AI Associate Practice Test 43 — 15 Questions

Question 1

A financial services company uses Salesforce Einstein Prediction Builder to score leads for a premium credit card offer. The model was trained on five years of historical lead data. An audit reveals that the model consistently assigns lower scores to leads from certain postal codes with historically lower average incomes. Which ethical principle is most directly compromised by continuing to use this model without intervention?

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

Accepted Answer

Fairness, because the model produces systematically lower scores for leads in lower-income postal codes, creating disparate impact.. Fairness in AI requires avoiding unjustified disparate impact on groups. Here, using postal code as a proxy for income causes the model to systematically disadvantage leads from lower-income areas, even though postal code is not a protected attribute. This is a classic proxy discrimination scenario. Mitigation could include removing or transforming the postal code feature, rebalancing training data, or applying bias detection tools before deployment.

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Reliability, because the model was trained on five years of data and may be outdated.

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Transparency, because the model's scoring formula is not disclosed to leads.

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Accountability, because no single person is named as responsible for the model's decisions.

Question 2

A Salesforce administrator at a bank deploys an Einstein Discovery model that predicts creditworthiness. The model's top predictor is a field called 'community_score' that was derived from historical lending data in majority-minority neighborhoods. The administrator wants to ensure the model complies with ethical AI guidelines. Which action best addresses the root cause of potential bias?

Accepted Answer

Investigate the historical data and feature engineering process that created community_score, then mitigate bias at the data and feature level before retraining.. The most effective action is to investigate and mitigate bias at the data and feature engineering stages, because the root cause is historical discrimination reflected in the community_score feature. Removing one field or adding a constraint does not address the underlying data problem. Salesforce's responsible AI guidance emphasizes understanding data provenance and correcting biased inputs. By auditing how community_score was created and applying data-level mitigation before retraining, the bank reduces the chance that bias persists through correlated features.

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Add a fairness constraint to the model that enforces equal approval rates across neighborhoods.

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Remove the community_score field from the model and retrain using only financial variables.

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Deploy the model but provide adverse action notices to applicants who are declined.

Question 3

What is the purpose of ‘toxicity detection’ in the Einstein Trust Layer?

Accepted Answer

To detect and prevent harmful or abusive language in AI outputs. Toxicity detection in the Einstein Trust Layer is designed to identify and filter harmful or abusive language in AI-generated outputs before they reach the user. It uses natural language processing (NLP) models to score content against categories such as hate speech, profanity, and harassment, ensuring outputs remain safe and appropriate. This prevents the AI from inadvertently disseminating offensive material, which is critical for maintaining trust and compliance in enterprise environments.

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To increase the speed of AI responses

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To monitor user interactions for compliance with data protection laws

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To identify and mask PII in user prompts

Question 4

A company uses Einstein Conversation Insights to analyze sales call recordings. They want to identify calls where the competitor name 'Acme Corp' is mentioned and track the talk time of the sales rep vs. customer. How can they achieve this?

Accepted Answer

Configure Einstein Conversation Insights to track the keyword 'Acme Corp' and use the talk-time analysis feature. Einstein Conversation Insights is specifically designed to analyze sales call recordings, track keywords (like 'Acme Corp'), and provide talk-time analysis to compare sales rep vs. customer speaking time. This directly matches the requirement to identify competitor mentions and measure talk-time distribution.

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Use Einstein Article Recommendations to suggest articles about Acme Corp

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Use Einstein Email Insights to search for Acme Corp in emails

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Enable Einstein Lead Scoring to score leads mentioning Acme Corp

Question 5

You are a Salesforce AI Specialist at a mid-sized manufacturing company. The company uses Einstein Lead Scoring to prioritize leads. The model was trained on historical lead data and has been in production for three months. Recently, the sales team reports that high-scoring leads are not converting as expected. You investigate and find that the model's data source includes leads from the past 18 months. However, six months ago, the company changed its lead qualification process: they started requiring a demo before scoring leads as 'qualified.' As a result, the definition of a converted lead changed. What is the best course of action to improve model performance?

