AI Associate Practice Test 20 — 15 Questions

Question 1

A sales team wants to use Einstein Lead Scoring to prioritize leads. What is the primary benefit of using Einstein Lead Scoring over manual scoring?

Accepted Answer

It uses historical data to predict which leads are most likely to convert.. Einstein Lead Scoring uses historical data and machine learning models to analyze patterns from past leads and their conversion outcomes. This allows it to assign a predictive score to each new lead, indicating the likelihood of conversion, which is more accurate and data-driven than manual scoring based on subjective criteria.

Answer

It automatically sends personalized emails to leads.

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It ensures all leads are contacted within 24 hours.

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It replaces the need for any manual lead qualification process.

Question 2

A marketing team wants to recommend products to customers based on their past purchases and browsing behavior. Which type of AI is most appropriate?

Accepted Answer

Predictive AI. Product recommendation is a predictive AI task that uses historical data to predict which products a customer is likely to purchase.

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Generative AI

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Reinforcement learning

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Unsupervised learning

Question 3

A company wants to build a chatbot in Service Cloud that can handle common customer queries and escalate to a human agent when needed. Which tool should they use?

Accepted Answer

Einstein Bots. Einstein Bots is the correct tool because it is specifically designed to build conversational chatbots within Service Cloud that can handle common customer queries using predefined flows and seamlessly escalate to a human agent when the bot cannot resolve the issue. Unlike other Einstein features, Einstein Bots integrates directly with Omni-Channel routing to transfer conversations to live agents, making it the appropriate choice for this use case.

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Einstein Next Best Action

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Einstein GPT

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Einstein Copilot

Question 4

A CRM team wants to predict the expected revenue from each opportunity. The data includes opportunity amount, close date, stage, and historical win rates. Which type of AI is best suited?

Accepted Answer

Predictive AI. Predictive AI is correct because the task is to forecast a continuous numerical value (expected revenue) based on historical labeled data (opportunity amount, close date, stage, win rates). This is a classic regression problem, a core supervised learning technique within predictive AI. The model learns patterns from past opportunities to estimate future revenue, directly supporting the CRM team's goal.

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Unsupervised learning

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Generative AI

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Reinforcement learning

Question 5

An admin wants to generate a summary of a sales call recording automatically. Which Einstein feature should be used?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it is specifically designed to analyze sales call recordings and generate summaries, key moments, and action items using natural language processing (NLP) and speech-to-text technology. It ingests audio from calls, transcribes them, and applies AI to extract insights such as customer sentiment, competitor mentions, and next steps, directly meeting the admin's requirement.

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Einstein Activity Capture

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Einstein GPT for Sales

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Einstein Email Insights

Question 6

A service analytics team wants to understand why customer case resolution times have increased. They need automated statistical analysis that generates natural language explanations and suggests improvements. Which product should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct product because it is specifically designed for automated statistical analysis that generates natural language explanations of data patterns and suggests actionable improvements. It uses machine learning to analyze historical data, identify key drivers of changes like increased resolution times, and outputs plain-English insights and recommendations, directly matching the team's need for automated analysis with explanatory and prescriptive output.

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Einstein Prediction Builder

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Einstein GPT

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Einstein Bots

Question 7

A service manager wants to analyze recorded customer service calls to identify top keywords, measure talk-time ratios, and capture next steps automatically. Which Einstein product should they use?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct Einstein product for analyzing recorded customer service calls because it uses natural language processing (NLP) to transcribe calls, extract top keywords, measure talk-time ratios (e.g., agent vs. customer speaking time), and automatically capture next steps or action items. This product is specifically designed for post-call analytics on voice interactions, unlike other Einstein tools that focus on chatbots, email, or predictive modeling.

Answer

Einstein Bots

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Einstein Email Insights

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Einstein Discovery

Question 8

A sales rep wants to automatically log emails and events to Salesforce without manual entry. Which feature should the admin enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture automatically syncs emails, events, and contacts between Microsoft 365 or Google Workspace and Salesforce, logging them to the correct records without manual entry. It is the feature designed specifically for automatic activity logging, matching the sales rep's requirement. Admins enable it via Setup and configure user-level sync settings.

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Einstein Conversation Insights

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Einstein Email Insights

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Einstein Activity Capture is not available; use the standard Email-to-Salesforce

Question 9

A company notices that Einstein Prediction Builder predictions for 'Churn' are less accurate than expected. Which action should the administrator take first to improve model performance?

