AI Associate Practice Test 30 — 15 Questions

Question 1

A company is deploying an AI-powered chatbot for customer service. The chatbot is trained on historical support tickets. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Checking for biased or discriminatory patterns in training data. When an AI chatbot is trained on historical support tickets, it can inherit biases present in that data — such as disproportionately negative responses to certain demographics or languages. Checking for biased or discriminatory patterns before deployment is the most important ethical consideration because it directly affects fairness and potential harm to customers. Cost, speed, and retraining are operational concerns, not ethical imperatives.

Answer

Minimizing the cost of AI training

Answer

Ensuring the chatbot responds quickly to all queries

Answer

Planning for regular model retraining

Question 2

A model trained on CRM data predicts customer lifetime value (CLV) with high accuracy, but when deployed, predictions are significantly off for new customer segments. What is the most likely cause?

Accepted Answer

The training data was not representative of the new segments. The model performs well on the original CRM data but fails on new customer segments because those segments were not represented in the training data. This is a classic case of sampling bias or covariate shift, where the training distribution does not match the deployment distribution. Since the model learned patterns specific to the original segments, it cannot generalize to unseen segments with different characteristics. The correct answer directly addresses this distribution mismatch.

Answer

Feature engineering was insufficient for the original segments

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The model is overfitting to the training data

Answer

The model architecture is too simple

Question 3

An administrator is setting up an Einstein Bot for a service cloud. The bot needs to understand when a customer types 'cancel order' to route them to a cancellation flow. Which bot component should the administrator configure to recognize this phrase?

Accepted Answer

Intent. An intent represents the goal or purpose behind a user's input, such as 'cancel order.' In Einstein Bots, intents are trained to recognize specific phrases and map them to corresponding dialog flows. By configuring an intent for 'cancel order,' the bot can accurately route the customer to the cancellation flow without relying on exact keyword matching.

Answer

Dialog

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Action

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Entity

Question 4

What does the term 'hallucination' refer to in the context of generative AI?

Accepted Answer

The model produces outputs that are not grounded in the training data or reality. In generative AI, hallucination refers to the model producing fluent, confident output that is not grounded in its training data or factual reality — for example, inventing citations, statistics, or events. It is a byproduct of probabilistic text generation, not a training bug or a keyword dependency. This is the standard definition tested on AI fundamentals exams.

Answer

The model only works when the user prompts it with specific keywords

Answer

The model has a bug in the training algorithm

Answer

The model generates creative but accurate content

Question 5

A company receives a complaint that their Einstein Next Best Action recommendations are consistently suggesting different products based on the customer's ZIP code, leading to unequal access. What should the company do first?

Accepted Answer

Review the training data for geographic bias.. The first step in addressing biased AI recommendations is to investigate the root cause. Geographic bias in training data is a common source of unequal outcomes in machine learning models like Einstein Next Best Action. Reviewing the data allows the company to identify and mitigate the bias before taking any other action.

Answer

Contact Salesforce support for a refund.

Answer

Increase the number of recommendations shown.

Answer

Disable the recommendation engine immediately.

Question 6

Which TWO are common data quality issues that can negatively impact AI model performance?

Accepted Answer

Missing values in critical fields. Option A (Missing values in critical fields) is correct because nulls or absent values in key features cause models to learn from incomplete patterns, often forcing imputation or dropping rows, which biases training and degrades prediction quality. Option E (Duplicate records in the dataset) is correct because duplicated rows over-represent certain examples, skewing class distributions and causing the model to overfit to repeated instances rather than generalizable patterns. Option B (Low model accuracy during validation) is not a data quality issue but a symptom or outcome measured after training, so it does not describe a root cause in the data itself. Option C (Insufficient storage space for data) is an infrastructure/capacity concern, not a data quality defect affecting feature integrity. Option D (Inconsistent data governance policies) is an organizational/process issue that can indirectly affect data, but it is not itself a common data quality issue such as missingness or duplication.

Answer

Low model accuracy during validation

Answer

Insufficient storage space for data

Answer

Inconsistent data governance policies

Question 7

A Salesforce admin is training an Einstein Bot to answer customer questions. Which data source should the bot use to provide accurate responses?

Accepted Answer

Knowledge articles with a published status.. Knowledge articles with a published status are the correct data source because they contain curated, approved, and structured information that Einstein Bot can reliably use to generate accurate responses. The bot leverages natural language processing to match customer questions against these articles, ensuring answers are based on verified content rather than unstructured or transient data.

Answer

Chatter posts from the product team.

Answer

Case records from the last 30 days.

Answer

Lead and contact reports.

Question 8

A sales manager wants to see how AI-predicted forecast amounts compare to sales reps' committed amounts. Which feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it directly compares AI-predicted forecast amounts against sales reps' committed amounts. This allows sales managers to see discrepancies between the AI's data-driven predictions and the reps' manual commitments, enabling more accurate pipeline management.

