AI Associate Practice Exam 3 — 20 Questions

Question 1

A service manager wants to automatically categorize incoming cases by Type, Priority, and Reason based on the case description. Which Einstein feature should be used?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses natural language processing (NLP) to automatically analyze the text of a case description and predict values for standard fields like Type, Priority, and Reason. This directly matches the requirement to categorize incoming cases without manual effort.

Answer

Einstein Article Recommendations

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Einstein Next Best Action

Answer

Einstein Discovery

Question 2

A marketing manager wants to use AI to generate personalized email content for customers. According to Salesforce's Trusted AI principles, what should the manager ensure before sending?

Accepted Answer

A human reviews the AI-generated content before sending. Salesforce's Trusted AI principles emphasize human oversight to ensure AI-generated content is accurate, appropriate, and aligned with brand values. The manager must have a human review the content before sending to mitigate risks like bias, inaccuracy, or inappropriate messaging, which aligns with the principle of accountability in AI deployment.

Answer

The emails are sent automatically to maximize efficiency

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The emails are sent only to customers who opted in to AI interactions

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The AI model is retrained daily on new customer data

Question 3

A team has limited labeled data for a Salesforce predictive model but wants to leverage a pre-trained model from a related task. Which machine learning approach should they use?

Accepted Answer

Transfer learning. Transfer learning (option A) is correct because it reuses a pre-trained model from a related task and fine-tunes it on the small labeled dataset, which is exactly the scenario of limited labeled data in Salesforce predictive modeling. This approach reduces the amount of task-specific labeled data needed and speeds up training by leveraging learned feature representations. Unsupervised learning (B) does not use labeled data and cannot directly fine-tune a pre-trained model for a supervised prediction target. Supervised learning (C) would require training from scratch on the limited labels, which is inefficient and prone to overfitting. Reinforcement learning (D) learns via reward signals from environment interaction, not from labeled examples or pre-trained task models.

Answer

Unsupervised learning

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Supervised learning

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Reinforcement learning

Question 4

A company is implementing Einstein Activity Capture. Users have enabled the feature, but emails are not being automatically logged. Which configuration should the administrator verify first?

Accepted Answer

Confirm that the Einstein Activity Capture permission set is assigned to users.. Einstein Activity Capture requires the dedicated permission set to be assigned to users before it can automatically log emails and events. Without this permission set, the feature is enabled at the org level but users lack the necessary access rights to capture activities, so verifying this assignment is the first logical step.

Answer

Check that the users' email clients are supported.

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Verify that Email-to-Salesforce is enabled.

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Ensure users have the 'Log a Task' permission.

Question 5

A company is deploying Einstein Vision for product quality inspection. To ensure ethical use, which TWO practices should they adopt? (Choose two.)

Accepted Answer

Test the model for bias across different product types and lighting conditions. Option A is correct because testing the Einstein Vision model for bias across different product types and lighting conditions ensures the model performs equitably and accurately across the varied real-world scenarios it will encounter, which is a core ethical practice in AI deployment. Option D is correct because providing clear documentation on the model's limitations and expected accuracy promotes transparency and informed use, allowing stakeholders to understand when and how the model should be trusted. Option B is incorrect because keeping the decision-making process proprietary undermines transparency and ethical accountability, which are essential for responsible AI. Option C is incorrect because deploying the model without human oversight risks unchecked errors and ethical harm, contradicting the need for human-in-the-loop practices. Option E is incorrect because using training images from a single supplier introduces bias and reduces the model's generalizability, which is unethical and ineffective for quality inspection.

Answer

Keep the model's decision-making process proprietary to protect intellectual property

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Deploy the model without human oversight to maximize efficiency

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Only use training images from a single supplier to maintain consistency

Question 6

A company is deploying Einstein Prediction Builder to predict equipment failure. Which three considerations are essential for building an accurate prediction model? (Choose 3)

Accepted Answer

Missing values in features should be handled appropriately.. Missing values in features can introduce bias or cause errors in the predictive model. Einstein Prediction Builder automatically handles missing data through imputation, but understanding how missing values are treated is essential for model accuracy, as inappropriate handling can distort relationships between features and the target outcome.

