AI Associate Practice Test 41 — 15 Questions

Question 1

A model predicts customer churn with 95% accuracy, but most customers who actually churn are not flagged by the model. Which metric should the team improve?

Accepted Answer

Recall. Recall measures the proportion of actual positive cases (customers who churned) that the model correctly identified. The scenario states that most actual churners are not flagged, which is precisely a low-recall problem. Improving recall will increase the number of true churners caught by the model.

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Precision

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Accuracy

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F1 score

Question 2

A company wants to build a custom AI prediction for churn rate using their Customer_Churn__c (Yes/No) field. In Einstein Prediction Builder, after selecting the object and prediction field, what is the next step?

Accepted Answer

Select the dataset (records to train on).. In Einstein Prediction Builder, after choosing the object and the prediction field (e.g., Customer_Churn__c), the next step is to select the dataset — the set of records used to train the model. This defines the historical data from which the model learns patterns. Only after the dataset is defined do you select features and configure scoring.

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Define the prediction score field and explanation field.

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Deploy the model to production.

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Select features (input fields) to train the model.

Question 3

A healthcare organization needs to automatically classify incoming cases into predefined categories (e.g., billing, clinical, technical) based on the case description. They have historical case data with known categories. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is specifically designed to automatically categorize incoming cases into predefined categories based on case description text. It uses historical case data with known categories to train a model that predicts the appropriate category for new cases. This directly matches the requirement of classifying cases into billing, clinical, technical, etc.

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Einstein Vision and Language Platform

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Einstein Prediction Builder

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Einstein Next Best Action

Question 4

A data scientist wants to use Einstein Discovery to analyze customer churn. They want to understand which factors contribute most to churn and get actionable suggestions. Which THREE outputs does Einstein Discovery provide?

Accepted Answer

Improvement suggestions (recommended actions). Einstein Discovery provides improvement suggestions (recommended actions) that guide data scientists on specific changes to reduce churn. These actionable insights are generated from the predictive model's analysis of historical data, offering concrete steps like 'increase engagement frequency' or 'offer discount' to improve outcomes.

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Prediction score for each record

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Waterfall chart (visualization of step-by-step impact)

Question 5

During data transformation, a data scientist applies one-hot encoding to a categorical feature with 50 unique values. The resulting dataset has 50 new columns. What is a potential drawback of this transformation?

Accepted Answer

High cardinality leading to sparse data and overfitting. One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.

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Reduction in training time

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Increased interpretability of the model

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Loss of ordinal information in categories

Question 6

A marketing manager wants to use Einstein Next Best Action to recommend offers to customers based on their behavior. Which TWO components are used in defining Next Best Action strategies?

Accepted Answer

Apex. In Einstein Next Best Action, recommendation strategies are defined declaratively using Salesforce Flows (option E), which evaluate the strategy's conditions and select the appropriate recommendation to surface to the user. Apex (option D) is also a valid strategy type, allowing developers to implement custom recommendation logic via an Apex class that implements the RecommendationStrategy interface when declarative Flows are insufficient. Options A (intents and entities) belong to Einstein Bots/Natural Language Processing for parsing user utterances, not Next Best Action strategy definition. Option B (prompt templates) is unrelated to Next Best Action strategies. Option C (image classification models) is an Einstein Vision capability, not a component for defining Next Best Action strategies.

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Intents and entities

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Prompt templates

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Image classification models

Question 7

A data analyst wants to use Einstein Discovery to understand factors driving case resolution time. Which TWO outputs does Einstein Discovery provide?

Accepted Answer

Waterfall charts. Einstein Discovery provides Stories (option E), which are the core analytical narratives that surface insights, predictions, and prescriptions about a chosen outcome such as case resolution time, including which variables most influence it. It also provides Waterfall charts (option B), which visually break down how each individual feature or field pushes a prediction above or below the baseline, explaining the drivers behind a specific prediction. These two outputs directly serve the analyst's goal of understanding the factors driving case resolution time. Bot analytics (A) belongs to Einstein Bots, not Einstein Discovery, and reports on chatbot conversation metrics. Case classifications (C) are produced by Einstein Case Classification, a separate predictive service for routing and field prediction. Lead scores (D) come from Einstein Lead Scoring, which ranks leads by conversion likelihood, not case resolution drivers.

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Bot analytics

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Case classifications

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Lead scores

Question 8

A company wants to use Einstein GPT to generate personalized sales emails for their sales team. They need to ensure that the generated emails adhere to brand voice guidelines. Which tool should they use to define the prompt template for email generation?

Accepted Answer

Prompt Builder. Prompt Builder is the correct tool because it allows users to create and manage prompt templates that define the structure, tone, and brand voice guidelines for generative AI outputs like sales emails. Unlike other Einstein tools, Prompt Builder is specifically designed to control the input and output of large language models (LLMs) within Salesforce, ensuring generated content adheres to predefined brand standards.

