AI Associate Practice Test 49 — 15 Questions

Question 1

A developer wants to build a custom app that classifies images of products into categories (e.g., shoes, bags). The app must run in Salesforce and use Einstein AI. Which approach is MOST appropriate?

Accepted Answer

Use Einstein Vision and Language Platform APIs. Einstein Vision and Language Platform APIs provide the image classification capabilities needed for this custom app. These APIs allow developers to train and deploy custom image classification models that can categorize products like shoes and bags directly within Salesforce.

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Use Einstein Recommendation Builder to classify products

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Use Einstein GPT with a prompt that describes the image

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Use Einstein Copilot to process images

Question 2

A company wants to improve sales forecast accuracy by incorporating AI predictions that compare rep commits with statistical forecasts. Which Einstein feature should they use?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it directly combines statistical forecasts with sales rep commit data to produce a more accurate, AI-driven prediction. This feature specifically addresses the need to reconcile top-down statistical models with bottom-up rep inputs, which is exactly what the question describes.

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Einstein Discovery

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Einstein Lead Scoring

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Einstein Opportunity Scoring

Question 3

A team is building a pipeline to train a model daily. The source data arrives in CSV files but needs to be converted to Parquet for efficiency. Which pipeline step should perform this conversion?

Accepted Answer

Data ingestion step. The data ingestion step is responsible for bringing raw data into the pipeline, including format conversions like CSV to Parquet. Converting to Parquet at ingestion improves storage efficiency and query performance for downstream processing, as Parquet uses columnar storage and compression.

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Feature engineering step

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Model deployment step

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Data validation step

Question 4

A sales manager wants to automatically capture emails and events from a sales rep's Outlook calendar and inbox into Salesforce without manual effort. Which feature should be enabled?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture is the correct feature because it automatically syncs emails and events from Microsoft 365 or Google Workspace into Salesforce without requiring manual logging. It uses a background sync engine to capture activities from connected calendars and inboxes, eliminating the need for users to manually log interactions.

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Einstein Bots

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Einstein Lead Scoring

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Einstein Discovery

Question 5

A company is deploying an Einstein chatbot to handle customer support inquiries. They want to ensure compliance with data privacy regulations and ethical AI principles. Which TWO actions should they take?

Accepted Answer

Enable PII masking in the Einstein Trust Layer. Option B is correct because enabling PII masking in the Einstein Trust Layer ensures that personally identifiable information such as names, emails, and phone numbers is detected and masked before data reaches the LLM, satisfying data privacy regulations like GDPR and CCPA. Option C is correct because configuring the chatbot to require human approval for responses involving sensitive topics enforces a human-in-the-loop control, which aligns with ethical AI principles such as accountability, transparency, and harm avoidance. Option A is incorrect because disabling audit trails reduces accountability and violates compliance requirements that mandate traceability of data access and AI decisions. Option D is incorrect because storing all customer chat logs indefinitely violates data minimization and retention limits under privacy regulations. Option E is incorrect because unrestricted use of all customer data breaches consent, purpose limitation, and data protection principles.

Answer

Disable audit trails to reduce data storage

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Store all customer chat logs indefinitely for model retraining

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Allow the chatbot to use all available customer data without restrictions

Question 6

A sales manager wants to use Einstein Opportunity Scoring to improve forecasting. Which TWO statements are true about Einstein Opportunity Scoring?

Accepted Answer

Score factors are displayed in the Lightning opportunity record.. Option C is correct because Einstein Opportunity Scoring surfaces the score factors (the key fields and their relative influence on the prediction) directly on the Lightning opportunity record page, giving reps insight into why an opportunity scores as it does. Option D is correct because the model outputs a win-probability score on a 1–99 scale, where higher numbers indicate greater likelihood of closing. Option A is wrong because scoring is predictive and does not automatically advance or change the opportunity stage. Option B is wrong because the score is not compared against the rep's commit amount; it reflects win likelihood, not forecast commit reconciliation. Option E is wrong because Einstein Opportunity Scoring uses a prebuilt, Salesforce-maintained model and does not require the admin to build a custom prediction model.

Answer

It can be used to automatically update opportunity stage.

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It compares the AI-predicted score to the rep's commit amount.

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It requires the admin to build a custom prediction model.

Question 7

When a Salesforce admin enables 'Score Factors' for an AI prediction, what does this provide to end users?

