- A
Increase the number of records in the training dataset
More data generally improves model performance.
- B
Use Einstein Discovery instead
Why wrong: Discovery is for analysis, not prediction building.
- C
Change the prediction field to a different field
Selecting a different binary field might yield better results if the original field has poor signal.
- D
Enable Einstein Case Classification
Why wrong: Unrelated feature for case classification.
- E
Add more relevant features (input fields) to the dataset
More relevant features can improve model accuracy.
Question 95 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.
A healthcare company uses Einstein Prediction Builder to predict patient no-shows. After training a model, they receive a low prediction accuracy. Which THREE actions should they take to improve?
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
✓
Increase the number of records in the training dataset
Increasing the number of records in the training dataset helps the model learn more patterns and reduces overfitting, which directly improves prediction accuracy. Einstein Prediction Builder requires a minimum number of historical records to produce statistically significant results, and more data generally leads to better model performance.
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 number of records in the training dataset
Why this is correct
More data generally improves model performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Einstein Discovery instead
Why it's wrong here
Discovery is for analysis, not prediction building.
- ✓
Change the prediction field to a different field
Why this is correct
Selecting a different binary field might yield better results if the original field has poor signal.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Einstein Case Classification
Why it's wrong here
Unrelated feature for case classification.
- ✓
Add more relevant features (input fields) to the dataset
Why this is correct
More relevant features can improve model accuracy.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Einstein Discovery with Einstein Prediction Builder, thinking they are interchangeable, or assume that enabling unrelated features like Case Classification can fix accuracy issues.
Detailed technical explanation
How to think about this question
Einstein Prediction Builder uses automated machine learning (AutoML) to train models based on the provided dataset. Under the hood, it evaluates feature importance and applies algorithms like gradient boosting or logistic regression. Adding more relevant features (input fields) allows the model to capture more nuanced relationships, while increasing record count helps the model generalize better and avoid bias from small sample sizes.
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: Increase the number of records in the training dataset — Increasing the number of records in the training dataset helps the model learn more patterns and reduces overfitting, which directly improves prediction accuracy. Einstein Prediction Builder requires a minimum number of historical records to produce statistically significant results, and more data generally leads to better model performance.
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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