AI Associate Practice Test 9 — 10 Questions

Question 1

A service manager wants to automatically categorize incoming support cases into appropriate Type, Priority, and Reason fields based on the case description. Which Einstein feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and populate case fields such as Type, Priority, and Reason based on the case description. It uses natural language processing (NLP) to analyze the text and map it to predefined picklist values, enabling automated categorization without manual rules.

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Einstein Prediction Builder

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Einstein Article Recommendations

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Einstein Next Best Action

Question 2

An admin wants to provide a conversational AI assistant within the CRM that can answer user questions about records, generate summaries, and create tasks. Which feature should they enable?

Accepted Answer

Einstein Copilot. Einstein Copilot is Salesforce's conversational AI assistant embedded in the CRM that can answer natural-language questions about records, generate summaries, and take actions such as creating tasks. It is purpose-built for the in-CRM conversational assistant use case described.

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Einstein GPT

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Einstein Bots

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Agentforce

Question 3

A support manager wants to automatically categorize incoming cases into Type, Priority, and Reason fields. Which Einstein feature should they enable?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically categorize incoming cases into fields like Type, Priority, and Reason using machine learning models trained on historical case data. This allows the support manager to streamline case routing and prioritization without manual input, directly matching the requirement.

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Einstein Prediction Builder

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Einstein Bots

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Einstein Article Recommendations

Question 4

A company uses Einstein Analytics to predict employee performance and identifies low-performing employees with high confidence. What is a potential ethical concern?

Accepted Answer

Invasion of employee privacy.. Einstein Analytics uses machine learning models to analyze employee data and predict performance. Identifying low-performing employees with high confidence raises ethical concerns about invasion of privacy because the model may rely on sensitive personal data (e.g., communication patterns, work hours, or behavioral metrics) without explicit employee consent or transparency. This violates principles of data minimization and informed consent, which are core to ethical AI frameworks.

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High computational cost.

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Difficulty in interpreting the model.

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Overfitting on historical data.

Question 5

A manager wants to use Einstein Forecasting to compare the AI-predicted forecast against sales reps' committed forecast amounts. Where in Salesforce can they view this comparison?

Accepted Answer

In the Einstein Forecasts list view, the AI forecast column appears next to the rep commit column.. Einstein Forecasting surfaces the AI-predicted forecast alongside the rep's committed forecast directly in the Einstein Forecasts list view, where the AI forecast column sits next to the rep commit column. This side-by-side layout lets managers compare human judgment against the model's prediction without building custom reports. It is the native, out-of-the-box location for this comparison.

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In the Einstein Lead Scoring dashboard.

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In the Einstein Discovery story for opportunities.

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Only in a custom report type built from Opportunity object.

Question 6

Refer to the exhibit. A developer configured a grounding policy for Einstein GPT. What is the effect of the fallbackBehavior set to 'USE_MODEL_KNOWLEDGE'?

Accepted Answer

The AI will rely on its pre-trained knowledge when no grounding data meets the relevance threshold. When fallbackBehavior is set to 'USE_MODEL_KNOWLEDGE', the Einstein GPT grounding policy instructs the AI to fall back to its pre-trained (model) knowledge if the retrieved grounding data does not meet the configured relevance threshold. This ensures the AI still generates a response based on its internal training rather than returning no answer or ignoring the policy entirely.

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The AI will return all retrieved grounding data in the response

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The AI will not generate a response if no grounding data is found

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The AI will ignore the grounding policy and only use model knowledge

Question 7

A Salesforce administrator deploys an Einstein Bot. Which TWO ethical considerations should be addressed? (Choose two.)

Accepted Answer

The bot should disclose it is an AI. Option A is correct because ethical AI deployment requires transparency: an Einstein Bot should clearly disclose that it is an AI/automated agent so users know they are not interacting with a human, which supports informed consent and trust. Option E is correct because ethical bot design includes a clear escalation path to a human agent when the bot cannot resolve an issue or when the user requests it, preventing frustration and ensuring accountability for sensitive or complex cases. Option B is incorrect because deliberately mimicking a human without disclosure is deceptive and conflicts with transparency principles. Option C is incorrect because fully autonomous decision-making without human oversight is risky for ethical, compliance, and customer-care reasons; bots should operate within defined guardrails. Option D is incorrect because it is overly absolute: bots may legitimately collect personal data when necessary, provided it is done lawfully, with notice/consent, and in line with privacy requirements.

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The bot should mimic a human

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The bot should make decisions autonomously

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The bot should not collect personal data

Question 8

A company is deploying an AI model that recommends next best actions for sales reps. They notice that the model's recommendations are biased towards high-revenue opportunities. Which data-related action can help reduce this bias?

Accepted Answer

Oversample the underrepresented segments in the training data. Oversampling underrepresented segments in the training data directly addresses the class imbalance that causes the model to favor high-revenue opportunities. By increasing the frequency of low-revenue examples, the model learns to treat all segments more equally, reducing bias in its recommendations. This is a standard data-level technique for mitigating bias in AI models.

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Use a larger neural network model

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Encrypt the data before training

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Remove all low-revenue opportunities from the training data

Question 9

A data scientist notices that the model accuracy drops significantly after retraining with new data. Upon inspection, they find that many records have missing values for a key feature. Which data quality improvement should be prioritized first?

Accepted Answer

Implement imputation for missing feature values.. The core issue is that missing values in a key feature introduce noise and bias, directly degrading model performance. Imputation (option A) is the most direct and impactful first step because it preserves the dataset size and feature set, allowing the model to learn from complete patterns. Without addressing missing data first, other quality improvements like normalization or feature reduction would be applied to corrupted data, failing to resolve the root cause.

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Normalize the feature range.

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Reduce the number of features.

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Remove duplicate records.

Question 10

A financial services firm uses an AI model to approve loan applications. They discover the model denies loans at a higher rate for a protected demographic. What is the most likely root cause?

Accepted Answer

The training data contains historical bias. Historical bias in training data can cause models to learn and perpetuate discrimination.

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The model is overfitted

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The model uses too few features

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The model has low precision