AI Associate Ethical AI and Data Privacy Practice Test 4 — 15 Questions

Question 1

A sales manager notices that Einstein Lead Scoring assigns lower scores to leads from a specific region. After investigation, they find that the historical conversion data for that region is sparse and unrepresentative. What should the manager do to improve the model's fairness?

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

Accepted Answer

Collect more data from that region to provide a representative sample for retraining. The core issue is data sparsity and unrepresentative historical conversion data for that region. By collecting more data from that region, the model can be retrained on a balanced dataset, which directly addresses the root cause of the biased lead scoring. This aligns with the principle of fairness in AI, where models must be trained on representative data to avoid systematic discrimination.

Answer

Adjust the lead scoring formula manually to increase scores for that region

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Remove the region field from the model

Answer

Accept the scores as-is because the model is certified

Question 2

A company is deploying Einstein Article Recommendations on its customer portal. They want to ensure customers know that recommendations are AI-generated. Which action aligns with the Salesforce Trusted AI Principle of Honesty?

Accepted Answer

Include a note stating 'Recommended by Einstein AI' on each recommendation. The Salesforce Trusted AI Principle of Honesty requires transparency about AI-generated content. By explicitly stating 'Recommended by Einstein AI' on each recommendation, the company clearly discloses that the recommendations are AI-generated, building trust with customers. This aligns with the principle's focus on avoiding deception and ensuring users understand the nature of the content they are seeing.

Answer

Provide a chatbot that can explain the recommendations

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Use a generic label like 'Recommended for you'

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Do not mention AI; just show the recommendations

Question 3

A company wants to use Einstein Prediction Builder to predict which customers are likely to churn. They have a dataset that includes customers' names, email addresses, and detailed purchase history. According to the data minimisation principle, which fields should be included in the model?

Accepted Answer

Only the purchase history. Data minimisation requires using only the data necessary for the stated purpose. For predicting churn, purchase history is the relevant behavioral signal; names and email addresses are personally identifiable information (PII) that add little predictive value and increase privacy risk, so they should be excluded.

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Only the email addresses, since they are unique identifiers

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All three fields because more data improves accuracy

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Names and purchase history, but not email addresses

Question 4

A customer service manager wants to use Einstein Bots to handle common inquiries. They are concerned about the bot generating offensive responses. Which Einstein Trust Layer feature should they enable to minimize this risk?

Accepted Answer

Toxicity detection. Toxicity detection in the Einstein Trust Layer scans both prompts and completions for harmful, offensive, or toxic content and can block or flag such responses. Enabling it directly addresses the manager's concern about the bot generating offensive replies, without needing custom model changes.

Answer

Zero Data Retention

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Grounding

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PII masking

Question 5

A company uses Einstein Discovery to build a model that predicts customer lifetime value. They want to ensure the model's decisions are auditable and that they can track the business impact of AI recommendations over time. What should they implement?

Accepted Answer

Audit trail. An audit trail logs when AI recommendations are made and whether they were followed, and can be used to track outcomes over time. This enables accountability and measurement of AI impact, supporting transparency and governance.

Answer

Score factors

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Grounding

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Toxicity detection

Question 6

A company is implementing Einstein Next Best Action for their customer service agents. They want to ensure that AI recommendations are provided as suggestions but that agents retain the final say. Which TWO practices support this goal of human oversight?

Accepted Answer

Configure the system to require agent confirmation before executing any AI-recommended action. Option D is correct because configuring the system to require explicit agent confirmation before executing any AI-recommended action enforces a human-in-the-loop control, ensuring the agent authorizes every action rather than the AI acting autonomously. Option E is correct because displaying AI recommendations as optional suggestions that agents can accept or ignore directly implements human oversight, keeping the agent as the decision-maker while still benefiting from AI guidance. Together, these practices align with Einstein Next Best Action's design of surfacing recommendations while preserving agent discretion. Option A is wrong because disabling all AI recommendations eliminates the AI value entirely rather than providing suggestions with oversight. Option B, while useful for adoption, is a training activity and does not itself enforce human oversight of AI actions. Option C is wrong because automating top recommendations removes the agent's final say, contradicting the stated goal.