Answer

Einstein Discovery

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Einstein Lead Scoring

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Einstein Opportunity Scoring

Question 9

An administrator is configuring Einstein Activity Capture and wants to prevent automatic logging of emails sent to a specific external domain (e.g., legal@acme.com) due to confidentiality. How should they achieve this?

Accepted Answer

Add the domain to the Excluded Addresses list in Activity Capture settings.. Einstein Activity Capture includes a built-in 'Excluded Addresses' list within its configuration settings. Adding a domain (e.g., acme.com) to this list prevents any emails sent to or from addresses matching that domain from being automatically logged, which directly addresses the confidentiality requirement without requiring custom code or post-processing.

Answer

Set up a flow to delete the email record after it is logged.

Answer

Use Einstein Email Insights to flag emails from that domain for manual review.

Answer

Create a validation rule on the Email Message object to block logging.

Question 10

A marketing manager wants to display product recommendations on a community site powered by Experience Cloud. Which Einstein feature should they integrate?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder generates personalized product, article, and content recommendations that can be embedded in Experience Cloud sites, making it the correct feature for displaying product recommendations on a community site. It uses collaborative filtering and business rules to surface relevant items to each visitor. This is the purpose-built recommendation engine in the Einstein portfolio.

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

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Einstein Article Recommendations

Question 11

An admin wants to create a prompt template that dynamically pulls the case subject and description to generate a knowledge article draft. Which prompt template type should they use in Prompt Builder?

Accepted Answer

Field Generation. Field Generation prompt templates in Prompt Builder are designed to pull values from record fields (such as Case Subject and Case Description) and generate content into a target field, which is exactly what is needed to draft a knowledge article from case data. This template type supports dynamic merge fields tied to the object's schema.

Answer

Sales Email

Answer

Service GPT Reply Recommendation

Answer

Flex Prompt

Question 12

Refer to the exhibit. The fairness evaluation shows a disparate impact of 0.85, equal opportunity difference of 0.12, and demographic parity difference of 0.18. Which fairness thresholds are violated?

Accepted Answer

Equal opportunity and demographic parity.. The fairness evaluation shows violations of both equal opportunity and demographic parity thresholds. The equal opportunity difference of 0.12 exceeds the commonly accepted threshold of 0.1, and the demographic parity difference of 0.18 exceeds the typical threshold of 0.1. Disparate impact of 0.85 is within the acceptable range (typically 0.8 to 1.25), so it is not violated.

Answer

Demographic parity only.

Answer

Equal opportunity only.

Answer

Disparate impact only.

Question 13

A credit scoring company develops an AI model that includes social media activity as a factor. The model awards higher scores to individuals with many online connections and consistent posting. Consumer advocates argue that this penalizes individuals with limited internet access or those who value privacy. The company defends the model, stating that it predicts creditworthiness better than traditional models. However, a regulatory body is investigating potential discrimination. The company wants to address ethical concerns without completely abandoning the model. Which approach is most appropriate?

Accepted Answer

Conduct a thorough analysis to determine whether social media activity is a legitimate, non-discriminatory predictor of creditworthiness.. The scenario calls for investigating whether social media activity is a legitimate, non-discriminatory predictor of creditworthiness before deciding how to proceed, which directly addresses the regulatory discrimination concern while preserving the model if it proves valid. A thorough analysis would assess disparate impact on protected groups, such as individuals with limited internet access, and evaluate whether the model's predictive benefit justifies its use. Option A is too abrupt because it abandons the model without evidence that social media data is unlawful or unethical. Option B may reduce reliance on social media but does not determine whether the model itself is discriminatory, and Option D still uses the potentially discriminatory model for some applicants while offering only an alternative, which does not resolve the regulatory investigation.

Answer

Remove social media data from the model immediately.

Answer

Increase the weight of traditional factors like income and payment history.

Answer

Continue using the current model but offer an alternative traditional scoring option.

Question 14

An admin wants to create a prompt template that will be used by sales reps to generate emails in Sales GPT. They need to include dynamic fields from the lead record. Which tool should they use?

Accepted Answer

Prompt Builder. Prompt Builder is the correct tool because it allows admins to create reusable prompt templates that can include dynamic fields from Salesforce records, such as lead data, for use in Sales GPT. This tool is specifically designed for building and managing prompts that integrate with Einstein GPT services to generate personalized email content.

Answer

Einstein Studio

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Einstein Discovery

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Einstein Copilot

Question 15

A data scientist notices that an Einstein Discovery model predicts a low probability of conversion for all leads in a new campaign, even though the campaign targets high-value accounts. Which initial diagnostic step should be taken?

Accepted Answer

Compare the feature distributions of the training and campaign data. The most likely cause of a model predicting low conversion for all leads in a new campaign is a shift in feature distributions between the training data and the campaign data (covariate shift). Checking these distributions is the standard initial diagnostic step to identify if the model is encountering data it was not trained on, which would invalidate its predictions. This aligns with best practices for model monitoring and data validation in Einstein Discovery.

Answer

Retrain the model with the new campaign data included

Answer

Check the Einstein model recipe for incorrect filters

Answer

Increase the prediction confidence threshold