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Salesforce AI Associate AI Associate (AI Associate) — Questions 976–1000

1000 questions total · 14pages · All types, answers revealed

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976
Multi-Selectmedium

A company wants to use Einstein Bots to handle customer inquiries. They need to train the bot to understand different customer intents. Which TWO components are essential for defining bot understanding?

Select 2 answers
A.Intents
B.Entities
C.Actions
D.Topics
E.Dialogue flows
AnswersA, B

Intents capture the customer's purpose or goal.

Why this answer

Intents are essential because they define the purpose or goal of a customer's input, such as 'Check Order Status' or 'Cancel Subscription'. The bot uses intents to classify user messages and determine the appropriate response or action. Without intents, the bot cannot understand what the customer wants, making them a foundational component of natural language understanding (NLU) in Einstein Bots.

Exam trap

The trap here is that candidates often confuse Topics or Dialogue flows with the core NLU components, mistakenly thinking they define understanding rather than just organizing or responding to it.

Full explanation →
977
MCQmedium

A team has limited labeled data for a Salesforce predictive model but wants to leverage a pre-trained model from a related task. Which machine learning approach should they use?

A.Transfer learning
B.Unsupervised learning
C.Supervised learning
D.Reinforcement learning
AnswerA

Transfer learning adapts a pre-trained model to a new task with limited data.

Why this answer

Option C is correct because transfer learning uses a pre-trained model and fine-tunes it with limited labeled data. Option A is wrong because unsupervised learning does not use labels. Option B is wrong because supervised learning requires large labeled datasets.

Option D is wrong because reinforcement learning is for decision-making, not classification.

Full explanation →
978
MCQhard

A developer is building an Einstein Bot that needs to understand when a customer says 'I want to return a purchase' and route them to the returns process. How should they configure the bot?

A.Create a dialogue that triggers on the exact phrase 'I want to return a purchase'
B.Create an intent called 'Return Purchase' and train it with sample phrases
C.Create an entity called 'Return Purchase' and map it to a dialogue
D.Use an intent called 'Customer Service' and a custom entity for return
AnswerB

Intents capture the user's goal; training with phrases helps NLP match the intent.

Why this answer

In Einstein Bots, intents represent the customer's goal, and entities capture specifics. 'Return purchase' is an intent, not an entity. The bot uses NLP to match utterances to intents.

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979
Multi-Selecthard

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Select 3 answers
A.Generating report charts and dashboards.
B.Summarizing case conversations from service interactions.
C.Automatically generating personalized customer emails.
D.Creating call scripts for sales reps based on opportunity data.
E.Creating workflow rules based on user behavior.
AnswersB, C, D

GPT can summarize lengthy case threads.

Why this answer

Option B is correct because Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Exam trap

Salesforce often tests the distinction between generative AI capabilities (like content creation and summarization) and traditional CRM features (like reporting or workflow automation), so candidates mistakenly select options that describe standard Salesforce functions rather than Einstein GPT's specific generative AI outputs.

Full explanation →
980
MCQeasy

A company is implementing Einstein Activity Capture. Users have enabled the feature, but emails are not being automatically logged. Which configuration should the administrator verify first?

A.Check that the users' email clients are supported.
B.Verify that Email-to-Salesforce is enabled.
C.Confirm that the Einstein Activity Capture permission set is assigned to users.
D.Ensure users have the 'Log a Task' permission.
AnswerC

The permission set is necessary for the feature to function.

Why this answer

Option C is correct because Einstein Activity Capture requires the dedicated permission set to be assigned to users before it can automatically log emails and events. Without this permission set, the feature is enabled at the org level but users lack the necessary access rights to capture activities, so verifying this assignment is the first logical step.

Exam trap

The trap here is that candidates confuse Einstein Activity Capture with Email-to-Salesforce or client-side logging tools, leading them to check email client support or Email-to-Salesforce settings instead of the permission set assignment.