Accepted Answer

A list of the most influential fields and their contribution to the prediction. When 'Score Factors' is enabled for an AI prediction in Salesforce, end users see a breakdown of the most influential fields that contributed to the prediction, along with their relative contribution (e.g., positive or negative impact). This provides transparency into why a specific prediction was made, helping users trust and act on the AI's output without exposing the underlying model logic.

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The exact formula used by the model

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A confidence interval for the prediction

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A histogram of all prediction values

Question 8

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 9

A Salesforce admin wants to automatically classify incoming service cases by Priority (High, Medium, Low) based on case fields like Subject, Description, and Account Type. 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 classify incoming service cases based on fields like Subject, Description, and Account Type. It uses natural language processing (NLP) and machine learning models trained on historical case data to predict the Priority (High, Medium, Low) without requiring custom code or manual rules.

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Einstein Discovery

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Einstein Prediction Builder

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Einstein Bots

Question 10

A company uses generative AI to create personalized email content for each customer. They notice that occasionally the AI produces content that is factually incorrect. What is this phenomenon called?

Accepted Answer

Hallucination. Hallucination refers to the phenomenon where a generative AI model produces content that is factually incorrect, nonsensical, or not grounded in its training data. In the context of personalized email generation, the AI may fabricate details such as product features, prices, or customer information. This occurs because large language models predict the next word based on patterns, without an inherent understanding of truth. Thus, 'hallucination' is the correct term for factually incorrect AI-generated content.

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Overfitting

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Concept drift

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Bias

Question 11

A company wants to use Einstein Conversation Insights to analyze sales call recordings. Which TWO capabilities does this feature provide?

Accepted Answer

Keyword tracking and trend analysis. Einstein Conversation Insights is designed to mine sales call recordings for coaching and deal insights, and it provides keyword tracking and trend analysis (D) so managers can see which terms, competitors, or topics appear across calls and how they trend over time. It also surfaces talk time and speed metrics (E), such as rep-versus-customer talk ratios and speaking pace, to help evaluate conversation quality and coaching opportunities. The feature works on recorded calls rather than live audio, so real-time transcription (B) is not one of its capabilities. Automatic email logging (A) and sentiment analysis of customer emails (C) belong to email-oriented Einstein features like Einstein Activity Capture or Email Insights, not Conversation Insights.

Answer

Automatic email logging

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Real-time transcription of calls

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Sentiment analysis of customer emails

Question 12

An administrator is configuring a Salesforce AI model that uses historical sales data. The data includes fields like 'Amount', 'Close_Date', and 'Lead_Source'. What is the primary purpose of data preprocessing in this context?

Accepted Answer

To clean and transform data into a format suitable for model training. Data preprocessing is essential for AI models because raw historical sales data often contains missing values, inconsistent formats, and noise. Cleaning (e.g., handling nulls in 'Amount') and transforming (e.g., encoding 'Lead_Source' into numerical features) ensure the model can learn patterns effectively, directly impacting training accuracy and convergence.

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To generate visualizations for business stakeholders

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To increase the storage capacity of the database

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To enforce data access permissions for different user roles

Question 13

A Salesforce admin wants to use Einstein Prediction Builder to predict case resolution time. What type of data is most critical for training this model?

Accepted Answer

Historical case records including resolution time. Einstein Prediction Builder requires historical data with known outcomes to train a supervised machine learning model. Historical case records containing actual resolution times provide the labeled examples needed for the model to learn patterns and predict future case resolution times. Without this ground truth data, the model cannot be trained to make accurate predictions.

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Customer satisfaction survey responses

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Product inventory levels

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Employee work schedules

Question 14

A company uses Einstein Bots to handle basic customer inquiries. When a customer asks a question that the bot cannot answer, the bot should transfer the conversation to a human agent. Which bot configuration is necessary?

Accepted Answer

Add a handoff node in the bot dialogue flow. A handoff node is the specific Salesforce Einstein Bot configuration that defines the transfer of a conversation from the bot to a live agent when the bot cannot handle the inquiry. This node is placed in the dialogue flow to trigger a seamless handoff, often using Omni-Channel routing to assign the conversation to an available human agent.

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Enable bot analytics

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Train the NLP model with more utterances

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Define intents and entities for the unknown question

Question 15

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.

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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.