AI Associate Practice Test 29 — 15 Questions

Question 1

Which TWO techniques are commonly used to handle missing values in a dataset for AI training?

Accepted Answer

Deletion of rows with missing values. Option B (Deletion of rows with missing values) is correct because listwise deletion removes records containing nulls, a standard and simple preprocessing technique when missingness is random and the affected rows are few. Option E (Imputation with mean or median) is correct because it fills missing numeric entries with the column's central tendency, preserving sample size and avoiding data loss during AI training. Option A (L1 regularization) is incorrect because it is a model penalty that drives weights toward zero to reduce overfitting, not a missing-value treatment. Option C (One-hot encoding) is incorrect because it converts categorical variables into binary vectors and does not address nulls. Option D (Min-max normalization) is incorrect because it rescales numeric features to a fixed range and cannot handle missing entries.

Answer

L1 regularization

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One-hot encoding

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Min-max normalization

Question 2

A financial institution uses an AI system to approve loan applications. The system denies loans to applicants from certain postal codes at a higher rate. The model includes 'postal code' as a feature. Which ethical consideration is most directly violated?

Accepted Answer

Fairness. The AI system's use of 'postal code' as a feature leads to disparate impact on applicants from certain areas, directly violating the ethical principle of fairness. Fairness requires that AI models do not discriminate against protected groups or perpetuate systemic biases, even if the feature itself is not a protected attribute. By denying loans at a higher rate based on postal code, the system is likely engaging in proxy discrimination, which is a core fairness violation.

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Privacy

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Accountability

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Transparency

Question 3

A company is deploying an AI model to automatically approve or reject small loan applications. To comply with the right to explanation under GDPR, what capability must the system provide?

Accepted Answer

The ability for the applicant to chat with a human agent and receive the specific reasons for the decision.. GDPR's right to explanation (Articles 13-15 and 22) requires that individuals subject to automated decision-making receive meaningful information about the logic involved and the specific reasons for the decision. Providing a human agent who can explain the specific reasons for the loan decision satisfies this requirement. This is the only option that delivers individualized, understandable reasoning.

Answer

A downloadable PDF report of the model's accuracy metrics.

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A live video feed of the data center where the model runs.

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A promise that the model was certified by an external auditor.

Question 4

A retail company implements an AI chatbot to recommend products. After launch, they notice the chatbot frequently suggests expensive items to budget-conscious customers. Which AI bias is most likely occurring?

Accepted Answer

Sample bias (biased training data). The chatbot's frequent suggestion of expensive items to budget-conscious customers indicates that the training data was biased toward high-cost products, leading the model to learn and replicate that preference. This is a classic case of sample bias (biased training data), where the dataset does not accurately represent the target user population, causing systematic errors in the model's recommendations.

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Confirmation bias

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Anchoring bias

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Overconfidence bias

Question 5

A marketing manager wants to prioritize leads with the highest likelihood of conversion. Which Einstein feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the Salesforce AI feature that analyzes historical lead data (converted vs. non-converted leads) and assigns each lead a score from 1-100 indicating likelihood of conversion. It surfaces this score directly on the lead record and in list views, allowing the marketing manager to prioritize outreach to the highest-scoring leads. This is precisely the use case described — ranking leads by conversion probability.

Answer

Approval Processes

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Custom Formula Fields

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Data Export

Question 6

A company wants to create an autonomous AI agent that can handle complex customer service tasks like processing returns and updating orders without human intervention. Which Salesforce feature should they use?

Accepted Answer

Agentforce. Agentforce is the correct choice because it is designed to create autonomous AI agents that can handle complex, multi-step customer service tasks like processing returns and updating orders without human intervention. Unlike simpler chatbots, Agentforce uses advanced reasoning and action execution to complete end-to-end workflows independently.

Answer

Einstein Bots

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Einstein Copilot

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Einstein Next Best Action

Question 7

A sales operations manager wants to automatically prioritize leads based on their likelihood to convert. The team uses Sales Cloud and wants to avoid custom development. Which feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it is specifically designed to automatically prioritize leads based on their likelihood to convert, using historical data and predictive models. It is a native Salesforce Sales Cloud feature that requires no custom development, directly addressing the manager's need to rank leads by conversion probability.

Answer

Einstein Opportunity Scoring

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Einstein Prediction Builder

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Einstein Next Best Action

Question 8

A financial services firm wants to deploy an autonomous AI agent that can handle complex loan application processes, including verifying documents, checking credit scores, and requesting additional information. The agent must be able to take actions in Salesforce and escalate to a human when needed. Which Salesforce tool should they use?

