AI Associate Practice Test 48 — 15 Questions

Question 1

A Salesforce admin is configuring Einstein Bots for a customer service channel. They want the bot to automatically send promotional offers to customers identified as high-value. Which combination of settings best ensures ethical AI and compliance with data privacy regulations?

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

Accepted Answer

Enable the Einstein Trust Layer with toxicity detection and PII masking, and require human approval before the bot sends any promotional messages.. The Einstein Trust Layer is Salesforce's built-in architecture for secure, ethical AI, providing data masking (PII), toxicity detection, zero data retention, and audit trails. For promotional messages to high-value customers, combining PII masking with toxicity detection protects sensitive data and content, while mandatory human approval ensures accountability for outbound marketing decisions — a core ethical AI principle. This layered approach satisfies both data privacy regulations (GDPR, CCPA) and Salesforce's own Trusted AI guidelines.

Answer

Disable the Einstein Trust Layer to improve response speed, and rely on the bot's internal moderation.

Answer

Use a custom AI model without the Trust Layer, but log all bot decisions for audit.

Answer

Enable the Einstein Trust Layer with toxicity detection only, and allow the bot to send messages automatically.

Question 2

A data governance officer wants to ensure that customer data used in Einstein Prediction Builder is not retained by Salesforce after the prediction is made. Which Einstein Trust Layer capability guarantees this?

Accepted Answer

Zero data retention. Zero data retention in the Einstein Trust Layer ensures that customer data used for predictions is not stored or retained by Salesforce after the prediction is processed. This capability is critical for compliance with data privacy regulations. It guarantees that data is discarded immediately post-prediction, preventing any persistent storage.

Answer

Grounding

Answer

Audit trail

Answer

PII masking

Question 3

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. 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 4

A company uses an AI model to classify customer support cases into categories (billing, technical, general). The model performs well on training data but poorly on new cases. Which issue is MOST likely occurring?

Accepted Answer

Overfitting. Overfitting means the model memorized the training data and fails to generalize to new, unseen data. Underfitting would show poor performance on both training and test data. Data leakage occurs when future information leaks into training. Bias in data is a different issue.

Answer

Underfitting

Answer

Data leakage

Answer

Bias in training data

Question 5

A company wants to use AI to automatically extract key information (e.g., invoice number, date, total amount) from scanned invoices. Which THREE technologies should be combined?

Accepted Answer

Optical Character Recognition (OCR). The scenario requires converting scanned invoice images into usable data, so Optical Character Recognition (OCR) (B) is essential because it converts the pixel-based text in scanned documents into machine-readable characters. Computer vision (E) is also required because it handles image preprocessing and layout analysis, such as detecting regions, correcting skew, and isolating text blocks before OCR runs. Natural Language Processing (NLP) with entity extraction (D) is the third necessary technology because it identifies and classifies the specific fields requested, such as invoice number, date, and total amount, from the OCR output. Generative AI for text generation (A) is not appropriate here because the goal is extraction, not creating new text, and sentiment analysis (C) is irrelevant since invoices contain no opinion or emotional content to classify.

Answer

Generative AI for text generation

Answer

Sentiment analysis

Question 6

Which Salesforce AI feature provides automated statistical analysis and generates plain-language stories about trends in data?

Accepted Answer

Einstein Discovery. Einstein Discovery is Salesforce's AI-powered analytics tool that automatically performs statistical analysis on datasets and generates natural-language narratives explaining trends, outliers, and predictions. It surfaces 'stories' that describe what is driving a metric, making it the correct answer for automated statistical analysis with plain-language explanations.

Answer

Einstein Forecasting

Answer

Einstein Prediction Builder

Answer

Einstein GPT

Question 7

A company wants to use Einstein Vision for product categorization. To ensure ethical use, they should:

Accepted Answer

Test the model for bias across different demographic groups.. Ethical use of a vision model for product categorization requires proactively checking that the model does not produce systematically different outcomes for different demographic groups. Testing for bias across demographic groups is the standard responsible-AI practice to detect and mitigate disparate impact before deployment. This directly addresses fairness, a core ethical requirement.

Answer

Avoid using any images that contain people.

Answer

Use only high-resolution images.

Answer

Only use images from a single demographic.

Question 8

An admin wants to create a custom AI model that predicts whether a support case will be escalated (Yes/No) based on historical cases. The training data includes fields like Subject, Description, Account Tier, and Product Family. Which Einstein feature should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the Salesforce feature designed to create custom predictive models on any standard or custom object using historical data. It allows admins to define a prediction (e.g., 'Will this case be escalated?') with a Yes/No outcome, select the fields to use as predictors (Subject, Description, Account Tier, Product Family), and train a model without writing code.

