AI Associate Practice Test 8 — 10 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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