AI Associate Practice Test 6 — 10 Questions

Question 1

A service manager wants to automatically assign the correct 'Type' and 'Priority' fields on incoming cases. Which feature automatically classifies cases using AI?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses AI to automatically predict and assign the 'Type' and 'Priority' fields on incoming cases based on historical case data and patterns. It leverages machine learning models trained on past cases to classify new cases without requiring manual rules or human intervention, directly meeting the service manager's requirement.

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Einstein Article Recommendations

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Einstein Prediction Builder

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Einstein Bots

Question 2

An organization wants to use Einstein GPT to generate case summaries. However, they need to ensure that the generated text adheres to company style and includes specific required fields. Which tool should they use to customize the prompts?

Accepted Answer

Prompt Builder. Prompt Builder is the correct tool because it allows administrators to create and manage custom prompt templates that enforce company style and required fields when generating content with Einstein GPT. Unlike other options, Prompt Builder is specifically designed to tailor generative AI outputs by defining instructions, variables, and guardrails for use cases like case summaries.

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Einstein Copilot

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Einstein Next Best Action

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Einstein Service GPT

Question 3

A service manager wants to provide agents with real-time suggestions for knowledge articles while they are working on a case. Which Einstein feature should be enabled?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it uses AI to analyze the context of a case (such as subject, description, and product) and surfaces relevant knowledge articles in real-time for agents. This directly matches the requirement of providing suggestions while agents work on a case, improving resolution speed and accuracy.

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Einstein Case Classification

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Einstein Discovery

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Einstein Next Best Action

Question 4

A developer needs to classify images of products into categories using a custom model. They have labeled image data. Which Einstein platform should they use?

Accepted Answer

Einstein Vision and Language Platform. The Einstein Vision and Language Platform is the correct choice because it provides pre-built APIs and custom model training capabilities specifically for image classification tasks. It allows developers to upload labeled image datasets and train a custom model to classify products into categories using deep learning techniques.

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Einstein Prediction Builder

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Einstein Recommendation Builder

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Einstein Discovery

Question 5

A company uses Einstein Lead Scoring but notices that many leads with high scores are not converting. The admin wants to understand which factors are influencing the score. Where can the admin find this information?

Accepted Answer

On the lead record, in the Einstein Lead Score component, by clicking 'Score Factors'. The Einstein Lead Score component on the lead record includes a 'Score Factors' link that, when clicked, displays the top positive and negative factors influencing the lead score. This is the designated UI element for admins to gain transparency into which attributes (e.g., source, industry, behavior) are driving the score, without needing to access setup menus or external support.

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In the Einstein Setup menu under Lead Scoring Models

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By contacting Salesforce support for a model explanation

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By running a report on the Lead object with the Einstein Score field

Question 6

An admin is building a custom AI prediction with Einstein Prediction Builder for a binary classification problem. Which THREE steps are required in the configuration? (Choose 3)

Accepted Answer

Select a prediction field (the field to predict). In Einstein Prediction Builder, the core configuration flow for a binary classification prediction requires you to first choose the prediction field (the field whose value the model will predict), which is exactly what option B states, since the model needs a defined target variable. Option C is also required because you must select the data set — the object and the set of records (with filters) used to train the model — so the algorithm has historical examples to learn from. Option D is required as well: you must select the features (input fields from the object) that the model will use as predictors, since without input variables no model can be trained. Option A is not required because Prediction Builder builds models automatically from selected fields and does not need a custom Apex class for data transformation. Option E is not required because retraining is handled by Einstein's scheduled model refresh, not by an admin-configured Apex trigger running daily.

Answer

Define a custom Apex class for data transformation

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Configure a trigger to retrain the model daily

Question 7

A sales manager wants to see an AI-generated prediction of which opportunities are most likely to close, along with the key factors influencing that prediction. Which feature provides this capability directly in the opportunity record?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is the correct feature because it directly provides an AI-generated prediction of which opportunities are most likely to close, along with the key factors influencing that prediction, all displayed within the opportunity record. This feature uses machine learning models to analyze historical data and assign a score (0–100) to each opportunity, surfacing the top positive and negative influencing factors to help sales reps prioritize their efforts.

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Einstein Forecasting

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Einstein Activity Capture

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Einstein Lead Scoring

Question 8

An AI model predicts loan approvals, and the bank notices that the model disproportionately denies loans to a certain demographic group. Which combination of actions addresses the AI bias according to Salesforce's Trusted AI principles?

Accepted Answer

Audit the model for bias, provide transparency on decision factors, and require human review for denied applications. It aligns with Salesforce's Trusted AI principles, which emphasize accountability, transparency, and human oversight. Auditing the model for bias identifies disparities, providing transparency on decision factors ensures stakeholders understand how outcomes are determined, and requiring human review for denied applications introduces a safeguard against automated discrimination. This combination addresses bias without abandoning AI's benefits.

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Disable the AI model and make all decisions manually

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Remove demographic data from the model entirely and continue using it

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Retrain the model with more data from the affected group and deploy automatically

Question 9

An organization using Einstein Prediction Builder wants to ensure that no customer personally identifiable information (PII) is used in model training. Which data governance practice should they enforce?

Accepted Answer

Data minimisation by selecting only non-PII fields as predictors. The question specifically asks how to ensure no PII is used in model training. Data minimization by selecting only non-PII fields as predictors directly prevents PII from entering the training dataset at the source. This is a proactive governance practice that avoids reliance on post-processing or masking, which may still expose PII during intermediate steps.

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Enabling zero data retention in the Trust Layer

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Data anonymization via the Einstein Trust Layer

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Regularly auditing the model for bias

Question 10

When using Einstein Copilot to generate email content, what mechanism ensures that the AI does not use customer data to improve the underlying large language model?

Accepted Answer

Zero data retention. Einstein Copilot employs a zero data retention policy specifically for the underlying large language model (LLM). This means that any customer data processed during email generation is not stored, logged, or used for model training or fine-tuning, ensuring compliance with data privacy standards. The mechanism explicitly prevents the LLM from learning from or being improved by customer interactions, isolating the AI's behavior from proprietary data.

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PII masking

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Grounding

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Toxicity detection