AI Associate Exam Simulation — 40 Questions

Question 1

A healthcare provider uses an AI model to predict patient readmission risk. The model is trained on historical data that underrepresents minority populations. What is the MOST significant ethical risk?

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

Accepted Answer

The model may produce biased predictions against minorities. Training on historical data that underrepresents minority populations leads to a model that has insufficient examples to learn patterns for those groups, resulting in biased predictions that systematically disadvantage minorities. This is a direct violation of fairness in AI ethics, as the model's outputs will be less accurate or equitable for underrepresented groups, potentially causing harm in critical healthcare decisions like readmission risk assessment.

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The model may overfit to the majority population

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The model cannot scale to real-time predictions

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The model lacks explainability

Question 2

A company deploys an AI-powered email composer for sales reps. The legal team requires that every AI-generated email be reviewed by a human before sending to a customer. Which approach aligns with Salesforce's Trusted AI Principle of Empathy and Human Oversight?

Accepted Answer

Require the sales rep to click 'Approve' before the email is sent. It directly implements human oversight by requiring the sales rep to click 'Approve' before the email is sent, aligning with Salesforce's Trusted AI Principle of Empathy and Human Oversight. This principle mandates that AI systems should include mechanisms for human review and control, especially in high-stakes communications, ensuring that AI-generated content is vetted for accuracy, tone, and compliance before reaching customers. The approval step creates a clear human-in-the-loop checkpoint, preventing automated sending without human judgment.

Answer

Allow the AI to send emails automatically but log all sent emails for audit

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Use a rule to block specific words in AI-generated emails

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Send a daily report of all AI-generated emails to the legal team after they are sent

Question 3

A data scientist is building a churn prediction model using Einstein Discovery. They want to ensure the model does not rely on sensitive attributes like race or gender, even if those are correlated with other features. Which technique is MOST aligned with Salesforce's data minimisation principle?

Accepted Answer

Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them. Data minimisation means using only the data necessary for the task. Excluding sensitive attributes from the feature set is the most direct way to prevent them from being used, even if they are correlated with other features. Correlation does not imply causation, and if those features are not essential, they should be removed.

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Include all features and rely on the AI to ignore biased ones

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Apply a fairness constraint after training to adjust predictions

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Use differential privacy to add noise to the training data

Question 4

A company is deploying Einstein Reply Recommendations for a sales team. To comply with Salesforce's Trusted AI principle of transparency, what must the company ensure about the AI-generated replies?

Accepted Answer

Sales reps are informed that the reply is AI-generated and can review before sending. Salesforce's Trusted AI principle of transparency requires that users understand when they are interacting with AI-generated content and have the ability to review it before it is used. For Einstein Reply Recommendations, this means sales reps must be explicitly informed that the suggested reply is AI-generated and must be given the opportunity to review, edit, or reject it before sending. This human-in-the-loop approach ensures accountability and aligns with the transparency pillar of Salesforce's Trusted AI framework.

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The AI model is fine-tuned on the company's past sales data

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The AI-generated replies are automatically sent without human review

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Customer data used to generate replies is retained for 90 days

Question 5

Which Einstein feature provides automated statistical analysis of Salesforce data, including story creation and improvement suggestions?

Accepted Answer

Einstein Discovery. Einstein Discovery provides automated statistical analysis, generates stories, waterfall charts, and improvement suggestions.

Answer

Einstein Forecasting

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Einstein Prediction Builder

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Einstein Recommendation Builder

Question 6

A sales director wants to implement lead scoring but has no historical data on which leads converted. What approach can the team use to start?

Accepted Answer

Use unsupervised learning to cluster leads into segments and score based on cluster characteristics. When no historical conversion labels exist, supervised learning cannot be used because there is no target variable to train on. Unsupervised learning — specifically clustering algorithms such as k-means or hierarchical clustering — can segment leads based on features like industry, engagement, and firmographics, and scores can then be assigned based on cluster characteristics. This provides a data-driven starting point without requiring labeled outcomes.

Answer

Train a supervised model using assumptions as labels

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Skip lead scoring until enough conversion data is collected

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Use reinforcement learning to learn scoring from sales team feedback

Question 7

A sales operations manager wants to use Einstein GPT for Sales to improve rep productivity. Which THREE tasks can Sales GPT perform?

Accepted Answer

Summarize sales calls. Einstein GPT for Sales includes a call summarization feature that uses generative AI to automatically create concise summaries of sales calls from transcripts. This directly improves rep productivity by saving time on manual note-taking and capturing key action items.