Answer

Disable all AI recommendations and rely solely on agent judgment

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Train all agents on how AI models work

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Automate the top recommendations to save agent time

Question 7

A sales team uses Einstein Lead Scoring and notices that the model gives disproportionately low scores to leads from a certain demographic group. The team suspects historical bias in the training data. Which THREE steps should they take to address this bias?

Accepted Answer

Investigate the historical data to identify and correct labeling errors or sampling bias. Option A is correct because bias in Einstein Lead Scoring typically originates in the training data, so investigating historical records to find and fix mislabeled outcomes or sampling bias directly addresses the root cause. Option B is correct because auditing the model for disparate impact quantifies whether the affected demographic receives systematically lower scores, providing the evidence needed to confirm and monitor the bias. Option E is correct because adding features that are not correlated with the protected attribute gives the model more legitimate predictive signal, reducing its reliance on biased proxies. Option C is not appropriate because manually inflating scores by a fixed percentage is an arbitrary post-hoc adjustment that masks the underlying bias and can distort rankings without fixing the model. Option D is not appropriate because abandoning Einstein Lead Scoring entirely discards a useful tool rather than remediating the data and model, and manual scoring does not inherently eliminate bias.

Answer

Manually increase scores for the affected group by a fixed percentage

Answer

Remove the model and rely on manual lead scoring

Question 8

A sales team is using Einstein Lead Scoring and notices that leads from a certain geographic region are consistently scored lower, even when the lead's profile matches high-performing customers from other regions. What is the most likely cause and recommended first step?

Accepted Answer

The model may be biased due to underrepresented data from that region in the training set. Audit the model for bias and review training data demographics.. When a model systematically scores one geographic segment lower despite comparable profiles, the most likely cause is underrepresentation or bias in the training data for that region. The correct first step is to audit the model for bias and review the training data demographics, because Einstein Lead Scoring learns patterns from historical data and will reproduce any historical skew. Auditing before retraining ensures the fix addresses the root cause rather than amplifying it.

Answer

The model is accurate; the region is genuinely underperforming. The team should accept the scores.

Answer

The region has lower quality leads, so the scores are correct. The team should focus on other regions.

Answer

Retrain the model with more data from that region, even if it means duplicating records.

Question 9

A company wants to implement an AI-powered customer service chatbot that generates responses based on customer inquiries. To comply with GDPR requirements for automated decision-making, what must the company provide to customers?

Accepted Answer

A clear explanation of how the AI reaches its conclusions and the option to request human review.. GDPR Article 22 grants data subjects the right not to be subject to a decision based solely on automated processing that produces legal or similarly significant effects, and Article 13-15 require meaningful information about the logic involved. For an AI chatbot making decisions with significant impact, the company must provide a clear explanation of how the AI reaches conclusions and offer the option to request human review. This directly satisfies the GDPR transparency and human-intervention requirements for automated decision-making.

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The ability to opt out of all AI interactions entirely.

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A copy of the AI model's source code upon request.

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An assurance that no personal data is used in training the AI model.

Question 10

A financial services company is deploying Einstein Prediction Builder to predict loan default risk. They are concerned about using sensitive attributes like race or gender in the model. Which data governance practice should they apply?

Accepted Answer

Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.. The correct governance practice is to exclude sensitive attributes such as race or gender from model features unless they are essential and legally permitted, and to verify that no proxy variables (e.g., ZIP code, name) indirectly encode them. This reduces disparate impact and aligns with fair lending regulations like ECOA and GDPR. Simply masking or including them still allows the model to learn discriminatory patterns.

Answer

Use synthetic data to replace sensitive attributes with random values.

Answer

Include all available attributes to maximize model accuracy, then apply fairness constraints.

Answer

Mask the sensitive attributes but still include them in the model training.

Question 11

A Salesforce admin is configuring Einstein Bots for a customer service channel. They want the bot to automatically send promotional offers to customers identified as high-value. Which combination of settings best ensures ethical AI and compliance with data privacy regulations?