Accepted Answer

Retrain the model using only leads from the last six months after the process change. The change in lead qualification process six months ago introduced a data distribution shift (concept drift), making older leads no longer representative of the current conversion behavior. Retraining the model on only the last six months of data aligns the training set with the new definition of a 'converted lead,' allowing Einstein Lead Scoring to learn the updated patterns and improve prediction accuracy.

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Manually adjust the model's prediction threshold to account for the new process

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Remove the 'Demo Scheduled' field from the model to avoid bias

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Add more historical leads from before the process change to increase data volume

Question 6

A data scientist notices that a churn prediction model has high variance: small changes in training data cause large changes in predictions. Which technique is BEST to address this?

Accepted Answer

Apply regularization techniques. High variance — where small changes in training data cause large swings in predictions — is the classic signature of overfitting, and regularization (L1/L2, dropout, early stopping) is the standard remedy. Regularization penalizes model complexity, constraining the hypothesis space so the model generalizes rather than memorizing noise.

Answer

Reduce the amount of training data

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Add more features to the model

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Increase the number of training epochs

Question 7

A user asks an Einstein chatbot 'What is my current account balance?' The chatbot has been trained on transactions but is not supposed to reveal account data. Which ethical principle is at risk?

Accepted Answer

Privacy.. Revealing personal financial data violates the principle of privacy.

Answer

Accountability.

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Transparency.

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Fairness.

Question 8

A company is building an Einstein Bot for customer support. They need to ensure the bot can understand user intents and extract key information. Which THREE components are essential for this? (Choose three)

Accepted Answer

NLP training. NLP training is essential because it enables the Einstein Bot to understand natural language inputs from users. By training the bot with NLP models, it can accurately interpret user intents and extract relevant information from conversations, which is fundamental for effective customer support automation.

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Handoff to agent

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Dialog flows

Question 9

A company is implementing Einstein Activity Capture. They want to ensure that emails between the sales rep and the company's legal department are NOT logged. Which TWO actions should the admin take?

Accepted Answer

Add the legal department's email domain (e.g., @company-legal.com) to the excluded addresses list. Einstein Activity Capture provides an Excluded Addresses setting where admins can list specific email addresses or entire domains that should never have their emails or events captured and synced. Option B is correct because adding the legal department's domain (e.g., @company-legal.com) to the excluded addresses list prevents any email to or from that domain from being logged for all users. Option C is correct because adding the specific email addresses of legal team members to the excluded addresses list achieves the same suppression for those individual senders/recipients. Option A is not correct because validation rules operate on Salesforce record saves and cannot intercept or suppress Einstein Activity Capture's email ingestion process. Option D is wrong because removing the sales rep's activity logging permission would block all activity capture, not just legal-related emails. Option E is wrong because disabling Einstein Activity Capture for the legal team's profiles affects the legal users' own capture, not the sales rep's emails with them.

Answer

Configure a validation rule to suppress logging when the recipient contains legal domain

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Remove the sales rep's permission to log activities

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Disable Einstein Activity Capture for the legal team's profiles

Question 10

A company uses Einstein GPT for Sales to generate email drafts. They want to ensure that the generated content always includes the correct product pricing from a separate system. How should they achieve this?

Accepted Answer

Use Prompt Builder with a Flex Prompt template that includes a flow to retrieve pricing data.. Prompt Builder with a Flex Prompt template allows the inclusion of a flow that can dynamically retrieve product pricing from an external system via Apex or an external data source. This ensures that the generated email drafts always contain accurate, up-to-date pricing without manual intervention, leveraging Einstein GPT's generative AI capabilities with real-time data integration.

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Ask the sales reps to manually copy pricing into each prompt.

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Use Einstein Recommendation Builder to suggest pricing.

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Use Einstein Discovery to generate pricing insights.

