AI Associate Practice Questions 2 — 50 Questions

Question 1

A service manager wants to automatically assign the correct 'Type' and 'Priority' fields on incoming cases. Which feature automatically classifies cases using AI?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses AI to automatically predict and assign the 'Type' and 'Priority' fields on incoming cases based on historical case data and patterns. It leverages machine learning models trained on past cases to classify new cases without requiring manual rules or human intervention, directly meeting the service manager's requirement.

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Einstein Article Recommendations

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Einstein Prediction Builder

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Einstein Bots

Question 2

An organization wants to use Einstein GPT to generate case summaries. However, they need to ensure that the generated text adheres to company style and includes specific required fields. Which tool should they use to customize the prompts?

Accepted Answer

Prompt Builder. Prompt Builder is the correct tool because it allows administrators to create and manage custom prompt templates that enforce company style and required fields when generating content with Einstein GPT. Unlike other options, Prompt Builder is specifically designed to tailor generative AI outputs by defining instructions, variables, and guardrails for use cases like case summaries.

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Einstein Copilot

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Einstein Next Best Action

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Einstein Service GPT

Question 3

A service manager wants to provide agents with real-time suggestions for knowledge articles while they are working on a case. Which Einstein feature should be enabled?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it uses AI to analyze the context of a case (such as subject, description, and product) and surfaces relevant knowledge articles in real-time for agents. This directly matches the requirement of providing suggestions while agents work on a case, improving resolution speed and accuracy.

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Einstein Case Classification

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Einstein Discovery

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Einstein Next Best Action

Question 4

A developer needs to classify images of products into categories using a custom model. They have labeled image data. Which Einstein platform should they use?

Accepted Answer

Einstein Vision and Language Platform. The Einstein Vision and Language Platform is the correct choice because it provides pre-built APIs and custom model training capabilities specifically for image classification tasks. It allows developers to upload labeled image datasets and train a custom model to classify products into categories using deep learning techniques.

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Einstein Prediction Builder

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Einstein Recommendation Builder

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Einstein Discovery

Question 5

A company uses Einstein Lead Scoring but notices that many leads with high scores are not converting. The admin wants to understand which factors are influencing the score. Where can the admin find this information?

Accepted Answer

On the lead record, in the Einstein Lead Score component, by clicking 'Score Factors'. The Einstein Lead Score component on the lead record includes a 'Score Factors' link that, when clicked, displays the top positive and negative factors influencing the lead score. This is the designated UI element for admins to gain transparency into which attributes (e.g., source, industry, behavior) are driving the score, without needing to access setup menus or external support.

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In the Einstein Setup menu under Lead Scoring Models

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By contacting Salesforce support for a model explanation

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By running a report on the Lead object with the Einstein Score field

Question 6

An admin is building a custom AI prediction with Einstein Prediction Builder for a binary classification problem. Which THREE steps are required in the configuration? (Choose 3)

Accepted Answer

Select a prediction field (the field to predict). In Einstein Prediction Builder, the core configuration flow for a binary classification prediction requires you to first choose the prediction field (the field whose value the model will predict), which is exactly what option B states, since the model needs a defined target variable. Option C is also required because you must select the data set — the object and the set of records (with filters) used to train the model — so the algorithm has historical examples to learn from. Option D is required as well: you must select the features (input fields from the object) that the model will use as predictors, since without input variables no model can be trained. Option A is not required because Prediction Builder builds models automatically from selected fields and does not need a custom Apex class for data transformation. Option E is not required because retraining is handled by Einstein's scheduled model refresh, not by an admin-configured Apex trigger running daily.

Answer

Define a custom Apex class for data transformation

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Configure a trigger to retrain the model daily

Question 7

A sales manager wants to see an AI-generated prediction of which opportunities are most likely to close, along with the key factors influencing that prediction. Which feature provides this capability directly in the opportunity record?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is the correct feature because it directly provides an AI-generated prediction of which opportunities are most likely to close, along with the key factors influencing that prediction, all displayed within the opportunity record. This feature uses machine learning models to analyze historical data and assign a score (0–100) to each opportunity, surfacing the top positive and negative influencing factors to help sales reps prioritize their efforts.