Answer

Einstein Next Best Action

Answer

Einstein Case Classification

Answer

Einstein Discovery

Question 9

A sales manager wants to automatically prioritize leads based on their likelihood to convert. The team uses Salesforce Sales Cloud and has historical lead data with conversion outcomes. Which Einstein feature should they use to create a custom prediction model?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the no-code Einstein feature specifically designed to allow admins to create custom binary prediction models (e.g., lead conversion) using their own historical data fields without requiring data science expertise. It automatically selects the most predictive fields and generates a model that outputs a probability score for each lead, which can then be used for prioritization.

Answer

Einstein Discovery

Answer

Einstein GPT

Answer

Einstein Lead Scoring

Question 10

A large enterprise needs to integrate data from Salesforce CRM, an external ERP, and marketing automation to train an AI model for cross-sell recommendations. Which data storage strategy is most aligned with Salesforce's AI capabilities?

Accepted Answer

Use Salesforce Data Cloud to unify the datasets. Salesforce Data Cloud is designed to ingest, harmonize, and unify data from multiple sources — including CRM, external ERP, and marketing automation — into a single customer profile. Training AI models on this unified dataset aligns with Salesforce's AI (Einstein) capabilities, which rely on Data Cloud as the foundation for cross-sell recommendations.

Answer

Use only Salesforce CRM data and ignore external sources

Answer

Store each source separately in Data Cloud and train models on each

Answer

Export all data to an external data lake and build a custom model

Question 11

A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?

Accepted Answer

Lead interaction history with emails and web activity.. Einstein Engagement Scoring is a predictive AI feature in Salesforce Marketing Cloud that analyzes a contact's behavioral engagement signals — specifically email opens, clicks, and web activity — to generate a score predicting how likely that person is to engage with future communications. The model is trained on historical interaction patterns, so the primary input is the lead's own engagement history with emails and web channels. This score is then used to prioritize leads for marketing outreach, not to predict conversion from sales data.

Answer

Historical conversion data from closed opportunities.

Answer

Lead demographic information like industry and company size.

Answer

Social media posts and mentions of the company.

Question 12

A marketing manager wants to understand why a specific lead received a high score from an Einstein model. Which Salesforce feature provides the most detailed explanation?

Accepted Answer

Einstein Discovery. Einstein Discovery is Salesforce's augmented analytics and machine-learning feature that builds predictive models and, critically, surfaces the top predictors driving each individual prediction through its explanation/insight cards. When a lead receives a high score, Einstein Discovery shows which fields (e.g., industry, engagement, company size) contributed most and in which direction. This per-record explainability is exactly what the marketing manager needs.

Answer

Einstein Bots

Answer

Einstein Activity Capture

Answer

Einstein Automate

Question 13

A service manager wants to automatically categorize incoming support cases based on the customer's description. Which Einstein feature should be used?

Accepted Answer

Einstein Case Classification. Einstein Case Classification uses machine learning to automatically categorize incoming support cases based on the customer's description, assigning them to predefined case fields (e.g., type, priority, product). This directly matches the requirement to automatically categorize cases from text, making it the correct choice.

Answer

Einstein Reply Recommendations

Answer

Einstein Bots

Answer

Einstein Article Recommendations

Question 14

You are a data scientist at a retail company. The company uses Einstein Discovery to analyze customer purchase patterns. The model is built on a dataset of 50,000 transactions. The model's R-squared is 0.85, but the predictions for new customers are consistently off by a large margin. The data includes features like 'Customer Age', 'Income', 'Previous Purchases', and 'Product Category'. The model was trained on data from the past two years. However, six months ago, the company launched a new loyalty program that significantly changed purchasing behavior. You suspect the model is not generalizing to new customers. What should you do to validate your hypothesis?

Accepted Answer

Create a holdout set of transactions from the last six months and compare model performance on it vs. older data. Creating a holdout set of transactions from the last six months directly tests whether the model's performance has degraded due to the loyalty program's impact on purchasing behavior. By comparing the R-squared or other metrics on this recent holdout set versus older data, you can quantify the drop in predictive accuracy and confirm that the model fails to generalize to the new data distribution. This approach is a standard method for detecting concept drift in machine learning models, especially when external changes (like a loyalty program) alter the underlying patterns.

Answer

Exclude new customers from the dataset entirely

Answer

Increase the training data size to include older transactions

Answer

Remove the 'Product Category' feature to simplify the model

Question 15

A company wants to implement an autonomous AI agent in Salesforce that can handle customer service cases end-to-end. Which feature should they use?

Accepted Answer

Agentforce. Agentforce is the correct feature because it is specifically designed to create autonomous AI agents that can handle customer service cases end-to-end in Salesforce. Unlike simpler bots or copilots, Agentforce can independently execute multi-step workflows, make decisions, and take actions across Salesforce objects without requiring human intervention for every step.

Answer

Einstein Copilot

Answer

Einstein Next Best Action

Answer

Einstein Bots