AI Associate Practice Test 5 — 10 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.

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Einstein Article Recommendations

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Einstein Next Best Action

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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.

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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.

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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.

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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.

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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.

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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.

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Use fewer records to avoid overfitting

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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.

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Einstein Next Best Action

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Einstein Recommendation Builder

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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.

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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