) may add noise but is less likely to introduce selection bias.

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

✓

Excluding rows with missing Stage

Option B is correct because excluding rows with missing Stage (a picklist that may correlate with outcome) can introduce selection bias. Filling with median (A) or default (C) are common imputation methods; ignoring Description (D) is generally safe as it treats missing as information.

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.

✓
Excluding rows with missing Stage
Why this is correct
Excluding rows can systematically remove cases if missing is not random, especially if Stage is related to the target.
Related concept
Read the scenario before looking for a memorised answer.
✗
Ignoring missing Description
Why it's wrong here
Ignoring a text field is acceptable; models often handle missing text as a separate category.
✗
Filling missing Amount with median
Why it's wrong here
Median imputation is standard and less likely to introduce bias than exclusion.
✗
Using default for missing CreatedDate
Why it's wrong here
Using a default (e.g., current date) may add noise but is less likely to introduce selection bias.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this AI Associate question test?

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: Excluding rows with missing Stage — Option B is correct because excluding rows with missing Stage (a picklist that may correlate with outcome) can introduce selection bias. Filling with median (A) or default (C) are common imputation methods; ignoring Description (D) is generally safe as it treats missing as information.

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

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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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.