AI Associate Practice Test 8 — 10 Questions

Question 1

A company wants to use Einstein Article Recommendations to surface relevant knowledge articles to its support agents. What two data components are required to set up this feature?

Accepted Answer

Knowledge Article View event data and Case records. Einstein Article Recommendations uses historical Knowledge Article View event data to understand which articles agents have found useful in the past, and Case records to provide context about the current issue. By analyzing patterns between case attributes and article views, the AI can predict and surface the most relevant articles for a given case. Without both data components, the recommendation engine cannot learn the association between case details and article usefulness.

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Email-to-case logs and Knowledge Article feedback

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Knowledge Article categories and Case priority

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Community user activity and Knowledge Article ratings

Question 2

Which THREE of the following are Einstein features available for Sales Cloud? (Choose three.)

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is a correct Einstein feature for Sales Cloud because it uses predictive AI models to automatically score leads based on historical conversion data, helping sales reps prioritize high-quality leads. It is natively integrated into Sales Cloud without requiring additional licenses or complex setup, leveraging standard Salesforce objects and fields.

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Einstein Bot

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Einstein Analytics

Question 3

An AI Associate reviews the Lead Scoring model exhibit. What is the primary ethical concern with this model?

Accepted Answer

The model has low recall, potentially missing minority class leads.. The primary ethical concern is that the model has low recall, meaning it fails to identify a significant portion of actual positive leads (the minority class). In a lead scoring context, this can result in missed business opportunities and potential bias against certain customer segments, as the model systematically overlooks valuable leads that do not fit the majority pattern.

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The model uses too many features.

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The model is not explainable.

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The training data is imbalanced.

Question 4

A company is deploying an AI-powered chatbot to handle customer service inquiries. The bot uses historical chat data for training. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Obtaining consent from customers whose data is used for training. The most critical ethical consideration is obtaining consent from customers whose historical chat data is used to train the chatbot. Under regulations like GDPR and CCPA, personal data (including chat transcripts) requires explicit consent for processing, especially when used to train an AI model. Deploying without consent violates data privacy laws and erodes user trust, regardless of the chatbot's technical performance.

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Maximizing the chatbot's response accuracy

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Ensuring the chatbot can handle high traffic volumes

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Designing a human handoff protocol for complex issues

Question 5

A marketing team wants to predict which segment of customers will likely purchase a new product. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Segment Prediction. Einstein Segment Prediction uses predictive modeling and machine learning to analyze historical customer data and identify which segments are most likely to purchase a new product. It is specifically designed for predictive segmentation based on behavioral and demographic patterns, making it the most appropriate choice for this use case.

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Custom Reports

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List Views

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Campaigns

Question 6

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.

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Model algorithm type

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Prediction explanation settings

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Features (input fields)

Question 7

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

Accepted Answer

Users must have the Einstein Relationship Intelligence permission set.. The Einstein Relationship Intelligence permission set grants users access to the feature's insights and data processing capabilities. Without this permission set, users cannot view or interact with Einstein Relationship Intelligence data, even if the feature is enabled at the org level.

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Exchange or Gmail must be used for email.

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The feature is only available in Unlimited Edition.

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All users must opt in to email logging.

Question 8

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

Accepted Answer

It predicts a binary outcome (e.g., convert or not). Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

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It predicts a numeric value

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It predicts the value of a text field

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It is currently retraining the model

Question 9

An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?

Accepted Answer

Fairness. The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.

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Transparency

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Accountability

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Privacy

Question 10

A company is training a customer service chatbot using historical conversation logs. Which TWO data preparation practices should be followed to ensure data quality?

Accepted Answer

Include answers with varied phrasing to enhance language variety. Training a chatbot on varied phrasing (e.g., synonyms, different sentence structures) improves its ability to understand and generate natural language responses. This practice enhances the model's robustness and generalization, preventing overfitting to a narrow set of expressions and ensuring it can handle the diverse ways customers phrase their queries.

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Exclude all user identifiers to protect privacy

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Include only successful interactions that were resolved

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Filter only English conversations for consistency