The correct choice is to exclude the 'Id' field because including it in Einstein Prediction Builder causes data leakage and overfitting. The 'Id' field is a unique identifier for each record, so the model can memorize specific rows rather than learning meaningful patterns, which leads to poor performance on new, unseen data. This directly addresses the search intent around data leakage from the Id field in Einstein Prediction Builder. On the Salesforce AI Associate exam, this concept tests your understanding of feature selection and model generalization—a common trap is assuming all fields are useful, but unique identifiers like Id have zero predictive value for the target outcome. A helpful memory tip: if a field is a unique key, it’s a leak—keep it out to keep your model honest.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for 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.
Refer to the exhibit. A data analyst has defined this field mapping for Einstein Prediction Builder. Which data issue would most likely arise from this mapping?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The 'Id' field should be excluded as it can cause data leakage and overfitting
Option C is correct because including the 'Id' field in Einstein Prediction Builder can cause data leakage and overfitting. The 'Id' field is a unique identifier that has no predictive value for the target outcome, but the model could learn to memorize specific records based on it, leading to poor generalization on unseen data.
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 'LeadSource' field should be mapped to 'Text' instead of 'Category' to preserve verbatim values
Why it's wrong here
LeadSource is typically a fixed set of values, so Category is appropriate.
✗
The 'Amount' field should be mapped to 'Category' to discretize the values
Why it's wrong here
Amount is numeric and should be used as Number.
✓
The 'Id' field should be excluded as it can cause data leakage and overfitting
Why this is correct
Unique identifiers act as keys and should not be used as predictors.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The 'CloseDate' field should be mapped to 'Text' to avoid date parsing issues
Why it's wrong here
Date type allows Einstein to extract temporal patterns.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the concept of data leakage by including a seemingly harmless field like 'Id', tricking candidates into thinking all fields should be mapped, when in fact unique identifiers must be excluded to prevent overfitting.
Detailed technical explanation
How to think about this question
In Einstein Prediction Builder, field mappings determine how data is preprocessed for model training. Unique identifiers like 'Id' or 'RecordId' are automatically excluded by best practices, but if manually included, they can cause the model to learn spurious correlations, leading to perfect training accuracy but poor validation performance. This is a classic overfitting scenario where the model memorizes instead of generalizing.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: The 'Id' field should be excluded as it can cause data leakage and overfitting — Option C is correct because including the 'Id' field in Einstein Prediction Builder can cause data leakage and overfitting. The 'Id' field is a unique identifier that has no predictive value for the target outcome, but the model could learn to memorize specific records based on it, leading to poor generalization on unseen data.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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