Question 11

A company uses Salesforce Einstein to build an AI model that predicts customer churn. The model is trained on historical data from the past two years. During testing, the model shows significantly higher accuracy for male customers compared to female customers. What is the most ethical course of action?

Accepted Answer

Investigate the cause of the disparity, retrain the model with more representative data, and re-evaluate fairness.. The correct option is C: investigate the cause of the disparity, retrain the model with more representative data, and re-evaluate fairness. In this scenario, the model's significantly lower accuracy for female customers indicates a likely bias in the training data or feature selection, so the ethical response is to diagnose the root cause, improve data representativeness, and validate fairness metrics before deployment. Option A is insufficient because a disclaimer does not remediate discriminatory performance, and deploying a known biased model can cause harm. Option B ignores the ethical and business risk of deploying a model with disparate accuracy across protected groups. Option D is also wrong because manually forcing equal outputs is a post-hoc manipulation that can distort predictions and does not address the underlying bias.

Answer

Deploy the model but add a disclaimer that it may be less accurate for female customers.

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Deploy the model as is, because it still meets the overall accuracy threshold.

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Manually adjust the model's output to ensure equal churn predictions across genders.

Question 12

Under the Salesforce Data Processing Addendum (DPA), what is Salesforce's commitment regarding customer data used in AI services?

Accepted Answer

Customer data is not used to train or improve Salesforce's base AI models. Under the Salesforce Data Processing Addendum (DPA), Salesforce commits that customer data is not used to train or improve Salesforce's base AI models. This is a contractual data-protection commitment that applies to AI services such as Einstein, ensuring customer data remains the customer's and is not repurposed to enhance shared foundation models.

Answer

Customer data may be used to improve Salesforce's AI models unless the customer opts out

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Customer data is anonymized and then used to train public AI models

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Customer data is only used to train models for that specific customer

Question 13

A developer wants to build a custom application that classifies customer images (e.g., product photos) into categories using Einstein AI. Which API should they use?

Accepted Answer

Einstein Vision API. Einstein Vision API is Salesforce's service for image classification and object detection, allowing developers to train custom models on product photos and classify them into categories. It exposes REST endpoints for dataset creation, training, and prediction. Einstein Language handles text, Prediction Builder handles tabular predictions, and Bots handles conversational agents, so Vision is the correct choice for image classification.

Answer

Einstein Bots API

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Einstein Prediction Builder API

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Einstein Language API

Question 14

A nonprofit organization wants to use Einstein Bots to handle inquiries on their website. They are concerned that the bot may give incorrect or insensitive responses. Which feature should they prioritize to maintain trustworthy AI?

Accepted Answer

Implement a fallback to human agent for uncertain queries.. Implementing a fallback to a human agent for uncertain queries directly addresses the risk of incorrect or insensitive responses by ensuring that when the bot cannot confidently answer, the conversation is escalated to a human. This aligns with the principle of maintaining trustworthy AI, as it prevents the bot from generating potentially harmful or inaccurate responses and provides a safety net for complex or sensitive inquiries.

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Use a larger training dataset from generic internet sources.

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Allow the bot to generate responses only from pre-authored articles.

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Disable sentiment analysis to avoid misinterpretation.

Question 15

A company wants to implement an autonomous AI agent using Agentforce. Which TWO components are essential for building the agent in Agent Builder?

Accepted Answer

Topics. In Agent Builder, Topics are the essential organizational units that define the scope of what an agent can handle — each Topic groups related instructions, and the agent uses topic classification to route a user's request to the right area of expertise, so option B is correct. Actions are equally essential because they define the specific tasks the agent can perform within a Topic (for example, calling a Flow, Apex, or prompt template), giving the agent its executable capabilities, so option C is correct. Users (A) are the people who interact with the agent and are not a build component, Profiles (D) are Salesforce permission sets of settings that govern access rather than agent construction, and Apex triggers (E) are database event-driven automation and are not required to define an agent's topics or actions.

Answer

Users

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Profiles

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Apex triggers