AI Associate Practice Test 50 — 15 Questions

Question 1

A retail company wants to display personalized product recommendations on their Experience Cloud site based on customer browsing behavior. Which Einstein feature should they implement?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct choice because it is specifically designed to deliver personalized product recommendations on Experience Cloud sites by analyzing customer browsing behavior, purchase history, and other engagement signals. It uses AI to surface the most relevant products in real time, directly matching the requirement for personalized product recommendations based on browsing behavior.

Answer

Einstein Next Best Action

Answer

Einstein Prediction Builder

Answer

Einstein Article Recommendations

Question 2

Which Einstein feature uses AI to automatically log user emails and events to Salesforce records without manual effort?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct answer because it is the specific Einstein feature that automatically logs user emails and events to Salesforce records without manual effort. It uses AI to analyze email and calendar data from connected systems (like Gmail or Outlook) and automatically associates them with the relevant Salesforce records, eliminating the need for manual logging.

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Answer

Einstein Lead Scoring

Question 3

A sales operations manager wants to use Einstein to automatically prioritize leads based on their likelihood to convert. The team needs a score from 1 to 99 that updates dynamically as new lead data is captured. Which Einstein feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it is specifically designed to automatically assign a dynamic score (1–99) to leads based on their likelihood to convert, updating in real time as new data is captured. This directly matches the requirement for prioritizing leads with a dynamic score, unlike other Einstein features that serve different purposes such as opportunity scoring, custom model building, or data analysis.

Answer

Einstein Opportunity Scoring

Answer

Einstein Prediction Builder

Answer

Einstein Discovery

Question 4

A user wants to automatically log emails and events from their email client to Salesforce without manual entry. Which feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (C) is the correct feature because it automatically logs emails and events from a connected email client (like Outlook or Gmail) into Salesforce without requiring manual entry. It uses a background synchronization process that captures email metadata and calendar events based on configured rules, eliminating the need for manual logging or add-ins.

Answer

Einstein Email Insights

Answer

Einstein Lead Scoring

Answer

Einstein Conversation Insights

Question 5

A service manager wants to reduce manual case classification effort by automatically setting the Type, Priority, and Reason fields on incoming cases. Which Einstein feature meets this requirement?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses machine learning to automatically predict and set the Type, Priority, and Reason fields on incoming cases based on historical case data and patterns. This directly reduces manual classification effort by assigning these three standard fields without requiring custom models or additional configuration.

Answer

Einstein Prediction Builder

Answer

Einstein Reply Recommendations

Answer

Einstein Article Recommendations

Question 6

A financial institution uses AI for loan approvals. They notice the model is denying loans to women more often. After retraining with balanced data, the disparity persists. What is the next best step?

Accepted Answer

Use adversarial debiasing technique. The next best step is D, use an adversarial debiasing technique, because the disparity persists even after retraining with balanced data, indicating that the bias is embedded in the model's learned representations rather than just the training distribution. Adversarial debiasing explicitly trains the model to make predictions that are invariant to the protected attribute (gender) by pitting a predictor against an adversary that tries to detect the protected attribute from the predictions, directly optimizing for fairness. Increasing regularization (A) or reducing model complexity (C) may reduce overfitting but do not specifically target gender-based disparity, and removing the gender feature (B) is insufficient because proxy variables can still encode gender information and perpetuate bias.

Answer

Increase model regularization

Answer

Remove the gender feature

Answer

Reduce model complexity

Question 7

A Salesforce administrator at a retail company is asked to explain to the sales team how Einstein Lead Scoring determines which leads receive a high score. The team wants to know what the model actually evaluates to produce its predictions. Which statement best describes the basis of Einstein Lead Scoring?

Accepted Answer

It compares each lead's field values against patterns learned from the company's own historically converted and unconverted leads.. Einstein Lead Scoring is a supervised learning feature that examines the org's own historical leads, learns which attributes and behaviors correlate with conversion, and scores new leads by similarity to those past patterns. The score is therefore data-driven and org-specific, not a manually weighted formula, not a shared cross-customer model, and not a simple view-count ranking.

Answer

It scores leads using a pre-trained model that Salesforce trained on aggregated, anonymized data from all customer orgs.

Answer

It applies a static scoring formula that the administrator defines by assigning point values to selected lead fields.

Answer

It ranks leads purely by the number of times each lead has been viewed or opened by sales reps in the previous quarter.

Question 8

A customer service director wants to automatically classify incoming case descriptions into categories like 'Billing', 'Technical', or 'General' to route them to the right team. They have a large dataset of historical cases with correct categories. Which Salesforce AI feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is specifically designed to predict case categories from text. It uses supervised learning on historical cases to build a model that automatically assigns categories to new cases, enabling efficient routing. The other features address different needs: sentiment analysis gauges emotion, Next Best Action recommends actions, and Bots handle conversations, none of which provide automatic case categorization.

