AI Associate Practice Exam 4 — 20 Questions

Question 1

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?

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

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.

Answer

Increase the model's complexity to capture more patterns

Answer

Add more features about the demographic to improve accuracy

Answer

Remove the demographic feature from the model

Question 2

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.

Answer

Einstein Recommendation Builder

Answer

Einstein Next Best Action

Answer

Einstein Article Recommendations

Question 3

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.

Answer

Numeric

Answer

Picklist

Answer

Date

Question 4

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.

Answer

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

Answer

Collect more historical data from underserved regions before making adjustments.

Answer

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

Question 5

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.

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

Answer

Einstein Language

Question 6

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.

Answer

Audit trail

Answer

Toxicity detection

Answer

PII masking

Question 7

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.

Answer

The training dataset includes fewer than 500 records.

Answer

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

Answer

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

Question 8

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.

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Answer

Salesforce Inbox

Question 9

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.

Answer

Generative AI

Answer

Reinforcement learning

Answer

Computer vision

Question 10

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.

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Answer

Einstein Lead Scoring

Question 11

A global retail company deploys an AI-powered chatbot for customer service. The chatbot uses natural language processing to understand and respond to customer inquiries. After deployment, the company notices that the chatbot consistently provides less accurate and less helpful responses to customers from non-English-speaking regions, particularly those using dialects or slang. The company's data science team trained the model primarily on English-language customer service transcripts from the US and UK. The AI Ethics team has raised concerns about fairness and potential bias. The company wants to address this issue while maintaining overall performance and minimizing cost. Which action should the company take first?

Accepted Answer

Conduct a fairness audit using diverse test cases from multiple languages and dialects to quantify the disparity.. The first step in addressing potential bias in an AI system is to measure and quantify the disparity. Conducting a fairness audit with diverse test cases from multiple languages and dialects provides the data science team with a clear, empirical baseline of the model's performance gaps. This diagnostic step is essential before any remediation (like retraining or adding constraints) to ensure that subsequent actions are targeted and effective, avoiding wasted resources or unintended consequences.

Answer

Implement a fairness constraint in the model's loss function to penalize disparities across language groups.

Answer

Disable the chatbot for non-English languages and redirect those customers to human agents.

Answer

Collect more training data from all regions and retrain the model from scratch.

Question 12

A sales operations manager wants to use Einstein Forecasting to improve forecast accuracy. Which TWO capabilities does Einstein Forecasting provide beyond traditional manager rollups? (Select two.)

Accepted Answer

Comparison of AI forecast to the rep's commit amount. Option E is correct because Einstein Forecasting's core capability is generating AI-based forecast predictions that analyze historical opportunity data, trends, and patterns to project future revenue, going beyond what a manager's manual rollup can produce. Option D is correct because Einstein Forecasting lets users compare the AI-generated forecast against the rep's own commit or forecast amount, highlighting gaps and improving forecast accuracy through that side-by-side comparison. Option A is incorrect because automated opportunity scoring is a feature of Einstein Opportunity Scoring, not Einstein Forecasting. Option B is incorrect because call script generation is not a forecasting capability; it relates to sales enablement tools. Option C is incorrect because Einstein Forecasting does not automatically adjust quota targets; quota setting remains a separate planning function.

Answer

Automated opportunity scoring for each deal

Answer

Generation of call scripts for sales reps

Answer

Automatic adjustment of quota targets based on AI predictions

Question 13

An admin wants to automatically log emails and events from Outlook or Gmail to Salesforce without manual user action. 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 Outlook or Gmail into Salesforce without requiring manual user action. It uses a background synchronization process that captures activities based on configured rules, eliminating the need for plugins or add-ins.

Answer

Einstein Conversation Insights

Answer

Einstein Bots

Answer

Einstein Email Insights

Question 14

A company wants an AI chatbot that can handle customer inquiries about order status. Which tool should be configured?

Accepted Answer

Einstein Bot. Einstein Bot is the correct tool because it is Salesforce's native AI-powered chatbot designed to handle customer inquiries, including order status, through natural language processing and automated conversations. It can be configured to answer common questions, escalate complex issues, and integrate with backend systems to retrieve real-time order data without human intervention.

Answer

Queues

Answer

Omni-Channel

Answer

Case Assignment Rules

Question 15

A data scientist discovers that an AI model used for loan approval predicts high default risk disproportionately for a specific demographic group. What is the first step to address this issue?

Accepted Answer

Audit the training data for bias. The first step when a model shows disparate impact is to audit the training data for bias — examining whether the data itself underrepresents or mislabels the affected group, or contains proxy variables correlated with the protected attribute. Without understanding the root cause in the data, any algorithmic fix (new model, dropping features, more data) is premature and may not address the actual source of bias.

Answer

Use a different algorithm

Answer

Remove demographic features from the model

Answer

Retrain the model with more data

Question 16

A company uses Einstein Forecasting to predict sales. What is a key difference between the AI forecast and the rep commit forecast?

Accepted Answer

AI forecast uses historical data and trends; rep commit is based on rep's manual entry. The AI forecast in Einstein Forecasting leverages historical data, trends, and machine learning models to generate predictions, while the rep commit forecast relies on manual entries made by sales representatives. This distinction is fundamental: AI forecasts are data-driven and automated, whereas rep commits are subjective and based on individual rep judgment.

Answer

AI forecast is always more accurate than rep commit

Answer

Rep commit is used for quota setting; AI forecast is not

Answer

AI forecast is only available in Enterprise Edition

Question 17

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.

Answer

Einstein Prediction Builder

Answer

Einstein Article Recommendations

Answer

Einstein Next Best Action

Question 18

A marketing manager wants to predict which customers are most likely to respond to a new email campaign. Which type of machine learning is most appropriate?

Accepted Answer

Supervised learning. Supervised learning is appropriate because the task is to predict a binary outcome (respond or not respond) based on historical data where the outcome is known. The model learns from labeled examples of customers who did or did not respond, then predicts for new customers.

Answer

Generative AI

Answer

Unsupervised learning

Answer

Reinforcement learning

Question 19

A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?

Accepted Answer

The model training data size and distribution of values.. Low confidence scores in Einstein Object Detection typically indicate that the model has insufficient or imbalanced training data. Einstein AI requires a minimum number of labeled records (e.g., at least 100 per class) and a balanced distribution across predicted values to learn effectively. Without adequate data size and diversity, the model cannot generalize well, resulting in confidence scores below 80%.

Answer

The number of fields used as input features.

Answer

The API version used in the integration.

Answer

The permission set for the Einstein AI feature.

Question 20

A sales operations manager wants to use Einstein Lead Scoring to prioritize leads. Where can the lead score be viewed in Salesforce?

Accepted Answer

As a field on the lead record and in list views. Einstein Lead Scoring writes the score to a standard field on the Lead object, so it is visible directly on the lead record and can be surfaced in list views, reports, and filters. This makes prioritization actionable in the rep's normal workflow rather than requiring a separate analytics tool.

Answer

Only in Einstein Analytics dashboards

Answer

Only in the Einstein Lead Scoring setup page

Answer

In the Einstein Lead Scoring mobile app only