AI Associate Practice Test 23 — 15 Questions

Question 1

A financial institution uses an AI system to approve loan applications. The system denies loans to applicants from certain postal codes at a higher rate. The model includes 'postal code' as a feature. Which ethical consideration is most directly violated?

Accepted Answer

Fairness. The AI system's use of 'postal code' as a feature leads to disparate impact on applicants from certain areas, directly violating the ethical principle of fairness. Fairness requires that AI models do not discriminate against protected groups or perpetuate systemic biases, even if the feature itself is not a protected attribute. By denying loans at a higher rate based on postal code, the system is likely engaging in proxy discrimination, which is a core fairness violation.

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Privacy

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Accountability

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Transparency

Question 2

An AI Associate is asked to build a model that predicts employee performance. The dataset includes gender, department, and tenure. Which practice could introduce ethical risk?

Accepted Answer

Including gender to improve model accuracy.. Option D is correct because including gender as a feature in a predictive model for employee performance can introduce bias and lead to unfair or discriminatory outcomes. Even if the model's accuracy improves, using protected attributes like gender may violate ethical guidelines and regulations such as GDPR or anti-discrimination laws, as it could perpetuate historical biases or result in disparate impact.

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Evaluating model performance across different groups.

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Excluding gender from the model features.

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Documenting model limitations and assumptions.

Question 3

An AI system is used to approve loan applications. The model uses income, zip code, and credit score as features. What is a potential ethical concern?

Accepted Answer

Zip code may act as a proxy for race, leading to discrimination. Option A is correct because using zip code as a feature can introduce proxy discrimination. Zip codes are strongly correlated with race and socioeconomic status due to historical redlining and residential segregation. When the model learns patterns from zip code, it may inadvertently deny loans to applicants from certain racial or ethnic groups, violating fair lending laws and ethical AI principles.

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The model should be a black box to avoid bias

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Credit scores are rarely accurate

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Income is not a reliable predictor of repayment

Question 4

Which TWO practices are recommended when using AI for automated decision-making in hiring?

Accepted Answer

Regularly audit the model for adverse impact.. Option B is correct because regular auditing for adverse impact is a core ethical practice to detect and mitigate bias in AI-driven hiring systems. Audits involve statistical analysis (e.g., the four-fifths rule from the Uniform Guidelines on Employee Selection Procedures) to compare selection rates across protected groups, ensuring the model does not disproportionately disadvantage certain demographics.

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Use the AI model as the sole decision-maker.

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Use all available data including protected attributes.

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Ignore adverse impact if the model is accurate.

Question 5

A company uses Einstein Prediction Builder to recommend products. They notice the model often recommends high-priced items to users in affluent areas, potentially excluding others. What should the AI Associate do first?

Accepted Answer

Check the training data for representation and bias.. The correct first step is to check the training data for representation and bias because the model's tendency to recommend high-priced items to affluent areas suggests the training data may be skewed or contain historical biases. Einstein Prediction Builder relies on historical data to learn patterns, and if the data over-represents affluent users or under-represents others, the model will perpetuate those biases. Auditing the data for fairness and representation is the foundational step before any remediation, as per responsible AI practices.

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Remove the model from production immediately.

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Ignore the issue because the model predictions are accurate overall.

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Add more features about customer income.

Question 6

Refer to the exhibit. A team is deploying an AI model for credit scoring. The model uses a complex neural network with high accuracy. The team has performed bias testing and used a representative dataset. According to the policy, what is the MOST significant ethical gap?

Accepted Answer

The model lacks explainability, which is not required by the policy. Option D is correct because the policy explicitly requires explainability for high-risk AI models, such as those used in credit scoring. A complex neural network inherently lacks explainability, and the team has not addressed this requirement, making it the most significant ethical gap despite high accuracy and bias testing.

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The training data may not be representative

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Customer consent was not obtained

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Bias testing was not performed

Question 7

An AI Associate deploys an Einstein Bot that uses sentiment analysis to escalate frustrated customers. After launch, the bot escalates disproportionately for non-native English speakers. What is the most likely cause?

Accepted Answer

The sentiment model was trained on a non-representative dataset.. Option A is correct because the sentiment analysis model likely exhibits bias due to training data that does not adequately represent the linguistic patterns, idioms, or expressions of non-native English speakers. This causes the model to misinterpret neutral or positive statements from these users as negative or frustrated, leading to disproportionate escalations. A non-representative dataset is a common source of algorithmic bias in AI systems.

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The bot is routing to the wrong department.

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The escalation threshold is set too low.

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The bot is not properly connected to the escalation queue.

Question 8

A company is developing an AI system to assist with hiring. Which TWO practices are essential for ethical AI deployment?

