AI Associate Practice Test 22 — 15 Questions

Question 1

An admin wants to create a custom AI prediction that uses data from a custom object and a standard object. The prediction should be a binary classification (Yes/No). Which tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it allows admins to create custom binary classification predictions (Yes/No) using data from both custom and standard objects without requiring a data scientist. It leverages point-and-click configuration to train a model on historical data and generate predictions directly within Salesforce.

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Einstein Vision

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Einstein Next Best Action

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Einstein Discovery

Question 2

A sales manager wants to automatically track emails and events from their sales team's Gmail accounts into Salesforce without manual logging. Which Salesforce feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (B) is the correct feature because it automatically syncs emails and events from Gmail (and Microsoft 365) into Salesforce without requiring manual logging by users. It uses a background synchronization process that captures activities based on configured rules, eliminating the need for manual entry or third-party integrations.

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Einstein Lead Scoring

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Einstein Email Insights

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Einstein Conversation Insights

Question 3

To identify common customer issues from chat transcripts using AI, which feature should be used?

Accepted Answer

Einstein Conversation Mining. Einstein Conversation Mining is the Salesforce AI feature specifically designed to analyze chat transcripts and other conversation data to surface common customer issues, intents, and trends. It uses natural language processing to cluster and classify conversations, making it the correct tool for identifying recurring customer problems from chat transcripts.

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Post-Interaction Survey

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Reports

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Dashboards

Question 4

A sales manager wants to use Einstein Activity Capture to log emails automatically. Which prerequisite must be met?

Accepted Answer

The user's email must be hosted on a supported platform (Gmail, Outlook).. Einstein Activity Capture requires that user emails be hosted on a supported platform (Gmail or Outlook/Exchange) because the feature uses server-side synchronization via APIs (Google Workspace APIs or Microsoft Graph) to automatically log emails and events into Salesforce. Without a supported email host, the service cannot connect to the mail server to capture activity data.

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The org must be on Enterprise Edition or higher.

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The user must have an Einstein AI license.

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The user must manually enable email logging in personal settings.

Question 5

A sales admin wants to automatically log emails and events from a sales rep's email client to Salesforce without manual entry. Which feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct feature because it automatically logs emails and events from a connected email client (like Outlook or Gmail) into Salesforce without requiring manual entry. It uses server-side synchronization to capture interactions based on configured rules, making it the direct solution for this use case.

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Einstein Conversation Insights

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Einstein Lead Scoring

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Einstein Email Insights

Question 6

A model predicts customer lifetime value with high precision but low recall on high-value customers. What is the business impact?

Accepted Answer

Many false negatives for high-value customers. Low recall means many high-value customers are missed, leading to lost opportunities to retain them.

Answer

The model is overfitted

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Many false positives for high-value customers

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High accuracy on low-value customers

Question 7

A company deployed an AI chatbot for customer service. After a week, they receive complaints that the chatbot responds differently based on customer accent. The ethical issue is most likely due to:

Accepted Answer

Bias in the training data leading to discriminatory behavior. The chatbot responding differently based on customer accent is a classic symptom of training-data bias: if the speech or text corpus used to train the model overrepresents certain accents or associates particular accents with negative sentiment, the model learns and reproduces that discriminatory pattern. Accent is a proxy for national origin, race, and socioeconomic status, so differential treatment constitutes an ethical and potentially legal violation. The root cause is the data, not the interface or infrastructure.

Answer

Lack of personality in the chatbot responses

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Insufficient computational resources allocated to the chatbot

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Poor user interface design

Question 8

A company wants to use Einstein Next Best Action to recommend actions to sales reps. Which TWO components are part of the Next Best Action strategy? (Choose 2)

Accepted Answer

Flows. In Einstein Next Best Action, a strategy is built from recommendation sources that supply the suggested actions, and the two supported source types are Flows (option A) and Apex (option C). Option A is correct because a Flow can be used as a recommendation source, where the flow logic determines which recommendation to surface and returns the recommendation object to the strategy. Option C is correct because an Apex class implementing the RecommendationStrategy interface can likewise serve as a recommendation source, allowing programmatic logic to generate recommendations. Option B is incorrect because Einstein Bots is a separate conversational AI feature for chat/self-service, not a Next Best Action strategy component. Option D is incorrect because Einstein Prediction Builder produces predictive scores/models, which can feed logic but are not themselves a Next Best Action strategy source. Option E is incorrect because external machine learning models are not a native Next Best Action strategy component; recommendations must come from the supported Flow or Apex sources.

Answer

Einstein Bots

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Einstein Prediction Builder models

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External machine learning models

Question 9

Which TWO of the following are ethical considerations when deploying AI in Salesforce?

