- A
Increase the number of records in the training dataset
More data generally improves model accuracy.
- B
Use fewer records to avoid overfitting
Why wrong: Fewer records typically underfit, not improve performance.
- C
Remove features that have little correlation with conversion
Irrelevant features add noise and reduce performance.
- D
Add more features, even if they are not related
Why wrong: Unrelated features add noise and can degrade performance.
- E
Ensure the prediction field value (e.g., converted) is well-represented in the data
Balanced or well-represented outcomes are important for binary classification.
Question 814 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 training an Einstein Prediction Builder model for binary classification (lead conversion). The model performance is poor. Which THREE actions should the admin take to improve it?
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
Option A is correct because increasing the number of records in the training dataset provides more examples for the model to learn patterns from, which is critical for binary classification tasks like lead conversion. In Einstein Prediction Builder, a larger dataset helps reduce variance and improves the model's ability to generalize, especially when the initial performance is poor due to insufficient 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.
- ✓
Increase the number of records in the training dataset
Why this is correct
More data generally improves model accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use fewer records to avoid overfitting
Why it's wrong here
Fewer records typically underfit, not improve performance.
- ✓
Remove features that have little correlation with conversion
Why this is correct
Irrelevant features add noise and reduce performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more features, even if they are not related
Why it's wrong here
Unrelated features add noise and can degrade performance.
- ✓
Ensure the prediction field value (e.g., converted) is well-represented in the data
Why this is correct
Balanced or well-represented outcomes are important for binary classification.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that reducing data prevents overfitting, but in Einstein Prediction Builder, overfitting is more commonly caused by too many irrelevant features or insufficient regularization, not by having too many records.
Detailed technical explanation
How to think about this question
Einstein Prediction Builder uses automated machine learning (AutoML) to train models, and it automatically handles feature selection and hyperparameter tuning. However, the quality and quantity of input data are foundational; for binary classification, a minimum of 500 records per class is often recommended, and class imbalance (as hinted in option E) can severely skew probability estimates. In practice, adding irrelevant features can confuse the model's gradient boosting or logistic regression algorithms, leading to longer training times and worse AUC-ROC scores.
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 — Option A is correct because increasing the number of records in the training dataset provides more examples for the model to learn patterns from, which is critical for binary classification tasks like lead conversion. In Einstein Prediction Builder, a larger dataset helps reduce variance and improves the model's ability to generalize, especially when the initial performance is poor due to insufficient 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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