Accepted Answer

Agentforce. Agentforce is the correct choice because it is Salesforce's platform for building autonomous AI agents that can execute complex, multi-step workflows across systems like Salesforce, including document verification, credit score checks, and data retrieval. It supports tool integration (e.g., Apex, MuleSoft) and built-in escalation to human agents, making it ideal for handling end-to-end loan application processes with minimal human intervention.

Answer

Einstein Next Best Action

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Einstein Bots

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Einstein Copilot

Question 9

An admin is configuring an Einstein Bot in Service Cloud. The bot needs to understand when a customer says 'I want to return a product' and route them to a return flow, but the bot is not recognizing phrases like 'return' or 'refund'. What should the admin do first?

Accepted Answer

Create a new intent named 'Return' and add training phrases like 'return item', 'refund'. Einstein Bots rely on Natural Language Understanding (NLU) to map user utterances to intents. By creating a new 'Return' intent and adding training phrases like 'return item' and 'refund', the admin provides the bot with the necessary examples to recognize and route these customer requests to the appropriate return flow.

Answer

Use Einstein Case Classification to classify the case

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Disable the bot and use a flow instead

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Add a handoff to human agent for all unrecognized phrases

Question 10

A data scientist is evaluating Salesforce's Einstein features for predictive analytics. Which three statements accurately describe Einstein Discovery? (Select three answers.)

Accepted Answer

It can generate natural language explanations of model insights.. Einstein Discovery includes a natural language generation (NLG) engine that automatically produces plain-English explanations of model insights, such as key drivers and predictions. This allows non-technical users to understand the output without needing to interpret raw statistical data.

Answer

It is limited to 1000 rows of data per prediction.

Answer

It requires a separate data preparation tool before use.

Question 11

A sales rep wants to generate personalized email drafts for leads using AI. Which feature should the admin enable?

Accepted Answer

Einstein GPT. Einstein GPT is the correct feature because it is Salesforce's native generative AI tool that can automatically create personalized email drafts for leads by leveraging CRM data and natural language processing. Unlike other options, Einstein GPT is specifically designed for AI-driven content generation within the Salesforce ecosystem.

Answer

Workflow Rules

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Process Builder

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Email Templates

Question 12

A sales operations analyst wants to understand why an opportunity's win likelihood score changed after a recent update. Where can they find the factors that influenced the score in Lightning?

Accepted Answer

On the opportunity record page, in the Einstein Scoring component. The Einstein Scoring component on the opportunity record page displays the key factors that influenced the win likelihood score. This component provides a breakdown of the positive and negative factors, such as changes in lead source or engagement, that caused the score to change after a recent update. It is the direct, in-context location for understanding score drivers in Lightning.

Answer

In a custom report that includes the opportunity score field

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In the Einstein Lead Scoring section of Setup

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In Einstein Discovery, by running a story on opportunity data

Question 13

A Salesforce customer uses Einstein Sentiment Analysis to analyze customer feedback. They find the model is less accurate for non-English languages. What ethical concern does this raise?

Accepted Answer

Bias. When a sentiment model performs worse for non-English languages, it produces systematically less accurate results for those users, which is a form of bias (specifically, language or cultural bias). Bias in AI refers to systematic and unfair performance differences across groups, which is exactly what is described. Accountability, privacy, and security concern responsibility, data handling, and protection, none of which are the core issue here.

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Accountability

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Privacy

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Security

Question 14

A company wants to create a custom AI model that predicts whether a support case will be escalated (Yes/No) based on historical case data. They need to define the objective field, training data, and features. Which Einstein feature allows them to build this custom prediction?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is Salesforce's point-and-click tool for creating custom binary or numeric predictions on any standard or custom object using historical CRM data. It lets admins define the objective field (e.g., 'Escalated__c'), select the training dataset, and choose features without writing code — exactly matching the scenario.

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Einstein Case Classification

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Einstein Next Best Action

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Einstein Discovery

Question 15

In Einstein Discovery, which visualization best explains the key drivers behind a business outcome, such as why a metric increased or decreased?

Accepted Answer

Waterfall chart. In Einstein Discovery, the Waterfall chart is specifically designed to decompose the net change in a metric (e.g., increase or decrease) into its contributing factors, showing how individual drivers add or subtract from the total. This makes it the ideal visualization for explaining key drivers behind a business outcome, as it visually breaks down the additive contributions of each predictor variable. Scatter plots, bar charts, and pie charts do not provide this sequential decomposition of change.

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Scatter plot

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Bar chart

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Pie chart