- A
Increase the dataset size
Why wrong: More data may help but does not directly reduce noise from irrelevant fields.
- B
Use a different algorithm by default
Why wrong: Prediction Builder automatically chooses the algorithm based on data.
- C
Select relevant features (input fields) and exclude irrelevant ones
Feature selection improves model accuracy by removing noisy fields.
- D
Change the prediction field to a different binary field
Why wrong: The prediction field is the target; changing it changes the problem.
Question 834 of 1,000
AI Associate Salesforce Einstein AI Features Practice Question
This AI Associate practice question tests your understanding of salesforce einstein ai features. 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.
An admin is using Einstein Prediction Builder to predict whether a case will escalate. They have selected the prediction field (binary) and the dataset. After training, they notice the model uses all available fields. What should they do to improve model performance and reduce noise?
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
✓
Select relevant features (input fields) and exclude irrelevant ones
Option C is correct because Einstein Prediction Builder automatically includes all available fields by default during training, which can introduce noise and reduce model accuracy. By manually selecting only relevant features (input fields) and excluding irrelevant ones, the admin reduces dimensionality, minimizes overfitting, and improves the model's predictive performance. This feature selection step is a standard best practice in machine learning to ensure the model focuses on meaningful predictors.
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.
- ✗
Increase the dataset size
Why it's wrong here
More data may help but does not directly reduce noise from irrelevant fields.
- ✗
Use a different algorithm by default
Why it's wrong here
Prediction Builder automatically chooses the algorithm based on data.
- ✓
Select relevant features (input fields) and exclude irrelevant ones
Why this is correct
Feature selection improves model accuracy by removing noisy fields.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the prediction field to a different binary field
Why it's wrong here
The prediction field is the target; changing it changes the problem.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume Einstein Prediction Builder automatically handles feature selection or that increasing data always improves performance, when in fact the tool requires manual feature selection to reduce noise and avoid overfitting.
Detailed technical explanation
How to think about this question
Einstein Prediction Builder uses automated feature engineering and a gradient-boosted decision tree (GBDT) algorithm, which can handle mixed data types but is sensitive to irrelevant features that increase the search space and risk overfitting. Under the hood, the model assigns feature importance scores; excluding low-importance fields reduces training time and improves generalization. In a real-world scenario, a case escalation model might include fields like 'Case Origin' and 'Priority' but exclude 'Case Number' or 'Created Date' to avoid spurious correlations.
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.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Salesforce Einstein AI Features — This question tests Salesforce Einstein AI Features — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Select relevant features (input fields) and exclude irrelevant ones — Option C is correct because Einstein Prediction Builder automatically includes all available fields by default during training, which can introduce noise and reduce model accuracy. By manually selecting only relevant features (input fields) and excluding irrelevant ones, the admin reduces dimensionality, minimizes overfitting, and improves the model's predictive performance. This feature selection step is a standard best practice in machine learning to ensure the model focuses on meaningful predictors.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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