Answer

Einstein Bots

Answer

Einstein Sentiment Analysis

Answer

Einstein Next Best Action

Question 9

A service manager wants Einstein Case Classification to automatically assign a priority to each new case based on its subject and description. The org currently uses a single case record type. Which action must the manager take first to enable the predictive model?

Accepted Answer

Enable Einstein Case Classification and select the Priority field as the target field for predictions.. Einstein Case Classification is activated and configured by selecting a supported target field, such as Priority, for the model to predict. Once enabled, it trains on historical cases and then scores new cases automatically. Creating custom fields, building external API flows, or importing static predictions do not activate the native predictive model.

Answer

Build a Flow that invokes an external Einstein Language API endpoint to classify each case.

Answer

Create a custom field to store the predicted priority and add it to the case page layout.

Answer

Import a CSV file containing predicted priorities for all existing cases.

Question 10

A sales manager at a manufacturing company wants to forecast next quarter's revenue more accurately. They have two years of historical opportunity data in Salesforce, including amounts, close dates, and stages. Which AI capability in Salesforce should they use to generate a predictive forecast?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the only Salesforce AI feature designed to predict future revenue based on historical opportunity data. It uses machine learning to analyze patterns and produce a forecast, which is exactly what the sales manager needs. The other options serve different purposes, such as scoring leads or opportunities individually, or capturing activities, and do not provide aggregate revenue predictions.

Answer

Einstein Activity Capture

Answer

Einstein Lead Scoring

Answer

Einstein Opportunity Scoring

Question 11

A sales operations analyst needs to give account executives a prioritized list of accounts to focus on this quarter based on historical patterns. Which Salesforce feature should the analyst enable to surface these predictive account insights directly on the account record?

Accepted Answer

Einstein Account Scoring. Einstein Account Scoring uses historical data and engagement signals to assign a predictive score to each account, helping sales teams prioritize their efforts. The score is visible on the account record, so account executives can quickly identify which accounts are most likely to convert or renew. This directly matches the analyst's requirement for a prioritized list of accounts.

Answer

Einstein Opportunity Scoring

Answer

Einstein Case Classification

Answer

Einstein Lead Scoring

Question 12

A sales operations manager at a retail company wants to ensure that their Salesforce org's AI features are set up correctly. They ask the admin to explain the difference between predictive AI and generative AI. Which statement accurately describes generative AI?

Accepted Answer

It creates new content such as text, images, or code based on patterns learned from training data.. Generative AI is defined by its ability to produce new, original content—such as text, images, or code—by learning patterns from training data. Unlike predictive AI, which classifies or forecasts based on historical data, generative AI creates outputs that did not previously exist. In Salesforce, this technology underpins features like Einstein Copilot, which can draft emails, summarize cases, and generate code, making it distinct from predictive scoring tools.

Answer

It classifies data into predefined categories based on historical labeled examples.

Answer

It forecasts numerical values such as sales revenue for the next quarter using regression analysis.

Answer

It retrieves and displays historical data in dashboards without altering the underlying records.

Question 13

A retail company is implementing Einstein Recommendations on its Salesforce Commerce Cloud storefront. The team wants to ensure the recommendations are relevant and drive conversions. Which two data sources are most critical for powering personalized product recommendations? (Choose two.)

Accepted Answer

Customer browsing behavior on the website. Historical purchase data and browsing behavior are the most critical inputs for personalized product recommendations. Purchase history reveals long-term preferences, while browsing behavior captures immediate intent. Together, they enable the recommendation engine to suggest products that are both relevant and timely. Other data like demographics or sentiment may add context but are not as directly influential on individual product suggestions.

Answer

Demographic data such as age and gender

Answer

Inventory levels at each store

Answer

Social media sentiment about the brand

Question 14

A marketing team wants to use AI to predict which customers are most likely to respond to a new email campaign. They have a large dataset in Salesforce and want to build a custom predictive model without writing code. Which Salesforce tool should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery enables users to build custom predictive models using a point-and-click interface, without code. It can analyze historical campaign data to identify patterns and predict which customers are most likely to respond. Since the marketing team needs a tailored model for email campaign response, Einstein Discovery is the correct tool to use within Salesforce.

Answer

Einstein Lead Scoring

Answer

Einstein Bots

Answer

Einstein Activity Capture

Question 15

A customer service manager wants to automatically detect the sentiment of incoming case descriptions and escalate negative cases to senior agents. Which Salesforce Einstein feature should the manager configure to achieve this?

Accepted Answer

Einstein Sentiment Analysis. Einstein Sentiment Analysis examines the text of case descriptions and assigns a sentiment value, enabling automation that can escalate negative cases. This is the only feature listed that directly analyzes the emotional tone of customer text. By configuring escalation rules based on sentiment, the service manager can ensure upset customers receive priority attention from senior agents.

Answer

Einstein Case Classification

Answer

Einstein Next Best Action

Answer

Einstein Article Recommendations