AI Associate Salesforce Einstein AI Features — Timed 10 Questions

Question 1

A company wants to use Einstein Conversation Insights to analyze call recordings. Which of the following metrics is NOT provided by this feature?

Accepted Answer

Next step capture. Einstein Conversation Insights provides talk time metrics, keyword tracking, and sentiment analysis. However, it does not provide next step capture, which is a separate Einstein feature. Therefore, the metric NOT provided is 'Next step capture'.

Answer

Keyword tracking

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Talk time metrics

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Sentiment analysis

Question 2

A sales operations manager wants to automatically prioritize leads based on their likelihood to convert. The team uses Sales Cloud and wants to avoid custom development. Which feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it is specifically designed to automatically prioritize leads based on their likelihood to convert, using historical data and predictive models. It is a native Salesforce Sales Cloud feature that requires no custom development, directly addressing the manager's need to rank leads by conversion probability.

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Einstein Opportunity Scoring

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Einstein Prediction Builder

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Einstein Next Best Action

Question 3

Which feature allows administrators to create and manage prompt templates for Einstein GPT features, such as Field Generation and Sales Email templates?

Accepted Answer

Prompt Builder. Prompt Builder is the dedicated Salesforce tool for creating and managing prompt templates that are used by Einstein GPT features like Field Generation and Sales Email templates. It allows administrators to define the structure, context, and variables for prompts that guide generative AI outputs within the Salesforce platform.

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Einstein Copilot

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Agent Builder

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Einstein Studio

Question 4

A company wants to predict which leads are most likely to convert. They have historical lead data with a 'Converted' field (True/False). Which Einstein feature should they use to build a custom prediction model from this data?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct choice because it allows users to build custom prediction models using their own historical data, including a binary outcome field like 'Converted'. It is designed for non-data scientists to create models without code, directly from standard or custom objects.

Answer

Einstein Lead Scoring

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Einstein Discovery

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Einstein Opportunity Scoring

Question 5

A service team uses Einstein Case Classification to auto-classify incoming cases. They notice that most cases are being classified as 'Low' priority regardless of the actual urgency. What is the most likely cause?

Accepted Answer

The historical case data used for training has a severe class imbalance with very few high-priority cases.. D is correct because a severe class imbalance in the historical training data—where high-priority cases are rare—causes the Einstein Case Classification model to bias predictions toward the majority class ('Low'). The model learns that most cases in the training set are low priority, so it classifies new cases as 'Low' even when the actual urgency is higher, as the algorithm optimizes for overall accuracy rather than per-class performance.

Answer

The model is overfitting to the training data.

Answer

The model has not been retrained in over 90 days.

Answer

The field mapping for priority is incorrect in the classification configuration.

Question 6

A sales manager wants to use Einstein to improve opportunity win rates. They want to understand which factors influence deal closures and receive actionable suggestions. Which TWO Einstein features should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery (B) is correct because it analyzes historical data to identify the key factors that influence deal closures, providing actionable insights and recommendations to improve win rates. Einstein Opportunity Scoring (E) is correct because it predicts the likelihood of each opportunity closing and surfaces the specific factors driving that score, enabling the sales manager to focus on high-impact actions.

Answer

Einstein Prediction Builder

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Einstein Forecasting

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Einstein Lead Scoring

Question 7

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant policy document chunks from a vector store at inference time, without requiring model retraining. Since the documents are updated monthly, RAG enables the team to simply re-index the new documents into the vector store, keeping the system current without modifying the underlying LLM. This avoids the cost and complexity of retraining or fine-tuning a model each month.

Answer

Train a custom model from scratch on the policy documents each month

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Fine-tune a base LLM on the policy documents monthly

Question 8

A Salesforce admin is building an Einstein Prediction Builder model to predict whether a support case will be escalated (binary: Yes/No). The dataset includes cases from the past two years. After selecting the prediction field and features, the admin notices that the model's training score is very high (0.99) but the prediction score field shows very low confidence for new cases. What is the MOST likely cause?

Accepted Answer

The model is overfitting because the number of features is too large relative to the number of training records. A training score of 0.99 combined with low confidence on new cases is a classic symptom of overfitting. In Einstein Prediction Builder, when the model has too many features relative to the number of training records, it memorizes the training data instead of learning generalizable patterns, leading to poor performance on unseen cases.

Answer

The prediction field contains data leakage (e.g., a future value)

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The admin selected too few features, causing underfitting

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The training data is stale and no longer reflects current case patterns

Question 9

A sales manager wants to automatically surface important customer emails that require immediate attention. Which Einstein feature should they use?

Accepted Answer

Einstein Email Insights. Einstein Email Insights is the correct feature because it uses natural language processing (NLP) to analyze email content and automatically surface high-priority emails that require immediate attention. This directly addresses the sales manager's need to identify important customer emails without manual sorting.

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Einstein Activity Capture

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Einstein Lead Scoring

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Einstein Opportunity Scoring

Question 10

An admin creates an Einstein Prediction Builder model to predict whether a lead will convert (binary classification). After training, they notice the prediction score field shows values from 0 to 1000 instead of the expected 0 to 100. What is the most likely cause?

Accepted Answer

The prediction field uses a scale of 0-1000 by default, not a percentage. Einstein Prediction Builder outputs prediction scores on a 0–1000 scale by default, not a percentage. The score represents the relative likelihood of the predicted outcome, where higher values indicate greater confidence. The admin expected a 0–100 scale, but the default range is 0–1000, making option A correct.

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The model includes a numeric custom field as a feature that skews the output

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The prediction field was incorrectly mapped to a currency field

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The model was trained on too few records, causing overfitting