AI Associate Practice Test 30 — 15 Questions

Question 1

A nonprofit uses an AI system to allocate resources to communities in need. The system uses historical data which shows that certain neighborhoods have lower service usage. What ethical risk should be considered?

Accepted Answer

The system may perpetuate historical inequities. Option D is correct because the AI system uses historical data that reflects lower service usage in certain neighborhoods. If that historical data is biased due to past inequities (e.g., redlining, underinvestment, or systemic discrimination), the model will learn and amplify those patterns, leading to unfair resource allocation that perpetuates historical disadvantages. This is a classic case of algorithmic bias where the training data encodes societal biases, and the model's predictions reinforce them.

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The system may violate data minimization principles

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The system cannot be held accountable for decisions

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The system lacks explainability

Question 2

A large enterprise is using Einstein Lead Scoring and notices that the model score is not updating for leads created via a web-to-lead form. The leads have all required fields populated. The admin has verified that the model is active and the data source includes the Lead object. What could be causing the score to remain static?

Accepted Answer

The model has not scored enough leads to start scoring new ones. Option D is correct because Einstein Lead Scoring requires a minimum number of scored leads (typically 500) before it begins scoring new leads. Until that threshold is met, the model remains in a 'training' or 'pending' state and will not update scores for any leads, including those from web-to-lead forms. The admin has confirmed the model is active and the Lead object is included, so the most likely cause is that the model has not yet processed enough leads to start scoring.

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The data source excludes leads created by web-to-lead

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Web-to-lead leads are not supported by Einstein AI

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The model is not yet activated

Question 3

Refer to the exhibit. The fairness evaluation shows a disparate impact of 0.85, equal opportunity difference of 0.12, and demographic parity difference of 0.18. Which fairness thresholds are violated?

Accepted Answer

Equal opportunity and demographic parity.. The correct answer is C because the fairness evaluation shows violations of both equal opportunity and demographic parity thresholds. The equal opportunity difference of 0.12 exceeds the commonly accepted threshold of 0.1, and the demographic parity difference of 0.18 exceeds the typical threshold of 0.1. Disparate impact of 0.85 is within the acceptable range (typically 0.8 to 1.25), so it is not violated.

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Demographic parity only.

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Equal opportunity only.

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Disparate impact only.

Question 4

A company wants to use its data from Salesforce to train an Einstein AI model. However, they need to exclude records where the customer has opted out of data use. Which field should they configure in the Data Manager?

Accepted Answer

A checkbox field named Data Use Opt-Out. Option A is correct because the Data Manager in Einstein AI uses a standard checkbox field named 'Data Use Opt-Out' to identify records that should be excluded from model training. When this checkbox is true, the system automatically filters out those records during data preparation, ensuring compliance with data privacy requirements.

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The Record Type of the object

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A picklist field on the object

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A formula field that evaluates to true

Question 5

A healthcare provider uses an AI model to predict patient readmission risk. The model is trained on historical data that underrepresents minority populations. What is the MOST significant ethical risk?

Accepted Answer

The model may produce biased predictions against minorities. Option C is correct because training on historical data that underrepresents minority populations leads to a model that has insufficient examples to learn patterns for those groups, resulting in biased predictions that systematically disadvantage minorities. This is a direct violation of fairness in AI ethics, as the model's outputs will be less accurate or equitable for underrepresented groups, potentially causing harm in critical healthcare decisions like readmission risk assessment.

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The model may overfit to the majority population

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The model cannot scale to real-time predictions

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The model lacks explainability

Question 6

A company uses Salesforce and wants to provide automated chat responses for common customer inquiries. Which Einstein feature should be configured?

Accepted Answer

Einstein Bots. Einstein Bots is the correct feature because it enables automated chat responses for common customer inquiries using natural language processing (NLP) and predefined dialog flows. It integrates directly with Salesforce Chat to handle routine questions without human intervention, reducing response times and agent workload.

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Einstein Sentiment

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Einstein Case Routing

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Einstein Recommender

Question 7

A Salesforce admin wants to use Einstein GPT to generate personalized email content for a marketing campaign. To ensure the AI does not produce responses that include sensitive customer data or violate company policies, which Salesforce feature should the admin configure?

Accepted Answer

Einstein Trust Layer. Einstein Trust Layer is the correct feature because it acts as a governance and security layer between Salesforce and the large language model (LLM). It automatically masks sensitive customer data (e.g., personally identifiable information) before the prompt is sent to the LLM and then unmasks the response, ensuring the AI never sees or exposes sensitive information. This directly addresses the admin's need to prevent responses containing sensitive data or violating company policies.

