AI Associate Practice 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?

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.

Answer

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

Answer

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

Answer

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.

Answer

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

Answer

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.

Answer

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

Answer

The AI-generated replies are automatically sent without human review

Answer

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

Answer

Einstein Prediction Builder

Answer

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

Answer

Skip lead scoring until enough conversion data is collected

Answer

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

Answer

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.

Answer

Set a fixed score threshold for all users.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

Salesforce Shield with encryption and monitoring.

Answer

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.

Answer

Use a completely different model for small businesses.

Answer

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

Answer

Transparency

Answer

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)

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