AI Associate Practice Test 46 — 15 Questions

Question 1

Which TWO actions can be performed using Einstein Activity Capture?

Accepted Answer

Automatically log emails from Outlook or Gmail. Einstein Activity Capture is designed to automatically log user activities such as emails and meetings from connected email and calendar systems (Outlook, Gmail, Exchange) into Salesforce records. Option A is correct because the feature captures emails sent or received from these providers and logs them as EmailMessage records against the relevant contacts, leads, and opportunities without manual user intervention.

Answer

Update opportunity amounts based on email content

Answer

Create tasks from email attachments

Answer

Generate leads from email signatures

Question 2

A company deploys an AI system that makes decisions about loan approvals. For transparency, what should they provide to applicants?

Accepted Answer

Explanation of factors considered. Transparency in AI-driven loan approvals requires providing applicants with an explanation of the factors considered in the decision, such as credit score, income, or debt-to-income ratio. This aligns with ethical AI principles like explainability and fairness, enabling applicants to understand and potentially contest the decision. Providing the full algorithm or training data would expose proprietary information and potentially violate data privacy regulations like GDPR.

Answer

The training data

Answer

The full algorithm

Answer

Confidence scores

Question 3

A financial services company deploys an AI system to approve small business loans. The system uses a deep neural network trained on historical loan data. After deployment, an internal audit reveals that the approval rate for minority-owned businesses is 15% lower than for non-minority-owned businesses with similar financial profiles. The company's AI Ethics policy requires that AI systems be fair and transparent. The data science team has access to the training data, model architecture, and feature importance scores. The company wants to understand why the disparity exists and take corrective action. Which approach should the team take first?

Accepted Answer

Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk.. The first step in diagnosing an AI fairness issue is to audit the training data for biases such as sampling bias (e.g., underrepresentation of minority-owned businesses) or labeling bias (e.g., historical loan officers unfairly labeling minority applicants as higher risk). Since the team has access to the training data, analyzing it directly addresses the root cause of the disparity before making model-level changes. This aligns with the AI Ethics policy requirement for transparency, as data bias is a common source of unfair outcomes in deep neural networks trained on historical data.

Answer

Apply a disparate impact analysis to quantify the adverse impact and then adjust the decision threshold.

Answer

Examine the model's weights and activations to identify which features contribute to the disparity.

Answer

Retrain the model with a fairness constraint that penalizes disparities in approval rates.

Question 4

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

Answer

Einstein Discovery

Answer

Einstein Next Best Action

Question 5

A developer is creating a prompt template for Einstein GPT to summarize customer case details. The prompt must include the case subject, description, and last 3 comments, but only when the case priority is High. Which approach best achieves this in Prompt Builder?

Accepted Answer

Use merge fields with a conditional IF statement to include fields only when priority is High. Prompt Builder supports conditional merge fields using IF statements, allowing dynamic inclusion of case subject, description, and last 3 comments only when the case priority is High. This approach ensures the prompt is concise and relevant, avoiding unnecessary tokens or manual edits.

Answer

Hardcode example cases in the prompt and rely on the model to generalize

Answer

Include all fields in the prompt and use a token limiter to truncate the response

Answer

Create a static prompt and manually edit it for each High-priority case

Question 6

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

Answer

Einstein Next Best Action

Answer

Einstein Service GPT

Question 7

An admin wants to create a prompt template that generates a personalized sales email for a lead using fields like Company, Industry, and Lead Source. The email should be generated when a user clicks a button on the lead record. Which Prompt Builder template type is appropriate?

Accepted Answer

Sales Email. The Sales Email template type in Prompt Builder is specifically designed to generate personalized sales emails using standard fields like Company, Industry, and Lead Source, and it triggers on a button click on the lead record. This template type includes built-in placeholders and formatting optimized for email generation, making it the correct choice for this use case.

Answer

Flex Prompt

Answer

Case Field Generation

Answer

Field Generation

Question 8

A sales operations manager wants to compare the AI-predicted forecast against the sales rep's manual commit for the current quarter. Which Einstein feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the specific feature designed to compare AI-predicted forecasts against sales reps' manual commitments for the current quarter. It provides a side-by-side view of the AI-generated forecast and the human-entered commit, enabling managers to identify discrepancies and adjust strategies accordingly.

Answer

Einstein Opportunity Scoring

Answer

Einstein Lead Scoring

Answer

Einstein Prediction Builder

Question 9

Which Einstein feature records call recordings and provides analysis on keywords, talk-time metrics, and next steps?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it is specifically designed to record and analyze sales calls, providing transcriptions, keyword spotting, talk-time metrics, and automated next steps. It uses natural language processing (NLP) to surface insights from conversational data, directly matching the question's requirements.