Answer

Create sales dashboards

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Predict lead scores

Question 8

A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)

Accepted Answer

Ensure the training data includes both converted and unconverted leads.. Option D is correct because Einstein Lead Scoring is a supervised machine-learning model that learns patterns distinguishing successful outcomes from failures, so the training dataset must contain a balanced mix of converted (won) and unconverted (lost) leads to build a predictive model. Option E is correct because the model's accuracy depends on continuous feedback: as sales reps update lead statuses and conversion outcomes in Salesforce, the model retrains on that fresh labeled data, so regularly reviewing and providing accurate conversion feedback keeps scores relevant and improves predictive quality. Option A is incorrect because restricting scoring to demographic data alone ignores behavioral and engagement signals (such as email opens, website visits, and activity history) that are among the strongest predictors in Einstein Lead Scoring. Option B is incorrect because a single fixed score threshold for all users ignores differing sales processes and territories; thresholds should be configurable per user or business unit. Option C is incorrect because disabling the model for low-volume leads would prevent scoring exactly where prioritization help is needed, and Einstein Lead Scoring requires sufficient historical data rather than being turned off for low-volume segments.

Answer

Include only demographic data for scoring.

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Set a fixed score threshold for all users.

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Disable the model for low-volume leads.

Question 9

A sales director wants to use AI to prioritize leads that are most likely to convert. The company has historical data on leads that includes whether they converted (yes/no) and various attributes. Which machine learning type should be used?

Accepted Answer

Supervised learning (classification). The problem is a binary outcome (converted: yes/no) predicted from historical labeled data with various attributes. This is a classic supervised learning classification task: the model learns from labeled examples (leads with known conversion outcomes) to predict the class of new leads. Classification is the correct ML type for predicting discrete categories.

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Generative AI

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Reinforcement learning

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Unsupervised learning (clustering)

Question 10

A sales manager wants to implement Einstein Automated Contacts to automatically create contacts from email interactions. The admin enables the feature and assigns the permission set. However, no contacts are being created automatically. What is the most likely reason?

Accepted Answer

The admin did not set up the contact creation criteria in the Einstein Automated Contacts settings.. Einstein Automated Contacts requires the admin to explicitly define the criteria that trigger automatic contact creation from email interactions. Simply enabling the feature and assigning the permission set does not configure the rules for when and how contacts should be created. Without these criteria, the system has no instructions to act upon, so no contacts are generated.

Answer

The users have not logged any emails to Salesforce.

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The feature requires at least 500 emails to be processed before it starts creating contacts.

Answer

The users need to install a plugin for their email client.

Question 11

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Accepted Answer

Summarizing case conversations from service interactions.. Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Answer

Generating report charts and dashboards.

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Creating workflow rules based on user behavior.

Question 12

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

Accepted Answer

Einstein Trust Layer with model explainability features.. The Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

Answer

Data Mask to protect sensitive information.

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Salesforce Shield with encryption and monitoring.

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Field Audit Trail to track changes to data.

Question 13

An organization uses Einstein to predict sales opportunities. They notice the model performs poorly for small businesses. What is the most ethical approach?

Accepted Answer

Retrain the model with more data from small businesses.. Retrain the model with more data from small businesses. Poor performance for small businesses most likely reflects underrepresentation in the training data, so adding more small-business examples improves the model's accuracy and fairness for that segment without abandoning it. This is the most ethical approach because it directly addresses the bias rather than hiding or accepting it. Option A does not fix the underlying data imbalance and may reduce overall accuracy, option B creates a fragmented solution that can still be biased and harder to govern, and option D is unethical because it ignores a customer segment based on revenue rather than addressing the model's deficiency.

Answer

Reduce the model's complexity to improve performance.

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Use a completely different model for small businesses.

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Ignore small businesses as they contribute little revenue.

Question 14

A sales team uses an AI tool to recommend products to customers. The tool recommends high-commission products over what best fits the customer. Which ethical principle is being violated?

Accepted Answer

Fairness. Fairness, because the AI tool is prioritizing high-commission products over what best fits the customer, which creates a biased and inequitable recommendation outcome that disadvantages the customer for the company's financial gain. Fairness in AI requires that systems avoid unjust bias and treat stakeholders equitably, and here the model is optimizing an inappropriate objective that skews recommendations. Privacy (A) is not implicated because no personal data misuse or unauthorized disclosure is described. Transparency (B) is not the core violation because the issue is not necessarily a lack of disclosure about how the tool works, but the biased recommendation itself. Accountability (D) is also not the primary violation because the scenario does not focus on who is responsible or answerable for the tool's outcomes.

Answer

Privacy

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Transparency

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Accountability

Question 15

An admin wants to create a custom AI model to predict lead conversion using Einstein Prediction Builder. Which TWO items must they select when creating the model? (Choose two)

Accepted Answer

Data set (records to train on). The data set defines the records (e.g., leads, opportunities) that the model will use for training. Without specifying which records to train on, the model has no source of historical data to learn patterns from. Einstein Prediction Builder requires you to select a data set (such as a report or object) to provide the training examples.