Accepted Answer

Enable the Einstein Trust Layer with toxicity detection and PII masking, and require human approval before the bot sends any promotional messages.. The Einstein Trust Layer is Salesforce's built-in architecture for secure, ethical AI, providing data masking (PII), toxicity detection, zero data retention, and audit trails. For promotional messages to high-value customers, combining PII masking with toxicity detection protects sensitive data and content, while mandatory human approval ensures accountability for outbound marketing decisions — a core ethical AI principle. This layered approach satisfies both data privacy regulations (GDPR, CCPA) and Salesforce's own Trusted AI guidelines.

Answer

Disable the Einstein Trust Layer to improve response speed, and rely on the bot's internal moderation.

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Use a custom AI model without the Trust Layer, but log all bot decisions for audit.

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Enable the Einstein Trust Layer with toxicity detection only, and allow the bot to send messages automatically.

Question 12

A company uses Einstein Discovery to analyze sales data and provide recommendations. A sales rep wants to understand why a specific opportunity was predicted to close. Which Einstein feature should the rep use?

Accepted Answer

The 'Score Factors' or 'Prediction Explanation' component available on the record page.. The 'Score Factors' or 'Prediction Explanation' component on the record page is the correct answer because it provides a transparent breakdown of the top factors that influenced a specific prediction for an individual record. This feature, often called 'explainability' or 'prediction explanation', shows how each factor contributed to the predicted outcome, such as why an opportunity is likely to close. It is designed exactly for sales reps to understand the reasoning behind a prediction at the record level, enabling them to take informed actions.

Answer

The 'Model Performance' tab in Einstein Discovery Settings.

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The 'Prediction Summary' report in Einstein Analytics.

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The 'Data Quality' dashboard to check if the prediction is reliable.

Question 13

A healthcare organization is using Einstein Bots to schedule patient appointments. They are subject to HIPAA regulations. What is the most important configuration they must apply to the Einstein Trust Layer?

Accepted Answer

Enable PII masking and configure it to identify and mask health-related data fields.. HIPAA requires protection of Protected Health Information (PHI). PII masking in the Einstein Trust Layer ensures that PHI is not exposed or retained in logs or training.

Answer

Set up an audit trail for all bot interactions.

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Enable toxicity detection to ensure the bot does not use offensive language.

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Disable data retention to ensure no customer data is stored.

Question 14

A data scientist is training a custom AI model using Salesforce Data. They want to ensure that the model does not inadvertently learn patterns from Personal Identifiable Information (PII) fields such as Social Security numbers. Which approach aligns with Salesforce's responsible AI practices?

Accepted Answer

Exclude PII fields from the training dataset entirely and use only non-sensitive features.. Salesforce's responsible AI guidance emphasizes data minimization and privacy by design — excluding PII fields from the training dataset entirely prevents the model from learning patterns tied to sensitive attributes like SSNs. This is the most robust approach because it eliminates the risk at the source rather than trying to filter or mask outputs after the fact. Using only non-sensitive features aligns with principles of purpose limitation and data minimization.

Answer

Use the PII fields but restrict the model from outputting them through post-processing filters.

Answer

Anonymize the PII fields by hashing them and include the hashed values as features.

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Include all fields initially, then use feature importance to prune PII fields after training.

Question 15

A company is implementing Einstein Lead Scoring and wants to ensure transparency for sales reps. According to Salesforce's Trusted AI principles, what should the company communicate to users about the AI-generated scores?

Accepted Answer

The scores are AI-generated, the key factors influencing each score are available, and reps should use their judgment.. Salesforce's Trusted AI principles emphasize transparency, accountability, and human oversight. For Einstein Lead Scoring, the company should inform users that scores are AI-generated, provide access to key factors influencing each score, and remind reps to use their judgment. This aligns with the principle of explainability.

Answer

The scores are based on a secret algorithm to prevent gaming the system.

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Only managers can see the score factors to avoid confusion.

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The scores are 100% accurate and should be followed without question.