Accepted Answer

Providing transparency in AI-generated outcomes. Transparency in AI-generated outcomes is a core ethical principle, especially in Salesforce's Einstein platform, where users must understand how predictions (e.g., lead scoring or opportunity insights) are made. Salesforce provides tools like 'Why This Prediction?' to explain model outputs, ensuring trust and accountability. Without transparency, users cannot validate or challenge AI decisions, leading to potential bias or misuse.

Answer

Maximizing model complexity for better accuracy

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Using only historical data without review for biases

Question 10

A service manager wants AI to automatically generate a summary of a phone call recording and capture follow-up tasks. Which Einstein feature should they use?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the Salesforce feature designed to transcribe and analyze voice and video calls (including phone calls via Einstein Conversation Insights or Sales/Service Cloud Voice), automatically generating summaries, extracting key topics, and surfacing follow-up tasks. It directly matches the requirement to summarize a phone call recording and capture action items.

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Einstein Email Insights

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Service GPT

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Einstein Activity Capture

Question 11

A financial services firm uses an AI model to approve small business loans. The model denies loans at a much higher rate for businesses owned by minorities, even when financial indicators are similar. What is the MOST likely cause?

Accepted Answer

Bias in training data. When a model denies loans at a higher rate for minority-owned businesses despite similar financial indicators, the most likely cause is bias in the training data — historical lending data may reflect past discriminatory practices, and the model learns and perpetuates those patterns. This is a classic example of algorithmic bias stemming from biased training data.

Answer

Low recall for minority groups

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Hallucination in model predictions

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Overfitting to historical data

Question 12

Which Salesforce feature automatically flags data quality issues before training an AI model?

Accepted Answer

Data Manager (Data Prep Assistant). Data Manager (Data Prep Assistant) is correct because it is the Salesforce Data Cloud feature that profiles and analyzes ingested data, automatically surfacing data quality issues such as missing values, outliers, and inconsistencies before the data is used to train an AI model. Its built-in quality metrics and remediation suggestions let users clean and prepare data prior to model training. Flow is an automation tool for business processes, not a data quality profiler. Validation Rules enforce field-level data integrity at record save time in Salesforce objects, but they do not scan datasets for quality issues before AI training. Duplicate Matching identifies duplicate records but does not comprehensively flag broader data quality problems like nulls or format inconsistencies.

Answer

Flow

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Validation Rules

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Duplicate Matching

Question 13

A sales rep wants to see which of their leads are most likely to convert, ranked from 1 to 99, directly in the lead list view. Which feature provides this capability?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it automatically assigns a score from 1 to 99 to each lead based on historical conversion patterns, directly in the lead list view. This allows the sales rep to rank leads by likelihood to convert without manual calculation or custom development.

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Einstein Opportunity Scoring

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Einstein Activity Capture

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Einstein Prediction Builder

Question 14

A company uses Salesforce Data Cloud to unify customer data from multiple sources for AI model training. After adding a new data source, model performance degrades significantly. What is the most likely cause?

Accepted Answer

Data duplication from overlapping sources. Data duplication from overlapping sources. When a new source overlaps with existing sources, Data Cloud's identity resolution can create duplicate unified profiles, inflating or skewing the training dataset and degrading model performance. Insufficient compute resources (A) would affect processing speed, not model accuracy. Data labeling errors (B) are possible but less directly tied to adding a source, and Data Cloud unification focuses on identity, not labels. A schema mismatch (C) would typically cause ingestion or mapping failures rather than subtle performance degradation.

Answer

Insufficient compute resources

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Data labeling errors

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Data schema mismatch

Question 15

A company is deploying an AI model to automatically classify customer emails into categories (Complaint, Inquiry, Feedback). They have 10,000 labeled emails. Which TWO actions are essential to ensure the model's accuracy? (Select TWO.)

Accepted Answer

Split the data into training and test sets. Option B is correct because splitting the 10,000 labeled emails into training and test (or validation) sets is essential to evaluate the model on data it has not seen, which detects overfitting and gives an honest estimate of generalization accuracy. Option E is correct because the three target categories (Complaint, Inquiry, Feedback) must each be adequately represented in the training data; a skewed class distribution biases the classifier toward majority classes and degrades per-class accuracy, so balanced representation (or appropriate class weighting/resampling) is needed. Option A is not correct because driving training accuracy to 100% typically means overfitting, which harms test accuracy rather than ensuring it. Option C is not correct because discarding 9,000 labeled examples and training on only the most recent 1,000 reduces data volume and can introduce recency bias, weakening the model. Option D is not correct because removing all emails with spelling errors shrinks and skews the dataset and removes real-world noise the model must handle; text normalization or spell correction is preferable to deletion.

Answer

Train the model until it achieves 100% accuracy on the training set

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Use only the most recent 1,000 emails for training to reflect current trends

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Remove all emails that contain spelling errors