AI Associate Salesforce Einstein AI Features Practice Test 7 — 15 Questions

Question 1

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 2

A sales manager wants to automatically capture emails and events from a sales rep's Outlook calendar and inbox into Salesforce without manual effort. Which feature should be enabled?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture is the correct feature because it automatically syncs emails and events from Microsoft 365 or Google Workspace into Salesforce without requiring manual logging. It uses a background sync engine to capture activities from connected calendars and inboxes, eliminating the need for users to manually log interactions.

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Einstein Bots

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Einstein Lead Scoring

Answer

Einstein Discovery

Question 3

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. Option D is correct because 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)

Answer

The admin selected too few features, causing underfitting

Answer

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

Question 4

A service manager wants to automatically categorize incoming cases by Type, Priority, and Reason based on the case description. Which Einstein feature should be used?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses natural language processing (NLP) to automatically analyze the text of a case description and predict values for standard fields like Type, Priority, and Reason. This directly matches the requirement to categorize incoming cases without manual effort.

Answer

Einstein Article Recommendations

Answer

Einstein Next Best Action

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Einstein Discovery

Question 5

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 6

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 uses historical conversion data. If the history includes leads that converted despite bounces or the model learns from patterns that don't match current behavior, scores may be inaccurate. But the question describes a scenario where the model is not trained on the correct audience. The best answer is that the administrator did not exclude inappropriate records from the training set.

Answer

Einstein Lead Scoring only works for imported leads

Answer

The lead score field is not added to the page layout

Answer

The model requires at least 2000 converted leads to be accurate

Question 7

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

Answer

Operational prescriptions

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Waterfall chart

Question 8

A company uses Einstein Bots to handle basic customer inquiries. When a customer asks a question that the bot cannot answer, the bot should transfer the conversation to a human agent. Which bot configuration is necessary?

Accepted Answer

Add a handoff node in the bot dialogue flow. Option D is correct because a handoff node is the specific Salesforce Einstein Bot configuration that defines the transfer of a conversation from the bot to a live agent when the bot cannot handle the inquiry. This node is placed in the dialogue flow to trigger a seamless handoff, often using Omni-Channel routing to assign the conversation to an available human agent.

Answer

Enable bot analytics

Answer

Train the NLP model with more utterances

Answer

Define intents and entities for the unknown question

Question 9

A sales operations manager wants to compare the AI-predicted forecast against the sales rep's manual commit for the current quarter. Which Einstein feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the specific feature designed to compare AI-predicted forecasts against sales reps' manual commitments for the current quarter. It provides a side-by-side view of the AI-generated forecast and the human-entered commit, enabling managers to identify discrepancies and adjust strategies accordingly.

Answer

Einstein Opportunity Scoring

Answer

Einstein Lead Scoring

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Einstein Prediction Builder

Question 10

A company wants to recommend products to visitors on their Experience Cloud site based on browsing behavior and past purchases. Which Einstein feature should be used?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it is specifically designed to deliver personalized product recommendations on Experience Cloud sites based on visitor browsing behavior and past purchase history. It uses AI to analyze customer interactions and transaction data to surface the most relevant products, directly matching the use case described.

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Einstein Next Best Action

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Einstein Article Recommendations

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Einstein Lead Scoring

Question 11

A developer wants to build a custom app that classifies images of products into categories (e.g., shoes, bags). The app must run in Salesforce and use Einstein AI. Which approach is MOST appropriate?

Accepted Answer

Use Einstein Vision and Language Platform APIs. Einstein Vision and Language Platform APIs provide the image classification capabilities needed for this custom app. These APIs allow developers to train and deploy custom image classification models that can categorize products like shoes and bags directly within Salesforce.

Answer

Use Einstein Recommendation Builder to classify products

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Use Einstein GPT with a prompt that describes the image

Answer

Use Einstein Copilot to process images

Question 12

A service agent receives an Einstein-generated case summary from Service GPT. The summary contains an error — it mentions a product the customer never purchased. What is the MOST likely cause?

Accepted Answer

The model experienced a hallucination — generating factually incorrect content. Option A is correct because the Einstein-generated case summary incorrectly mentions a product the customer never purchased, which is a classic symptom of a hallucination in large language models. Hallucinations occur when the model generates plausible-sounding but factually incorrect content, often due to its probabilistic nature rather than relying on verified data. In this context, Service GPT may fabricate details if it lacks sufficient grounding in the actual Salesforce data, but the direct cause is the model's tendency to invent information.

Answer

The training data for Service GPT was not representative

Answer

The admin did not enable grounding in Salesforce data

Answer

The case description field was empty

Question 13

A company is implementing Einstein Activity Capture. They want to ensure that emails between the sales rep and the company's legal department are NOT logged. Which TWO actions should the admin take?

Accepted Answer

Add the legal department's email domain (e.g., @company-legal.com) to the excluded addresses list. Option B is correct because Einstein Activity Capture provides an 'Excluded Addresses' list where you can specify email domains (e.g., @company-legal.com) to prevent any emails to or from that domain from being logged. Option C is correct because you can also add individual email addresses to the same excluded list, giving granular control over which specific contacts are excluded from activity logging.

Answer

Configure a validation rule to suppress logging when the recipient contains legal domain

Answer

Remove the sales rep's permission to log activities

Answer

Disable Einstein Activity Capture for the legal team's profiles

Question 14

A Salesforce admin is creating an Agentforce agent in Agent Builder to handle order status inquiries. The agent needs to look up order data from a custom object 'Order__c' and respond with the status. Which THREE components must the admin configure in Agent Builder?

Accepted Answer

Prompt instructions that tell the agent how to respond. Topics group related conversations, actions perform data operations, and prompt instructions guide the agent's behavior. Intents are for Einstein Bots, not Agent Builder. Flows are used within actions, but the action itself is required.

Answer

Intents and entities for natural language understanding

Answer

A Flow to route the conversation to a human agent

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

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Einstein Lead Scoring

Answer

Service GPT's case summary feature