AI Associate Ethical AI and Data Privacy Practice Test 5 — 15 Questions

Question 1

A sales operations team notices that an Einstein Lead Scoring model assigns lower scores to leads from a particular geographic region, even though those leads have historically converted at a higher rate. What is the most likely cause of this discrepancy?

Accepted Answer

The model has been trained on biased historical data that underrepresents or undervalues leads from that region. The model was trained on historical data that may have reflected past biases (e.g., underinvestment in that region). The biased training data leads to unfair predictions. The correct action is to audit the model for bias.

Answer

The model is using outdated lead source fields

Answer

The model's threshold for lead conversion is set too high

Answer

The model is overfitted to noise in the data

Question 2

A healthcare organization uses Einstein Prediction Builder to predict patient no-show rates. They want to ensure that protected health information (PHI) like patient names and social security numbers are not used in the model. Which Salesforce Trusted AI principle or feature directly addresses this requirement?

Accepted Answer

Data minimisation. Data minimisation is a key principle: only use relevant features for the model and avoid including sensitive PII unnecessarily. The Einstein Trust Layer's PII masking also helps, but the principle that directly addresses using only relevant fields is data minimisation.

Answer

Zero data retention

Answer

Explainability

Answer

Human oversight

Question 3

A financial services company is deploying an Einstein chatbot that provides investment advice. They want to ensure that if the chatbot generates a potentially harmful recommendation (e.g., suggesting a risky trade), the message is blocked before reaching the customer. Which Einstein Trust Layer capability should they rely on?

Accepted Answer

Toxicity detection. Toxicity detection identifies harmful or offensive content. In this context, harmful financial advice can be flagged as toxic. Human oversight (agent review) would also be applicable, but the question specifically asks about blocking before reaching the customer via a capability of the Trust Layer.

Answer

Grounding

Answer

Zero data retention

Answer

PII masking

Question 4

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 provides model explanations, including score factors (most influential fields) for individual predictions. This is the standard way to explain why a particular lead scored as it did.

Answer

Einstein Bots

Answer

Einstein Activity Capture

Answer

Einstein Automate

Question 5

A company is subject to GDPR and wants to use customer purchase history to predict future buying behavior. What is the primary requirement they must fulfill under GDPR when using customer data for AI predictions?

Accepted Answer

Obtain explicit consent from customers to use their data for AI predictions. GDPR requires a lawful basis for processing personal data, such as consent or legitimate interest. The right to explanation is also relevant for automated decisions, but the primary requirement is obtaining a lawful basis (e.g., consent) before using the data.

Answer

Ensure the AI model is hosted in the same country as the customer

Answer

Provide a discount to customers who opt in to data usage

Answer

Anonymize all customer data before training the model

Question 6

A data scientist is building a custom AI model using Salesforce Data Cloud to predict customer churn. They want to ensure that the model does not inadvertently use gender as a feature to avoid biased predictions. Which step is MOST appropriate?

Accepted Answer

Exclude gender from the feature set used for model training. The best practice is to exclude protected attributes from the model features. Data minimisation supports this. Auditing for bias is also important but does not prevent the model from using the attribute in the first place.

Answer

Use gender as a feature but ignore the model predictions for certain groups

Answer

Allow gender in training but use a post-processing technique to adjust scores

Answer

Include gender as a feature and then apply a fairness constraint during training

Question 7

A sales rep receives an AI-generated recommendation to upsell a product to a customer. The rep wants to verify the reasoning behind the recommendation before acting. What Salesforce feature can the rep use to see the key factors that influenced the recommendation?

Accepted Answer

Einstein Discovery score factors. Einstein Discovery provides prediction explanations and score factors. For recommendations within Sales Cloud, the 'Why this recommendation' feature (part of Einstein Discovery) shows the most influential fields.

Answer

Einstein Next Best Action

Answer

Einstein Activity Capture

Answer

Einstein Bots

Question 8

Which Salesforce Trusted AI principle ensures that users are informed when they are interacting with an AI-generated output or recommendation?

Accepted Answer

Transparency. Transparency requires that AI systems are open about their capabilities and limitations. Informing users that a recommendation is AI-generated is a direct application of transparency.

Answer

Safety

Answer

Accuracy

Answer

Honesty

Question 9

A company is using Einstein Service Replies to generate suggested responses for service agents. They want to ensure that agents always review and approve the suggested reply before it is sent to the customer. Which configuration best supports this requirement?

Accepted Answer

Configure Einstein Service Replies to require agent approval before sending. Human oversight requires that AI-generated content be reviewed before sending. Einstein Service Replies can be configured to require agent approval before the reply is sent, which is a direct implementation of human oversight.

Answer

Use Einstein Bots instead of Service Replies

Answer

Turn on toxicity detection for all replies

Answer

Enable auto-send for Einstein Service Replies

Question 10

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 is the specific commitment that customer data used for predictions is not stored or used for training base models. This is a key component of the Einstein Trust Layer.

Answer

Grounding

Answer

Audit trail

Answer

PII masking

Question 11

A company wants to use AI to automatically qualify leads without human intervention. However, they are concerned about potential bias in the model. Which Salesforce approach can help them detect and mitigate bias in their lead scoring model?

Accepted Answer

Einstein Trust Layer bias detection. Einstein Trust Layer includes audit trails and bias detection capabilities. Additionally, Einstein Discovery can be used to audit models for bias. The specific tool for bias detection is the Einstein Trust Layer's bias detection feature.

Answer

Einstein Activity Capture

Answer

Einstein Automate

Answer

Einstein Bots

Question 12

A company is developing an AI system that makes loan approval decisions. Under GDPR, customers have the right to request an explanation of how the decision was made. Which Salesforce feature provides this explanation?

Accepted Answer

Einstein Discovery prediction explanations. Einstein Discovery provides prediction explanations with score factors, which can be used to explain automated decisions. This aligns with the right to explanation under GDPR.

Answer

Einstein Bots

Answer

Einstein Trust Layer audit trail

Answer

Einstein Next Best Action

Question 13

A company is deploying an Einstein chatbot to handle customer support inquiries. They want to ensure compliance with data privacy regulations and ethical AI principles. Which TWO actions should they take?

Accepted Answer

Enable PII masking in the Einstein Trust Layer. PII masking protects customer privacy by removing sensitive data from requests. Human oversight ensures that automated decisions are reviewed when necessary, especially for high-stakes interactions.

Answer

Disable audit trails to reduce data storage

Answer

Store all customer chat logs indefinitely for model retraining

Answer

Allow the chatbot to use all available customer data without restrictions

Question 14

A data scientist is building a custom AI model using Salesforce Data Cloud. They want to follow best practices for data minimisation and consent management. Which THREE steps should they take?

Accepted Answer

Obtain consent from customers before using their data in the model. Using only relevant fields reduces unnecessary data collection, obtaining consent ensures compliance with privacy laws, and documenting data sources supports transparency and auditability.

Answer

Include all available fields in the model to maximize accuracy

Answer

Use synthetic data instead of real customer data

Question 15

A company is implementing Einstein Prediction Builder to forecast sales opportunities. They want to ensure transparency and trust with their sales team. Which TWO practices should they adopt?

Accepted Answer

Indicate clearly when a prediction is AI-generated. Providing explanations and indicating AI-generated predictions help sales reps understand and trust the AI outputs, aligning with transparency.

Answer

Use only historical data from the past month to train the model

Answer

Allow the model to automatically update opportunity records without human review

Answer

Hide the fact that predictions are AI-generated to avoid confusion