AI Associate Practice Test 19 — 15 Questions

Question 1

A marketing team wants to recommend products to customers in an Experience Cloud community using AI. Which feature should they implement?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it allows marketers to create and deploy AI-powered product recommendations specifically within Experience Cloud communities, using customer behavior and profile data to personalize the community experience. Unlike other Einstein features, Recommendation Builder is designed for community sites and can be configured without code to recommend products, articles, or custom records.

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Einstein GPT for Sales

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Einstein Article Recommendations

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Einstein Next Best Action

Question 2

A retail company wants to increase average order value by showing personalized product recommendations on their website. They currently use Salesforce Commerce Cloud and have Einstein Recommendations enabled. However, they notice that recommendations are not reflecting recent customer interactions, such as items added to cart but not purchased. What should the administrator do to improve recommendation relevance?

Accepted Answer

Enable the 'Add to Cart' and 'Checkout' events in the Einstein Recommendations data integration to capture real-time data.. Einstein Recommendations relies on data integration events to capture real-time customer behavior. By enabling 'Add to Cart' and 'Checkout' events, the system can ingest recent interactions (e.g., items added to cart but not purchased) and adjust recommendations accordingly, improving relevance without requiring a full model reset.

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Reset the Einstein Recommendations model and retrain from scratch.

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Implement an Einstein Bot to directly ask customers about their preferences.

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Disable Einstein Recommendations for email and ads, leaving only web recommendations.

Question 3

A company is ingesting data from multiple sources into Data Cloud for Einstein. Which THREE data preparation steps should be performed?

Accepted Answer

Normalization. Normalization is correct because Data Cloud requires data from multiple sources to be transformed into a consistent format, such as standardizing date formats, units, or naming conventions, to ensure the data can be unified and analyzed effectively. This step is critical for Einstein AI models to process data without inconsistencies that could skew predictions or insights.

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Encryption

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Data labeling

Question 4

A company wants to offer personalized product recommendations on their Experience Cloud site. 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. It uses AI to analyze user behavior and preferences to suggest relevant products, directly matching the requirement for personalized product recommendations.

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Einstein Prediction Builder

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Einstein Article Recommendations

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Einstein Next Best Action

Question 5

A Salesforce admin wants to deploy an Einstein bot that uses natural language processing. Which practice best ensures ethical use?

Accepted Answer

Provide clear disclaimers that the user is interacting with an AI.. Transparency is a key ethical principle; users should know they are interacting with AI. Option B is wrong because restricting to internal processes does not address ethical use. Option C is wrong because collecting excessive personal data violates privacy. Option D is wrong because autonomous decisions may require human oversight.

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Use the bot only for internal processes.

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Collect as much personal data as possible to improve accuracy.

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Allow the bot to make autonomous decisions without human review.

Question 6

Which TWO actions are essential for ensuring transparency in an AI system? (Choose two.)

Accepted Answer

Log all AI decisions and allow audit. Options B and D are correct because providing explanations and logging decisions are key to transparency. Option A is wrong because hiding the model reduces transparency. Option C is wrong because training on all data may embed biases, and does not directly relate to transparency. Option E is wrong because consent is about privacy, not transparency.

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Hide the model's internal logic to protect intellectual property

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Train the model on the largest dataset available

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Obtain consent from all data subjects

Question 7

A sales rep wants to see which of their opportunities are most likely to close this quarter without reviewing each one manually. Which feature provides a win probability score for opportunities?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring automatically scores opportunities 1-99 based on win likelihood, visible in Lightning views.

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Einstein Lead Scoring

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Einstein Discovery

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Einstein Forecasting

Question 8

A marketing manager uses Einstein recommendations on their website, but customers are receiving suggestions for products they already purchased. What is the most likely cause?

Accepted Answer

The recommendation model is not filtering out previously purchased items.. C is correct because the most likely cause is that the recommendation model is not configured to exclude previously purchased items. Einstein Recommendations uses customer purchase history to personalize suggestions, but if the model's filtering logic does not explicitly remove items the customer has already bought, those items will continue to appear in the recommendations. This is a common oversight in model configuration rather than a data sync or algorithm type issue.

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The product catalog is not updated with purchase history.

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The model is using real-time browsing data that includes past purchases.

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The recommendations are based on collaborative filtering without personalization.

Question 9

A service manager wants to auto-classify incoming cases by Type, Priority, and Reason based on the case description. Which Einstein feature should be configured to achieve this?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and assign values for fields like Type, Priority, and Reason based on the case description. It uses natural language processing (NLP) and machine learning models trained on historical case data to classify incoming cases without manual intervention, directly meeting the service manager's requirement.

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Einstein Next Best Action

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Einstein Article Recommendations

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Einstein Prediction Builder

Question 10

A company uses AI to automatically categorize customer support tickets into 'Billing', 'Technical', or 'General'. The model is trained on thousands of past tickets labeled by agents. What type of AI task is this?

Accepted Answer

Text classification. This is a text classification problem using supervised learning, where each ticket is assigned to a predefined category based on labeled training data.

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Intent detection

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Sentiment analysis

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Named entity recognition

Question 11

A company wants to use Einstein Forecasting to improve sales predictions. Which TWO statements about Einstein Forecasting are correct?

Accepted Answer

It provides AI-enhanced forecast predictions beyond manager rollups. Einstein Forecasting uses machine learning to analyze historical data and generate AI-powered predictions that go beyond simple manager rollups of rep forecasts. This provides a more accurate and data-driven forecast that accounts for patterns and trends a human might miss.

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It is only available for Service Cloud

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It automatically adjusts quotas for reps

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It replaces CRM Analytics for all reporting

Question 12

A company wants to use Einstein Bots to handle customer support queries. Which preparation is most important before deploying the bot?

Accepted Answer

Ensure the knowledge base articles are well-organized and cover common issues.. Einstein Bots rely on a well-organized knowledge base to retrieve accurate answers for customer queries. Without properly structured articles covering common issues, the bot cannot effectively resolve tickets, leading to poor deflection rates and user frustration. This preparation directly impacts the bot's ability to understand and respond to natural language inputs using Salesforce's NLP and article matching.

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Train all agents on how to monitor the bot.

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Set up a new email channel for the bot.

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Create a custom object to store bot conversations.

Question 13

A user asks an Einstein chatbot 'What is my current account balance?' The chatbot has been trained on transactions but is not supposed to reveal account data. Which ethical principle is at risk?

Accepted Answer

Privacy.. Revealing personal financial data violates the principle of privacy.

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Accountability.

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Transparency.

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Fairness.

Question 14

A service manager wants to automatically log emails and events from Gmail into Salesforce without manual user intervention. Which Einstein feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct feature because it automatically logs emails and events from Gmail into Salesforce without requiring manual user intervention. It uses server-side synchronization to capture and associate email and calendar data with relevant Salesforce records, eliminating the need for plugins or manual logging.

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Einstein Email Insights

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Einstein Conversation Insights

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Einstein Bots

Question 15

Which THREE are valid considerations when deploying an Einstein Bot?

Accepted Answer

Defining the intents the bot should handle.. Defining intents is a core requirement for an Einstein Bot because intents represent the specific goals or tasks users want to accomplish, such as checking an order status or resetting a password. The bot uses Natural Language Processing (NLP) to map user utterances to these intents, enabling it to route conversations appropriately. Without clearly defined intents, the bot cannot accurately understand or respond to user requests.

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Limiting the bot to a single communication channel.

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Ensuring the bot can escalate to a human agent only via email.