AI Associate Salesforce Einstein AI Features Practice Test 2 — 15 Questions

Question 1

A company wants to use Einstein Forecasting to compare AI-generated predictions against sales rep commitments. They have enabled the feature and entered their sales data. What must they do to view the AI forecast?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Turn on Einstein AI in the Forecasts settings. After enabling Einstein Forecasting and entering sales data, the user must turn on Einstein AI in the Forecasts settings to activate the AI-generated predictions. This setting allows the system to compare AI forecasts against sales rep commitments within the standard Forecasts component.

Answer

Run Einstein Discovery on opportunity data

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Enable Einstein Activity Capture

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Create a custom report type for forecasts

Question 2

A company needs to analyze thousands of customer feedback comments to identify common themes and sentiment. They want to use a prebuilt Salesforce AI solution. Which approach is best?

Accepted Answer

Use Einstein Discovery to analyze the feedback data and identify themes. Einstein Discovery can perform automated statistical analysis on text data to identify themes and patterns, including sentiment analysis, without requiring custom model training.

Answer

Use Einstein Prediction Builder to predict sentiment

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Use Einstein Vision and Language Platform to build a custom text classification model

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Use Einstein Bots to collect more feedback

Question 3

A sales team wants to use Einstein Opportunity Scoring to improve win rates. Which TWO statements about Einstein Opportunity Scoring are correct? (Choose 2)

Accepted Answer

It provides a score from 1 to 99 indicating the likelihood of winning the opportunity. Option A is correct because Einstein Opportunity Scoring assigns each opportunity a score from 1 to 99 that represents the likelihood that the opportunity will be won, giving the sales team a quantitative way to prioritize deals. Option E is correct because the score, along with the factors and influences that contributed to it, is surfaced directly on the opportunity record page in Lightning Experience, so reps can see why the score is what it is. Options B, C, and D are not correct: the minimum data requirement for Opportunity Scoring is 200 closed-won and 200 closed-lost opportunities (not 500 closed opportunities in 12 months), the score is not exposed through an out-of-the-box REST API endpoint, and Einstein Opportunity Scoring does not automatically change the opportunity stage—it only predicts the likelihood of winning.

Answer

It requires a minimum of 500 closed opportunities in the last 12 months to activate

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The score can be accessed via the REST API out-of-the-box

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It automatically updates the opportunity stage based on the score

Question 4

A support center wants to use Einstein Case Classification to automatically assign categories to incoming cases. They have historical case data with the 'Type' field populated for 70% of cases, 'Priority' for 50%, and 'Reason' for 30%. They want to classify on 'Type' and 'Reason'. What is the best approach to maximize model accuracy?

Accepted Answer

Start by training a model for Type only, since it has more populated records, then train a model for Reason once more data is accumulated.. Einstein Case Classification requires a minimum volume of labeled training records per field to build a reliable model, and Reason at 30% populated likely falls below the threshold for acceptable accuracy. Training Type first (70% populated) delivers immediate value, and deferring Reason until more data accumulates avoids a low-quality model. This staged approach maximizes overall accuracy and ROI.

Answer

Use Einstein Prediction Builder instead, which can handle multi-class classification for both fields.

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Build a single model that predicts both Type and Reason simultaneously to leverage all available data.

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Create two separate models: one for Type using all records with Type populated, and one for Reason using all records with Reason populated.

Question 5

A company wants to automatically generate draft email replies to customer support inquiries using generative AI. Which Einstein GPT feature is designed for this purpose?

Accepted Answer

Service GPT. Service GPT is specifically designed to assist customer service agents by generating draft email replies, summarizing cases, and suggesting responses. It leverages generative AI to automate support interactions.

Answer

Einstein Copilot

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Sales GPT

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Einstein Discovery

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 CRM data to surface the key factors that drive outcomes such as closed-won versus closed-lost opportunities, and it presents actionable recommendations and predictions that the sales manager can act on to improve win rates. Einstein Opportunity Scoring (E) is correct because it applies AI to score open opportunities based on their likelihood to close, giving the manager prioritized, actionable guidance on which deals to focus on. Einstein Prediction Builder (A) is a general-purpose tool for building custom predictions on any object but is not the purpose-built feature for opportunity insights and recommendations in this scenario. Einstein Forecasting (C) projects future revenue from the pipeline rather than explaining deal-closure factors or suggesting actions. Einstein Lead Scoring (D) scores leads rather than opportunities, so it does not address opportunity win rates.

Answer

Einstein Prediction Builder

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Einstein Forecasting

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Einstein Lead Scoring

Question 7

An administrator is setting up Einstein GPT for service agents. They want to enable case summary generation and knowledge article draft creation. Which TWO Einstein GPT features should they configure?