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All input features must be numerical.

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The model should be retrained only once after initial deployment.

Question 7

An administrator is configuring Einstein Bots and needs the bot to understand when a customer says 'I want to return a product' and route them accordingly. What must be created to map this phrase to a specific bot action?

Accepted Answer

An intent named 'ReturnProduct' and associated training phrases. Intents represent the purpose of a user input (e.g., 'ReturnProduct'), and entities capture specifics like product name. The bot uses NLP to match intents.

Answer

A new object to store return requests

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A dialogue flow with condition nodes

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A custom entity for 'return'

Question 8

An admin is training an Einstein Prediction Builder model for binary classification (lead conversion). The model performance is poor. Which THREE actions should the admin take to improve it?

Accepted Answer

Increase the number of records in the training dataset. Option A is correct because Einstein Prediction Builder learns from historical data, so increasing the number of records in the training dataset gives the model more examples to detect meaningful patterns and improves accuracy, provided the data is relevant and clean. Option C is correct because removing features with little correlation to conversion reduces noise and irrelevant inputs, which helps the model focus on the variables that actually influence the predicted outcome. Option E is correct because the prediction field (e.g., converted) must be well-represented in the data; if one class is severely imbalanced or the field has many blanks, the model cannot learn the binary classification reliably. Option B is not correct because using fewer records generally reduces the model's ability to learn and can worsen performance rather than prevent overfitting. Option D is not correct because adding unrelated features introduces noise and can degrade model quality instead of improving it.

Answer

Use fewer records to avoid overfitting

Answer

Add more features, even if they are not related

Question 9

A nonprofit organization wants to automatically recommend relevant knowledge articles to service agents while they are working on a case. Which feature should they enable?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it specifically uses AI to analyze the context of a service case (such as subject, description, and product) and automatically surfaces relevant knowledge articles to the agent without requiring manual search. This directly matches the requirement of recommending articles while the agent works on a case.

Answer

Einstein Next Best Action

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Einstein Recommendation Builder

Answer

Einstein Case Classification

Question 10

Which TWO actions align with ethical AI practices in Salesforce?

Accepted Answer

Monitoring models for bias. Option C is correct because ethical AI in Salesforce requires ongoing bias detection and mitigation—Salesforce's Einstein Trust Layer and Model Cards emphasize regularly monitoring models for disparate impact across protected groups so that biased outcomes are caught and corrected. Option E is correct because providing explanations for AI predictions supports transparency and accountability, aligning with Salesforce's responsible AI principles and emerging regulations (e.g., GDPR Article 22) that give users the right to understand automated decisions. Option A is not inherently ethical—restricting training to internal data can still violate privacy or bias concerns, and ethical AI does not mandate internal-only data. Option B is wrong because fully automating decisions without human review removes the human oversight that ethical AI frameworks require for high-impact choices. Option D is wrong because using customer data without explicit consent violates privacy and consent principles central to ethical AI and Salesforce's data-use policies.

Answer

Using only internal data for training

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Automating all decisions without human review

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Using customer data without explicit consent

Question 11

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.

Answer

Einstein Article Recommendations

Answer

Einstein Prediction Builder

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Einstein Bots

Question 12

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.

Answer

Einstein Copilot

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Einstein Next Best Action

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Einstein Service GPT

Question 13

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.

Answer

Einstein Case Classification

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Einstein Discovery

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Einstein Next Best Action

Question 14

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.

Answer

Einstein Prediction Builder

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Einstein Recommendation Builder

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Einstein Discovery

Question 15

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.

Answer

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 16

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

Answer

Configure a trigger to retrain the model daily

Question 17

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.

Answer

Einstein Forecasting

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Einstein Activity Capture

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Einstein Lead Scoring

Question 18

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.

Answer

Disable the AI model and make all decisions manually

Answer

Remove demographic data from the model entirely and continue using it

Answer

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

Question 19

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.

Answer

Enabling zero data retention in the Trust Layer

Answer

Data anonymization via the Einstein Trust Layer

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Regularly auditing the model for bias

Question 20

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.

Answer

PII masking

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Grounding

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Toxicity detection