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Einstein Forecasting

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Einstein Activity Capture

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Einstein Lead Scoring

Question 8

An AI model predicts loan approvals, and the bank notices that the model disproportionately denies loans to a certain demographic group. Which combination of actions addresses the AI bias according to Salesforce's Trusted AI principles?

Accepted Answer

Audit the model for bias, provide transparency on decision factors, and require human review for denied applications. It aligns with Salesforce's Trusted AI principles, which emphasize accountability, transparency, and human oversight. Auditing the model for bias identifies disparities, providing transparency on decision factors ensures stakeholders understand how outcomes are determined, and requiring human review for denied applications introduces a safeguard against automated discrimination. This combination addresses bias without abandoning AI's benefits.

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Disable the AI model and make all decisions manually

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Remove demographic data from the model entirely and continue using it

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Retrain the model with more data from the affected group and deploy automatically

Question 9

An organization using Einstein Prediction Builder wants to ensure that no customer personally identifiable information (PII) is used in model training. Which data governance practice should they enforce?

Accepted Answer

Data minimisation by selecting only non-PII fields as predictors. The question specifically asks how to ensure no PII is used in model training. Data minimization by selecting only non-PII fields as predictors directly prevents PII from entering the training dataset at the source. This is a proactive governance practice that avoids reliance on post-processing or masking, which may still expose PII during intermediate steps.

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Enabling zero data retention in the Trust Layer

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Data anonymization via the Einstein Trust Layer

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Regularly auditing the model for bias

Question 10

When using Einstein Copilot to generate email content, what mechanism ensures that the AI does not use customer data to improve the underlying large language model?

Accepted Answer

Zero data retention. Einstein Copilot employs a zero data retention policy specifically for the underlying large language model (LLM). This means that any customer data processed during email generation is not stored, logged, or used for model training or fine-tuning, ensuring compliance with data privacy standards. The mechanism explicitly prevents the LLM from learning from or being improved by customer interactions, isolating the AI's behavior from proprietary data.

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PII masking

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Grounding

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Toxicity detection

Question 11

A data scientist is building a model to recommend products. They notice the model rarely recommends certain categories to users from a specific demographic. What should the scientist do first to address potential bias?

Accepted Answer

Audit the training data for representation and bias. The first step in addressing potential bias is to audit the training data for representation and bias. This involves examining whether certain categories or demographics are underrepresented or overrepresented in the dataset, which can lead to skewed model recommendations. Without understanding the data composition, other interventions may fail to address the root cause of bias.

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Increase the model's complexity to capture more patterns

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Add more features about the demographic to improve accuracy

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Remove the demographic feature from the model

Question 12

A service manager wants to automatically classify incoming cases into Type, Priority, and Reason fields based on the case description. Which Einstein feature should they configure?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and populate standard case fields—such as Type, Priority, and Reason—by analyzing the unstructured text in the case description using natural language processing (NLP) and machine learning models. This eliminates manual data entry and ensures consistent categorization across incoming cases.

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Einstein Recommendation Builder

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Einstein Next Best Action

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Einstein Article Recommendations

Question 13

When training an Einstein Discovery model, which data type is not supported as a predictor field?

Accepted Answer

Multi-select picklist. Multi-select picklists are not supported as predictors in Einstein Discovery. Numeric, picklist, and date fields are supported.

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Numeric

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Picklist

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Date

Question 14

A government agency uses an AI system to allocate resources for public services such as healthcare and education. The system is designed to optimize overall efficiency based on historical usage data. After deployment, it becomes clear that underserved regions with less historical data receive significantly less funding than well-served regions. The agency's mission is to promote equity. The system's performance metrics show high efficiency, but community leaders protest the unfair distribution. What should the agency do?

