AI Associate Practice Test 38 — 15 Questions

Question 1

A company needs to analyze thousands of customer feedback comments to identify common themes and sentiment. They want to use a prebuilt Salesforce AI solution. Which approach is best?

Accepted Answer

Use Einstein Discovery to analyze the feedback data and identify themes. Einstein Discovery can perform automated statistical analysis on text data to identify themes and patterns, including sentiment analysis, without requiring custom model training.

Answer

Use Einstein Prediction Builder to predict sentiment

Answer

Use Einstein Vision and Language Platform to build a custom text classification model

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Use Einstein Bots to collect more feedback

Question 2

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 provides toxicity detection to prevent harmful outputs, PII masking to protect sensitive data, and audit trails for governance. Direct human review of promotional offers is also important for empathy and oversight.

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.

Answer

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

Question 3

Which TWO are best practices when implementing Einstein Bots? (Choose two.)

Accepted Answer

Start with high-volume, low-complexity conversations. Einstein Bots are designed to automate high-volume, low-complexity conversations first, such as password resets or order status inquiries. This approach allows the bot to handle the most frequent interactions efficiently, reducing agent workload and providing immediate ROI. Starting simple also enables easier intent training and faster deployment, aligning with best practices for conversational AI implementation.

Answer

Use complex intents for initial setup

Answer

Configure the bot to handle all customer conversations

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Deploy the bot to all channels immediately without testing

Question 4

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.. Einstein Discovery provides 'score factors' that show the most influential fields and their impact on the prediction, enabling transparency.

Answer

The 'Model Performance' tab in Einstein Discovery Settings.

Answer

The 'Prediction Summary' report in Einstein Analytics.

Answer

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

Question 5

A healthcare company is using Einstein Bots to handle patient intake. They want to ensure compliance with HIPAA and the Salesforce Trusted AI principles. Which TWO features of the Einstein Trust Layer should they enable?

Accepted Answer

Audit trail to log all AI decisions and interactions for compliance reviews.. PII masking directly protects health information, while audit trail provides accountability and traceability for compliance.

Answer

Zero data retention to ensure customer data is not stored.

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Grounding to connect AI responses to CRM data.

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Toxicity detection to identify harmful language.

Question 6

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

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Honesty

Question 7

A financial services company uses Einstein Bots to answer customer inquiries. A customer asks the bot to explain why their loan application was rejected. The bot provides a response based on AI predictions. Which Salesforce Trusted AI Principle is MOST directly addressed by the bot's ability to explain the decision?

Accepted Answer

Transparency. Transparency in AI means that decisions should be explainable and understandable to users. Providing an explanation for a loan rejection directly supports the principle of Transparency, which requires AI systems to be open about their reasoning.

Answer

Honesty

Answer

Accuracy

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Safety

Question 8

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.. Under GDPR Article 22, customers have the right to request an explanation of automated decisions and to contest them. Transparency about AI limitations is also part of Salesforce's Honesty principle.

Answer

The ability to opt out of all AI interactions entirely.

Answer

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

Answer

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

Question 9

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. Human oversight can be achieved by requiring agents to confirm before an action is taken, and by clearly presenting AI recommendations as suggestions. Disabling the AI or automating actions removes human involvement. Training agents is helpful but does not directly ensure oversight in the workflow.

Answer

Disable all AI recommendations and rely solely on agent judgment

Answer

Train all agents on how AI models work

Answer

Automate the top recommendations to save agent time

Question 10

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 11

A Salesforce administrator is setting up Einstein Next Best Action for a marketing campaign. They want to ensure that customer consent preferences are respected. Which action should they take?

Accepted Answer

Configure the recommendation strategy to check the 'HasOptedOutOfEmail' field and exclude customers who have opted out.. Consent management involves checking the customer's communication preferences and honoring opt-outs. This can be done by configuring the recommendation logic to check consent fields or using Data Cloud's consent data.

Answer

Include all customers and let the AI decide who to target, as consent is not relevant for recommendations.

Answer

Anonymize customer data before running the AI model so consent is not needed.

Answer

Use a data extension to store consents and manually update it weekly.

Question 12

A company must comply with GDPR when using Einstein Features to process customer data for AI predictions. Which THREE actions are required under GDPR that relate directly to AI-driven decisions?

Accepted Answer

Ensure that only data necessary for the prediction is used (data minimisation). GDPR requires a lawful basis for processing (e.g., consent), the right to explanation for automated decisions, and data minimization. Data retention policy is important but not exclusive to AI decisions; and bias auditing is a best practice but not a GDPR requirement.

Answer

Implement a data retention policy that deletes customer data after 30 days

Answer

Audit AI models for bias on a quarterly basis

Question 13

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

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Einstein Bots

Question 14

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

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

Question 15

A retail company uses Einstein Prediction Service to forecast customer churn. To improve model accuracy, which data preparation step is most critical?

Accepted Answer

Clean the dataset by handling missing values and outliers.. Handling missing values and outliers is the most critical data preparation step for Einstein Prediction Service because the underlying gradient boosting models (like XGBoost) are sensitive to data quality issues. Missing values can introduce bias or cause the model to misinterpret patterns, while outliers can disproportionately influence split decisions, reducing predictive accuracy for churn scenarios.

Answer

Select only the top three features based on correlation.

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Use a different algorithm like neural networks.

Answer

Increase the dataset size by collecting more customer records.