Answer

Einstein Email Insights

Answer

Einstein Activity Capture

Answer

Einstein Discovery

Question 10

A company wants to use Einstein Prediction Builder to predict which customers are likely to churn. They have a dataset that includes customers' names, email addresses, and detailed purchase history. According to the data minimisation principle, which fields should be included in the model?

Accepted Answer

Only the purchase history. Data minimisation means using only the data necessary for the prediction. Purchase history is relevant to churn prediction, while names and email addresses are personally identifiable information (PII) that are not needed for the model and should be excluded to minimize privacy risks.

Answer

Only the email addresses, since they are unique identifiers

Answer

All three fields because more data improves accuracy

Answer

Names and purchase history, but not email addresses

Question 11

A company is preparing data for Einstein Prediction Builder to forecast lead conversion. They have historical data with fields like Lead Source, Industry, Number of Employees, and Converted (boolean). Which data preparation step is most critical?

Accepted Answer

Ensure data completeness by handling missing values in Lead Source. Handling missing values in Lead Source is critical because Einstein Prediction Builder requires complete, high-quality data to train accurate predictive models. Missing categorical fields like Lead Source can introduce bias or cause the model to ignore important patterns in lead conversion. Ensuring data completeness through imputation or removal of incomplete records is a standard data preparation step for AI/ML in Salesforce.

Answer

Mix data from all lead sources without normalization

Answer

Use only the last 3 months of data for training

Answer

Remove all records with outliers in Number of Employees

Question 12

A company is configuring Einstein Lead Scoring. Which TWO statements accurately describe how the feature works?

Accepted Answer

It assigns a score between 1 and 99 indicating conversion likelihood.. Einstein Lead Scoring uses a predictive model to assign a score between 1 and 99 that reflects the likelihood a lead will convert to an opportunity. The score is calculated automatically based on historical conversion patterns and lead attributes, without requiring manual rule definition.

Answer

It only works with leads imported from external systems.

Answer

It scores opportunities based on deal size.

Answer

It requires manual configuration of scoring rules by an admin.

Question 13

Refer to the exhibit. A Salesforce developer configures the Einstein Trust Layer as shown. What is the primary purpose of enabling data masking?

Accepted Answer

To anonymize personally identifiable information (PII) in the model output.. Enabling data masking in the Einstein Trust Layer ensures that personally identifiable information (PII) is anonymized before the model output is returned to the user. This protects sensitive data from exposure in AI-generated responses, which is a core requirement for privacy compliance and responsible AI use.

Answer

To improve the accuracy of sentiment analysis.

Answer

To reduce latency of the AI response.

Answer

To comply with Salesforce's service-level agreement.

Question 14

A Service Cloud admin wants to deploy a chatbot that can handle common customer requests and escalate to a human agent when necessary. The chatbot must understand natural language variations. Which combination of tools should they use?

Accepted Answer

Einstein Bots with intents and entities, and handoff to agent. Einstein Bots in Service Cloud can be configured with intents and entities for NLP understanding and can hand off to human agents when needed.

Answer

Einstein Case Classification with auto-response

Answer

Einstein Copilot with custom actions

Answer

Einstein Next Best Action with flows

Question 15

A financial institution deploys an AI system to recommend investment portfolios to retail clients. The system uses reinforcement learning to maximize returns based on client risk profiles. After six months, an internal audit reveals that the system has been consistently recommending high-risk, high-commission products to elderly clients with low risk tolerance, resulting in significant financial losses for those clients. The system's training data included historical transactions, which showed that elderly clients were less likely to complain or switch advisors. The institution's AI ethics policy mandates fairness, transparency, and accountability. The system currently provides no explanations for its recommendations, and there is no human oversight process. The compliance team needs to remediate the situation. Which course of action BEST addresses the ethical violations?

Accepted Answer

Retrain the model on a balanced dataset, implement explainability features, and require human approval for high-risk recommendations to elderly clients.. It directly addresses the root cause of the ethical violations: biased training data (historical transactions where elderly clients were less likely to complain) and lack of transparency. Retraining on a balanced dataset mitigates the reinforcement learning model's exploitation of that bias, while explainability features (e.g., SHAP values or LIME) and human-in-the-loop approval for high-risk recommendations ensure accountability and fairness as mandated by the AI ethics policy.

Answer

Disable the AI system and revert to manual portfolio management.

Answer

Add a disclaimer to all recommendations stating that past performance does not guarantee future results.

Answer

Adjust the model to lower the risk threshold for all clients.