How to eliminate wrong answers

Option A is wrong because Einstein Activity Capture works with any email client that supports IMAP or Exchange Web Services; it does not require a specific supported client list. Option B is wrong because Email-to-Salesforce is a separate feature for logging emails via a generated address, not a prerequisite for Einstein Activity Capture, which uses server-side sync. Option D is wrong because the 'Log a Task' permission is not required for Einstein Activity Capture; the feature creates activity records automatically without needing manual task logging permissions.

Full explanation →
981
MCQeasy

A service agent needs to quickly find a relevant knowledge article while working on a case. Which Einstein feature can automatically suggest articles based on the case details?

A.Einstein Prediction Builder
B.Einstein Case Classification
C.Einstein Next Best Action
D.Einstein Article Recommendations
AnswerD

Article Recommendations suggests knowledge articles based on case context.

Why this answer

Einstein Article Recommendations is the correct answer because it is the specific Einstein feature designed to automatically surface relevant knowledge articles based on the context of a case, such as subject, description, and product. It uses natural language processing (NLP) to match case details against article content, providing agents with immediate, relevant suggestions without manual search.

Exam trap

The trap here is that candidates often confuse Einstein Next Best Action (which suggests actions) with article recommendations, but Next Best Action is a broader framework for any guided action, not specifically for knowledge articles, and it relies on rules or predictive scoring rather than direct NLP-based article matching.

How to eliminate wrong answers

Option A is wrong because Einstein Prediction Builder is used to create custom predictive models (e.g., predicting case escalation or churn) based on historical data, not for suggesting knowledge articles in real time. Option B is wrong because Einstein Case Classification automatically categorizes cases (e.g., by type or priority) using machine learning, but it does not recommend articles; it focuses on routing or sorting. Option C is wrong because Einstein Next Best Action delivers guided recommendations for actions (e.g., offers, steps) based on rules or AI, but it is not specifically designed to suggest knowledge articles from a case context.

Full explanation →
982
MCQeasy

Which Einstein feature uses strategy builder (flows, Apex) to recommend offers or actions to users at the right moment?

A.Einstein Copilot
B.Einstein Prediction Builder
C.Einstein Next Best Action
D.Einstein Recommendation Builder
AnswerC

Next Best Action uses flows and Apex to determine the best action or offer for a user.

Why this answer

Einstein Next Best Action is the correct answer because it is the Einstein feature that uses Strategy Builder (which includes flows and Apex) to define decision logic and recommend the most relevant offers or actions to users at the right moment. It evaluates real-time context and business rules to surface the optimal next step, such as a discount or a follow-up task, directly within the Salesforce user interface.

Exam trap

The trap here is that candidates confuse Einstein Next Best Action with Einstein Recommendation Builder, because both involve 'recommendations,' but only Next Best Action uses Strategy Builder with flows and Apex for real-time, context-aware action suggestions, while Recommendation Builder is a simpler, legacy tool for static product recommendations.

How to eliminate wrong answers

Option A is wrong because Einstein Copilot is a conversational AI assistant that uses natural language to answer questions and automate tasks, not a recommendation engine driven by Strategy Builder flows and Apex. Option B is wrong because Einstein Prediction Builder creates custom predictive models (e.g., predicting churn) based on historical data, but it does not use Strategy Builder to recommend offers or actions in real time. Option D is wrong because Einstein Recommendation Builder is a legacy tool for product recommendations on ecommerce sites, not a real-time action recommendation engine using flows and Apex.

Full explanation →
983
MCQhard

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

A.Einstein Trust Layer with model explainability features.
B.Data Mask to protect sensitive information.
C.Salesforce Shield with encryption and monitoring.
D.Field Audit Trail to track changes to data.
AnswerA

The Trust Layer includes capabilities to explain predictions.

Why this answer

Option A is correct because the Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

Exam trap

Salesforce often tests the distinction between data security features (like Data Mask or Shield) and AI explainability features, so candidates mistakenly choose a security-focused option when the question explicitly asks about understanding model predictions.