Accepted Answer

Prompt Builder. Option B, Prompt Builder, is correct because it is the Salesforce tool used to create and manage the reusable prompt templates that power generative features such as case summaries and knowledge article drafts, letting the admin define the prompt, inputs, and model to generate the desired output. Option C, Service GPT, is correct because it is the Einstein GPT capability set for service agents that includes case summary generation and knowledge article draft creation, so configuring it enables those exact features. Option A, Einstein Copilot, is not correct here because it is a conversational assistant for taking actions and answering questions, not the specific configuration for case summaries and article drafts. Option D, Sales GPT, is not correct because it targets sales use cases like email drafting and opportunity insights, not service case summaries or knowledge articles. Option E, Einstein Discovery, is not correct because it is a predictive analytics and machine learning tool for surfacing insights and predictions, not a generative text feature for case summaries or article drafts.

Answer

Einstein Copilot

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Sales GPT

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Einstein Discovery

Question 8

A company wants to use generative AI to draft knowledge articles from case resolutions. Which feature should they use?

Accepted Answer

Service GPT. Service GPT is the correct feature because it is specifically designed for service use cases, such as drafting knowledge articles from case resolutions. It leverages generative AI to summarize case details and create structured knowledge base content, directly addressing the company's need.

Answer

Prompt Builder

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Einstein Copilot

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Sales GPT

Question 9

A company is building an Agentforce agent to handle order cancellations. Which THREE components are essential to configure in Agent Builder? (Choose three.)

Accepted Answer

Topics (e.g., 'Cancel Order'). In Agent Builder, Topics (A) are essential because they define the scope of what the agent can handle—such as a 'Cancel Order' topic—and route the user's request to the appropriate instructions and actions. Actions (D) are also required, since the agent must actually perform the work, like invoking a flow or Apex to 'Update Order Status' or process a 'Refund' for the cancellation. Testing in Agent Builder (C) is essential to validate the topic routing, action invocation, and overall conversation behavior before deployment, ensuring the agent responds correctly. Options B and E are not required here: intents and entities for NLP belong to classic Einstein Bots/NLU configuration rather than the Agentforce Agent Builder model, and Prompt Builder templates are used for prompt-based generative steps but are not a mandatory component for configuring an agent's cancellation logic.

Answer

Intents and entities for NLP

Answer

Prompt Builder template

Question 10

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

Answer

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 11

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 12

A company wants to use generative AI to automatically generate personalized email drafts for sales reps to send to leads. Which Einstein GPT feature should be used?

Accepted Answer

Sales GPT. Sales GPT is the correct Einstein GPT feature for generating personalized email drafts for sales reps because it is specifically designed to automate sales communications, including email content tailored to leads. It leverages CRM data and generative AI to create context-aware drafts that align with the sales process, unlike other GPTs that serve different domains like service or general assistance.

Answer

Service GPT

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Prompt Builder

Answer

Einstein Copilot

Question 13

An administrator is configuring Einstein Lead Scoring. After activation, lead scores are visible in the lead record page. However, some leads that are clearly not interested (e.g., bounced email) are scored 90+. What is the MOST likely reason?

Accepted Answer

The administrator did not exclude bounced leads from the training population. Einstein Lead Scoring trains on historical lead data to predict conversion likelihood, and the training population is defined by the leads the admin selects. If bounced or disqualified leads are included in the training set and labeled as converted or high-scoring, the model learns incorrect patterns and assigns high scores to leads that should be low-value. Excluding bounced leads from the training population prevents this contamination.

Answer

Einstein Lead Scoring only works for imported leads

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The lead score field is not added to the page layout

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The model requires at least 2000 converted leads to be accurate

Question 14

A financial services firm is required to explain why a specific customer was denied a loan. They use Einstein Discovery to analyze loan approval data. Which Einstein Discovery output is BEST suited for generating a human-readable explanation of the key factors leading to the decision?

Accepted Answer

Story creation. Story creation in Einstein Discovery is specifically designed to generate natural-language narratives that explain the key factors influencing a prediction or decision. For a loan denial, it would produce a human-readable summary of the top drivers (e.g., 'Credit score was the most important factor, followed by debt-to-income ratio'), making it ideal for regulatory or customer-facing explanations. Other outputs like improvement suggestions or operational prescriptions focus on actions or optimizations, not on explaining a past decision.

Answer

Improvement suggestions

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Operational prescriptions

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Waterfall chart

Question 15

A marketing manager wants to use Einstein GPT to generate follow-up emails after a meeting. Which TWO capabilities of Einstein GPT can be used for this purpose?

Accepted Answer

Einstein Copilot. Einstein Copilot (B) is correct because it is the conversational AI assistant that can generate follow-up emails based on meeting context and user prompts. Sales GPT's meeting follow-up feature (E) is correct because it is specifically designed to auto-generate follow-up emails after a meeting, leveraging CRM data and natural language generation.

Answer

Prompt Builder to create a follow-up template

Answer

Einstein Lead Scoring

Answer

Service GPT's case summary feature