Accepted Answer

Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.. The scenario describes an AI system that optimizes efficiency but produces inequitable outcomes for underserved regions, conflicting with the agency's equity mission. The correct response is to redesign the system to incorporate fairness constraints that guarantee minimum resource levels for underserved regions, aligning the AI's objective with the agency's values. This balances efficiency with equity and addresses the root cause of the bias.

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Abandon the AI system and use a manual, rule-based allocation system.

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Collect more historical data from underserved regions before making adjustments.

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Continue using the system as is, since it maximizes efficiency.

Question 15

A company wants to use AI to automatically analyze and classify images uploaded to Salesforce records, such as identifying product defects. Which Einstein feature should they use?

Accepted Answer

Einstein Vision. Einstein Vision is the correct feature because it is specifically designed for image recognition and classification tasks, such as analyzing uploaded images to identify product defects. It uses deep learning models to detect objects, classify images, and extract text from images, making it ideal for visual inspection use cases within Salesforce.

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Einstein Prediction Builder

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Einstein Next Best Action

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Einstein Language

Question 16

A Salesforce admin wants to ensure that customer data used by Einstein features is not retained by Salesforce to train foundation models. Which component of the Einstein Trust Layer enforces this commitment?

Accepted Answer

Zero Data Retention. Zero Data Retention is the Einstein Trust Layer component that contractually and technically ensures customer data sent to the LLM is not stored or used to train Salesforce's foundation models. It is a core commitment of the Trust Layer and directly addresses the admin's requirement. Other components like audit trail and PII masking serve different purposes.

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Audit trail

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Toxicity detection

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PII masking

Question 17

A company uses Einstein Prediction Builder to predict customer churn. The data includes account creation date, number of support cases, and average payment delay. After training, the model shows low confidence scores. What is the most likely cause?

Accepted Answer

The data contains many missing values or outliers for the selected fields.. Low confidence scores in Einstein Prediction Builder often stem from data quality issues such as missing values or outliers. These anomalies distort the model's ability to learn meaningful patterns, leading to uncertain predictions. Clean, complete data is essential for the model to produce high-confidence scores.

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The training dataset includes fewer than 500 records.

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The prediction field is set to a numeric type instead of a picklist.

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The model was trained on data refreshed daily instead of weekly.

Question 18

A company wants to automatically log all sent emails and calendar events from Gmail into Salesforce without manual user action. Which feature should be configured?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture automatically syncs emails and calendar events from Gmail (and Outlook) into Salesforce records without manual user action. It is the native feature designed for automatic activity logging at scale, matching the requirement precisely.

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Einstein Conversation Insights

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Einstein Email Insights

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Salesforce Inbox

Question 19

A sales team wants to use AI to get product recommendations for customers. Which TWO types of machine learning could be used?

Accepted Answer

Unsupervised learning. Supervised learning (E) is correct because product recommendation can be framed as a classification or regression task trained on labeled historical data, such as past customer purchases and ratings, to predict which products a customer is likely to buy. Unsupervised learning (D) is also correct because techniques like clustering and association rules can group customers with similar behavior or find products frequently bought together, enabling recommendations without explicit labels. Generative AI (A) is not the intended answer here since it creates new content rather than being the core ML paradigm for recommendation, and reinforcement learning (B) is typically used for sequential decision-making and reward optimization, not standard product recommendation. Computer vision (C) is unrelated because it processes images and video, not the tabular customer-product interaction data used for recommendations.

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Generative AI

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Reinforcement learning

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Computer vision

Question 20

A sales rep wants to automatically log emails and events from Outlook to Salesforce without manual work. Which Einstein feature should be enabled?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (C) is the correct feature because it automatically syncs emails and events from Microsoft Outlook (or Google Workspace) to Salesforce without requiring manual logging. It uses a background sync engine that captures email headers and calendar events based on configured rules, eliminating the need for users to manually log activities via add-ins or plugins.

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Einstein Conversation Insights

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Einstein Email Insights

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Einstein Lead Scoring