Accepted Answer

Conduct regular bias audits on model predictions. Option C is correct because regular bias audits are a core ethical practice for AI systems, especially in hiring. These audits involve systematically testing the model's predictions across demographic groups to detect and mitigate unintended discrimination, ensuring compliance with fairness standards like the EEOC's Uniform Guidelines on Employee Selection Procedures.

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Optimize the system for speed to reduce waiting times

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Remove all demographic features to ensure fairness

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Maximize accuracy on historical hiring data

Question 9

A healthcare organization is deploying an AI model to predict patient readmission risk. The model was trained on historical data that underrepresented minority populations. During testing, the model shows lower accuracy for those groups. What should the data scientist do first?

Accepted Answer

Re-evaluate the training data to ensure balanced representation and consider re-sampling techniques.. Option D is correct because the first step in addressing model bias is to audit the training data for representational imbalance. Re-evaluating the data and applying re-sampling techniques (e.g., oversampling minority groups or undersampling the majority) directly targets the root cause of the disparity—skewed class distributions—before modifying the model or its features.

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Remove sensitive attributes like race and gender from the training data.

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Ignore the disparity because the model's overall accuracy is acceptable.

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Retrain the model with more complex algorithms to improve accuracy.

Question 10

An AI system is used to detect fraud in financial transactions. Which THREE steps should be taken to address ethical concerns?

Accepted Answer

Implement a human-in-the-loop for high-stakes decisions. Option C is correct because implementing a human-in-the-loop ensures that high-stakes decisions, such as blocking a legitimate transaction or allowing a potentially fraudulent one, are reviewed by a human before final action. This addresses ethical concerns by preventing fully automated decisions that could cause financial harm or violate user trust, and it aligns with principles of accountability and fairness in AI governance.

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Lower the fraud detection threshold to catch more cases

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Automatically accept all flagged transactions to improve user experience

Question 11

A company wants to use Einstein Article Recommendations to suggest knowledge articles to support agents. What is a prerequisite for this feature?

Accepted Answer

Knowledge articles must be created and published.. Einstein Article Recommendations requires that knowledge articles are created and published in the Salesforce Knowledge base. The feature uses natural language processing (NLP) to match the context of a case or conversation with published articles, so unpublished or draft articles cannot be recommended. Without published articles, the AI model has no content to analyze or suggest.

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Articles must be of a specific type, such as FAQ.

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The org must be enabled for Einstein features.

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A case must be open for the recommendation to appear.

Question 12

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.

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Chatter posts from the product team.

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Case records from the last 30 days.

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Lead and contact reports.

Question 13

A company uses Salesforce Data Platform to store customer data. They want to use this data to train an AI model for lead scoring, but they are concerned about data quality. Which step should they take first to ensure the data is suitable for AI?

Accepted Answer

Profile the data to identify missing values, outliers, and inconsistencies. Profiling the data is the essential first step because it systematically identifies missing values, outliers, and inconsistencies that degrade model performance. Without this baseline assessment, any subsequent normalization or labeling would be applied to flawed data, leading to unreliable lead scoring predictions. Salesforce Data Platform supports profiling via tools like Einstein Analytics or Data Prep, which scan fields for nulls, range violations, and format errors.

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Immediately normalize all numerical features

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Create a labeled dataset using historical lead outcomes

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Set up a data pipeline to stream data in real-time

Question 14

A data scientist needs to prepare data for Einstein Discovery. The dataset includes a field 'Customer_Status__c' with values 'Active', 'Inactive', and 'Churned'. How should this field be treated?

Accepted Answer

Keep as a text field and let Einstein Discovery handle it as a categorical predictor.. Option C is correct because Einstein Discovery natively supports text fields as categorical predictors, automatically encoding them for model training. The platform handles string values like 'Active', 'Inactive', and 'Churned' without requiring manual transformation, preserving the semantic meaning and cardinality of the data.

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Create separate boolean fields for each value to improve model accuracy.

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Remove the field because text fields cannot be used in Einstein Discovery.

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Convert to numeric values 1, 2, 3 to preserve order.

Question 15

A Salesforce admin is troubleshooting an Einstein Prediction Builder model that is not generating predictions. The model was created with a custom object 'Feedback__c'. The admin notices that the model's data source includes records with status 'In Progress' and 'Closed'. What is the most likely cause of the model not generating predictions?

Accepted Answer

The outcome field has more than two unique values. Option C is correct because Einstein Prediction Builder requires the outcome field to have exactly two unique values (binary classification). The presence of 'In Progress' and 'Closed' as statuses suggests the outcome field likely contains more than two values, which violates this requirement and prevents the model from generating predictions.

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The data source is not refreshed daily

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The object is a custom object

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The object has fewer than 1000 records