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Einstein Copilot

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Einstein Discovery

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Einstein Recommendation Builder

Question 9

What type of AI is designed to perform a specific task, such as playing chess or recommending products?

Accepted Answer

Narrow AI. Narrow AI (also called Weak AI) is designed to perform a specific, limited task such as playing chess, recommending products, or recognizing faces. It operates within a predefined domain and cannot transfer its capabilities to unrelated tasks. This matches the scenario described in the question.

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Super AI

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Reactive AI

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General AI

Question 10

A company wants to use Einstein Sentiment Analysis to classify customer feedback. What is the FIRST step they should take to ensure ethical use of customer data?

Accepted Answer

Obtain proper consent from customers for using their data in AI. Before using customer data in any AI system, including Einstein Sentiment Analysis, the organization must obtain proper consent from customers for that specific use — this is the foundational ethical and often legal requirement (e.g., under GDPR, CCPA). Consent establishes the lawful basis for processing and ensures transparency about how the data will be used. Only after consent is secured should technical measures like anonymization or audit trails be considered.

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Set up an audit trail for all AI actions

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Choose a pre-trained model to avoid using customer data

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Anonymize all customer feedback before analysis

Question 11

An admin wants to use Einstein GPT to generate personalized sales emails for reps. They need to ensure the emails include the latest product inventory data from an external system. Which approach should they take?

Accepted Answer

Create a Prompt Template in Prompt Builder that calls an Apex class to fetch inventory data, then use it in Sales GPT. It leverages Prompt Builder to create a custom prompt template that calls an Apex class, enabling real-time retrieval of external inventory data via an API callout. This ensures the personalized sales emails generated by Einstein Sales GPT always include the latest product inventory without manual intervention or stale data.

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Embed a report snapshot in the email using Einstein Analytics

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Use Einstein Sales GPT with a standard template and manually update the inventory data weekly

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Use Einstein Copilot to ask the rep to check inventory before sending each email

Question 12

A global manufacturing company uses Sales Cloud and has implemented Einstein Opportunity Scoring to prioritize deals. The scoring model was trained on historical data and initially performed well. Over the past month, the scores have become less accurate, with many high-scoring opportunities not closing and some low-scoring ones closing. The admin notices that the sales team has been using a new discounting strategy that heavily influences deal outcomes. The admin wants to improve model performance without manual intervention. Which action should the admin take?

Accepted Answer

Retrain the Einstein Opportunity Scoring model with the latest opportunity data including discount information.. Retraining the Einstein Opportunity Scoring model with the latest opportunity data, including discount information, allows the machine learning model to automatically learn the new patterns introduced by the sales team's discounting strategy. This aligns with the AI Associate principle that models must be retrained on current data to maintain accuracy when business processes change, without requiring manual intervention.

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Manually adjust the field weights for discount-related fields in the model.

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Run a data quality report to identify and clean missing discount data.

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Create a custom field for discount percentage and add it to the model.

Question 13

A company wants to predict which sales opportunities are most likely to close. They want the prediction to consider factors like stage, amount, and historical win rates. Which Einstein feature should they use?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is the correct feature because it uses AI to analyze historical win rates, deal stage, amount, and other opportunity attributes to predict the likelihood of a deal closing. This directly matches the requirement to consider factors like stage, amount, and historical win rates for sales opportunities.

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Einstein Forecasting

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Einstein Lead Scoring

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Einstein Prediction Builder

Question 14

A company uses Einstein Conversation Insights to analyze sales call recordings. They want to automatically capture the next steps mentioned in calls. Which feature of Conversation Insights should they configure?

Accepted Answer

Next Step Capture. Next Step Capture, is correct because it is the specific Einstein Conversation Insights feature designed to automatically identify and extract action items or follow-up tasks mentioned during sales calls. This allows the system to surface commitments and next steps without manual note-taking, directly addressing the requirement to capture next steps from call recordings.

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Talk-time Metrics

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Keyword Tracking

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Call Summary

Question 15

A Salesforce admin wants to deploy an Einstein bot that uses natural language processing. Which practice best ensures ethical use?

Accepted Answer

Provide clear disclaimers that the user is interacting with an AI.. Providing clear disclaimers that the user is interacting with an AI is a fundamental ethical practice for transparency, ensuring users are aware they are not communicating with a human. This aligns with ethical AI principles such as honesty and informed interaction, which are critical for Einstein bots using NLP. It helps set appropriate expectations and builds trust.

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Use the bot only for internal processes.

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Collect as much personal data as possible to improve accuracy.

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Allow the bot to make autonomous decisions without human review.