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Prompt Builder

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Data Cloud

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Einstein Studio

Question 8

A service manager wants to automatically categorize incoming support cases based on the customer's description. Which Einstein feature should be used?

Accepted Answer

Einstein Case Classification. Einstein Case Classification uses machine learning to automatically categorize incoming support cases based on the customer's description, assigning them to predefined case fields (e.g., type, priority, product). This directly matches the requirement to automatically categorize cases from text, making it the correct choice.

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Einstein Reply Recommendations

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Einstein Bots

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Einstein Article Recommendations

Question 9

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

Accepted Answer

Users must have the Einstein Relationship Intelligence permission set.. Option C is correct because the Einstein Relationship Intelligence permission set grants users access to the feature's insights and data processing capabilities. Without this permission set, users cannot view or interact with Einstein Relationship Intelligence data, even if the feature is enabled at the org level.

Answer

Exchange or Gmail must be used for email.

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The feature is only available in Unlimited Edition.

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All users must opt in to email logging.

Question 10

Which TWO features are part of Einstein AI capabilities in Salesforce Sales Cloud?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is a core Einstein AI capability in Sales Cloud that uses predictive models to analyze historical data and assign a score to each opportunity, indicating its likelihood to close. This helps sales reps prioritize their efforts on deals most likely to convert, directly leveraging AI to enhance sales productivity.

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Einstein Case Classification

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Einstein Bots

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Einstein Article Recommendations

Question 11

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

Accepted Answer

It predicts a binary outcome (e.g., convert or not). Option D is correct because Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

Answer

It predicts a numeric value

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It predicts the value of a text field

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It is currently retraining the model

Question 12

A marketing manager wants to use Einstein Send Time Optimization. To generate personalized send time recommendations, which data does the model primarily rely on?

Accepted Answer

The individual contact's past email open and click behavior.. Einstein Send Time Optimization (STO) uses a machine learning model that analyzes each individual contact's historical email engagement patterns—specifically their past open and click behavior—to predict the optimal send time unique to that contact. This personalized approach ensures that each recipient receives the email when they are most likely to engage, rather than relying on aggregate or rule-based heuristics.

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The aggregated engagement data of all contacts in the same time zone.

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The industry benchmarks for optimal send times.

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The sender's historical campaign performance by hour.

Question 13

Refer to the exhibit. A Salesforce admin is troubleshooting email capture failures. Based on the log, which configuration step is most likely missing?

Accepted Answer

The email domain 'acme.org' needs to be added to the Einstein Activity Capture allowed domains.. The log indicates that emails from 'acme.org' are being captured but not linked to contacts, which is a classic symptom of a missing domain in the Einstein Activity Capture allowed domains list. Without adding the domain, the system cannot associate emails from that domain with the correct contact records, even if the email addresses are valid. Option B directly addresses this configuration gap.

Answer

The connected email service is not authenticated for the organization.

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The contact with ID 003B0000007v4ZW does not have a valid email address.

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The user 'admin@mycompany.com' has exceeded their email capture quota.

Question 14

A marketing team wants to predict which segment of customers will likely purchase a new product. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Segment Prediction. Einstein Segment Prediction uses predictive modeling and machine learning to analyze historical customer data and identify which segments are most likely to purchase a new product. It is specifically designed for predictive segmentation based on behavioral and demographic patterns, making it the most appropriate choice for this use case.

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Custom Reports

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List Views

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Campaigns

Question 15

A company wants to use Einstein Prediction Builder to predict customer churn. They have a dataset with 10,000 records and 50 features. What is the primary consideration for model accuracy?

Accepted Answer

The dataset must be balanced between churned and non-churned customers.. Option C is correct because Einstein Prediction Builder uses automated machine learning (AutoML) to train models, and class imbalance is a critical factor that directly impacts model accuracy. If the dataset is highly skewed (e.g., 95% non-churned, 5% churned), the model may achieve high accuracy by simply predicting the majority class, but it will fail to identify actual churners. Einstein Prediction Builder includes built-in handling for imbalanced data, but the user must ensure the dataset is reasonably balanced or use techniques like oversampling to improve predictive performance.

Answer

The dataset size is too small for reliable predictions.

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All features must be numerical and normalized.

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The model needs to be retrained daily.