Accepted Answer

Review the training data for missing values and ensure relevant fields are included in the model.. The first step in improving Einstein Prediction Builder model performance is to review the training data for missing values and ensure relevant fields are included. Missing values or irrelevant fields can introduce noise and bias, directly degrading predictive accuracy. Einstein Prediction Builder relies on high-quality, complete training data to learn meaningful patterns, so data quality issues must be addressed before any other tuning steps.

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Enable field history tracking on all object fields used in the prediction.

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Change the prediction outcome to a different field to see if accuracy improves.

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Retrain the model with the same data but increase the number of training iterations.

Question 10

A company is using Einstein Discovery to predict customer churn. The model was created six months ago and has been making predictions. Recently, the model's accuracy has dropped significantly. The data scientist confirms that the data schema has not changed. What is the most likely reason for the drop in accuracy?

Accepted Answer

The model is suffering from concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate even though the data schema remains unchanged. In Einstein Discovery, models are trained on historical data, and if the underlying patterns of customer churn evolve (e.g., due to market shifts or new competitor behavior), the model's learned relationships become stale. Since the data schema is confirmed unchanged, concept drift is the most likely cause of the accuracy drop.

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The data source is not being refreshed daily

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The model's features have become irrelevant

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The model needs to be retrained weekly instead of monthly

Question 11

A company wants to build a chatbot that can understand customer intents like 'open a case' or 'check order status' and route conversations accordingly. They need the bot to be trained with natural language examples. Which Einstein Bot component is used to map user phrases to actions?

Accepted Answer

Intents. Intents are the correct component because they map user phrases (natural language examples) to specific actions or goals, such as 'open a case' or 'check order status'. In Einstein Bot, intents are trained with sample utterances to recognize what the user wants, enabling the bot to route conversations to the appropriate dialog flow.

Answer

Entities

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Dialog flows

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NLP training

Question 12

A company uses Einstein GPT for Sales to generate personalized email drafts. They want to ensure that the generated emails consistently include the recipient's company name and a specific discount offer from the opportunity. Which configuration is required?

Accepted Answer

Create a prompt template in Prompt Builder with merge fields for Company and Discount. Prompt Builder allows admins to create prompt templates that include merge fields for dynamic data, such as the recipient's company name and a discount offer from the opportunity. When Einstein GPT for Sales generates email drafts, it uses these merge fields to pull the specific values from the Salesforce record, ensuring consistency across all generated emails.

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Configure Einstein Email Insights to highlight opportunities with discount information

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Set up a Flow in Einstein Next Best Action to inject data into the email

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Use Einstein Recommendation Builder to define dynamic email components

Question 13

Which TWO are best practices when implementing Einstein Recommendation Builder?

Accepted Answer

Use a dataset that reflects recent customer interactions. Option A is correct because Einstein Recommendation Builder learns from the interaction data you supply, so the dataset must reflect recent customer interactions and current catalog/behavior patterns; stale or outdated data would produce recommendations that no longer match what customers actually browse or buy. Option E is correct because recommendation models must be monitored, retested, and retrained over time — reviewing metrics and validating outputs ensures the model still delivers relevant recommendations as customer behavior and inventory change. Option B is incorrect because negative feedback (such as dislikes or non-conversions) is valuable signal that helps the model avoid poor recommendations; disabling it degrades quality rather than simplifying usefully. Option C is incorrect because dumping in all data, including old or unrelated records, introduces noise and skews the model instead of improving it. Option D is incorrect because a single dataset containing the entire history of the org is not a best practice — it bloats training, mixes irrelevant contexts, and ignores the need for recent, scoped, high-quality data.

Answer

Disable negative feedback to simplify the model

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Include all available data, even if it is old or unrelated

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Use a single dataset that contains the entire history of the org

Question 14

A sales manager wants to automatically log emails from a specific customer domain to Salesforce, but exclude internal company emails and spam. Which feature should they configure?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture is the correct feature because it automatically logs emails and events from supported email clients (like Gmail and Outlook) into Salesforce, with configurable rules to include or exclude specific domains. This allows the sales manager to set a rule to log emails from the customer domain while excluding internal company emails and spam, without requiring manual user action or complex automation.

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Einstein Email Insights

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Einstein Lead Scoring

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Einstein Conversation Insights

Question 15

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should be used?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it uses predictive models to automatically rank leads based on their likelihood to convert, enabling the sales manager to prioritize follow-up efforts. It analyzes historical lead data and engagement patterns to assign a score, directly addressing the requirement for automated prioritization without manual rules.

Answer

Einstein Prediction Builder

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Einstein Relationship Health

Answer

Einstein Activity Capture