Answer

Model algorithm type

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Prediction explanation settings

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Features (input fields)

Question 16

A sales operations manager wants to automatically log all emails and events from sales reps' Outlook accounts to Salesforce without manual setup. Which feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct feature because it automatically syncs emails and events from Microsoft 365 or Google Workspace into Salesforce without requiring manual setup or user-installed add-ins. It uses a server-side integration that logs activities directly to Salesforce records, meeting the requirement for automatic logging of Outlook emails and events.

Answer

Einstein Conversation Insights

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Einstein Lead Scoring

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Einstein Email Insights

Question 17

A retail company deploys an AI system that adjusts prices dynamically based on customer browsing history. The system charges higher prices to returning customers. This practice is known as:

Accepted Answer

Unfair price discrimination. Unfair price discrimination, because the system charges different prices to customers based on their personal browsing history, specifically penalizing returning customers with higher prices, which is a discriminatory pricing practice. This goes beyond legitimate personalization since it exploits customer loyalty data to charge more, raising ethical and fairness concerns. Option A, lack of transparency, is a related but distinct issue—the problem here is the discriminatory pricing itself, not merely that it is undisclosed. Option C, personalization, refers to tailoring offerings or experiences to users, but it does not inherently involve charging different prices to different customers. Option D, dynamic pricing, involves adjusting prices based on factors like demand, time, or inventory, not on individual customer identity or history.

Answer

Lack of transparency

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Personalization

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Dynamic pricing

Question 18

A company wants to use customer purchase history to train a recommendation model. Which action is essential to comply with data privacy regulations?

Accepted Answer

Obtain explicit consent from customers.. Data privacy regulations such as GDPR and CCPA require a lawful basis for processing personal data, and explicit consent is a primary lawful basis when using customer purchase history for training a recommendation model. Without obtaining explicit consent, the company would be processing personal data without a valid legal ground, violating regulations that mandate transparency and user control over their data.

Answer

Use only publicly available data.

Answer

Ignore regulations because data is internal.

Answer

Anonymize the data after training.

Question 19

A mid-sized company uses Salesforce for sales and service. They have implemented Einstein Prediction Builder on a custom object 'Support_Ticket__c' to predict whether a ticket will be escalated (field: 'Escalated__c' Boolean). The model was trained with 10,000 records and 15 fields including 'Subject', 'Description_Summary__c', 'Priority__c', 'Hours_to_Resolution__c', and others. After deployment, the model's precision for escalated tickets is only 30%, while recall is 80%. The business finds too many false positives. The admin notices that the 'Priority__c' field has many missing values (60% null) and that the field 'Is_Critical__c' (a formula field) was included though it flags tickets as critical only rarely. The data spans 12 months but the last 3 months have a significantly higher escalation rate due to a product bug that has since been fixed. Which course of action will most likely improve the model's precision without harming recall?

Accepted Answer

Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority.. It addresses both the data drift and data quality issues that degrade precision. Excluding the last 3 months removes the biased escalation pattern caused by a fixed product bug, ensuring the model learns from stable historical patterns. Imputing missing 'Priority__c' values with the most common priority reduces noise from nulls without discarding the field entirely, which helps maintain recall by preserving predictive signal.

Answer

Roll back the model to the version trained 6 months ago when escalation rates were lower.

Answer

Exclude the 'Priority__c' field from the model and retrain.

Answer

Remove the 'Is_Critical__c' field and increase training data to 50,000 records.

Question 20

A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)

Accepted Answer

Including seasonality and trend components from historical data. Option B is correct because retail sales data almost always exhibits recurring seasonal peaks (e.g., holidays, back-to-school) and long-term trends, so explicitly modeling seasonality and trend components (as in SARIMA, Prophet, or seasonal decomposition) materially improves forecast accuracy. Option D is correct because missing values, outliers, and inconsistent records in five years of monthly data bias parameter estimation and can cause the model to learn spurious patterns, so cleaning and imputing the training data is a prerequisite for reliable predictions. Option E is correct because sales are driven by exogenous events such as holiday calendars, promotions, and markdowns; incorporating these as external regressors (e.g., in Prophet or SARIMAX) captures demand shifts that pure historical autocorrelation cannot explain. Option A does not belong because algorithm popularity is irrelevant to accuracy; the right choice depends on data characteristics, and a trendy algorithm can underperform a simple, well-tuned one. Option C does not belong because deep learning is not inherently more accurate for a small tabular dataset of five years of monthly sales, and simpler models often generalize better with limited data.

Answer

Choosing the most popular AI algorithm on the market

Answer

Using a deep learning model instead of a simpler model