How to eliminate wrong answers

Option B is wrong because Data Mask is a feature for obfuscating sensitive data in non-production environments, not for explaining AI predictions. Option C is wrong because Salesforce Shield provides encryption, field audit trails, and event monitoring for data security and compliance, but it does not offer model explainability or interpretability for AI predictions. Option D is wrong because Field Audit Trail tracks changes to field values over time for data governance, not for understanding why an AI model made a specific prediction.

Full explanation →
984
MCQmedium

A service manager wants to automatically classify incoming cases into Type, Priority, and Reason fields to reduce manual data entry. Which Einstein feature best meets this requirement?

A.Einstein Discovery
B.Einstein Next Best Action
C.Einstein Article Recommendations
D.Einstein Case Classification
AnswerD

Case Classification predicts values for fields like Type, Priority, and Reason.

Why this answer

Einstein Case Classification is specifically designed to automatically predict and populate fields like Type, Priority, and Reason for incoming cases using machine learning models trained on historical case data. This reduces manual data entry by suggesting or auto-filling these fields based on the case's subject, description, and other attributes.

Exam trap

The trap here is that candidates may confuse Einstein Case Classification with Einstein Article Recommendations or Einstein Next Best Action because all three involve 'recommendations' or 'suggestions,' but only Case Classification directly addresses populating structured case fields from incoming data.

How to eliminate wrong answers

Option A is wrong because Einstein Discovery is used for predictive analytics and forecasting trends, not for auto-classifying case fields. Option B is wrong because Einstein Next Best Action recommends the next optimal action or offer to a user or customer, not for populating case metadata. Option C is wrong because Einstein Article Recommendations suggests knowledge articles to agents or customers to resolve cases, not for classifying case fields.

Full explanation →
985
MCQeasy

Which Einstein feature provides AI-powered call recording analysis that tracks keywords, talk-time metrics, and next steps?

A.Einstein Email Insights
B.Einstein Activity Capture
C.Einstein Conversation Insights
D.Einstein Voice
AnswerC

This feature analyzes call recordings for keywords, talk time, etc.

Why this answer

Einstein Conversation Insights analyzes call recordings and provides metrics and action items.

Full explanation →
986
MCQmedium

An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?

A.Transparency
B.Fairness
C.Accountability
D.Privacy
AnswerB

Unequal performance across groups violates fairness.

Why this answer

The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.

Exam trap

Salesforce often tests fairness by presenting a scenario where a model performs well overall but has known disparities for a subgroup, tempting candidates to choose transparency or accountability because they focus on the team's decision to release rather than the core ethical violation of unequal treatment.

How to eliminate wrong answers

Option A is wrong because transparency refers to openness about how the AI system works, its limitations, and its decision-making process; the scenario does not involve hiding information or lack of explainability, but rather knowingly accepting unequal performance. Option C is wrong because accountability concerns who is responsible for the system's outcomes and decisions; while releasing a biased model may raise accountability issues, the core ethical breach here is the unequal treatment itself, not the assignment of responsibility. Option D is wrong because privacy involves the protection of personal data and informed consent; the scenario does not mention any data misuse, unauthorized access, or violation of patient confidentiality.

Full explanation →
987
MCQmedium

A sales manager wants to automatically surface the most important emails that require immediate attention from a high volume of daily customer messages. Which Einstein feature should they enable?

A.Einstein Conversation Insights
B.Einstein Activity Capture
C.Einstein Lead Scoring
D.Einstein Email Insights
AnswerD

Einstein Email Insights identifies and surfaces important emails requiring a response.

Why this answer

Einstein Email Insights (D) is the correct feature because it uses natural language processing (NLP) to analyze email content and metadata, automatically prioritizing messages that require immediate attention based on urgency, sender importance, and context. This directly addresses the sales manager's need to surface critical emails from a high volume of daily customer messages without manual sorting.

Exam trap

The trap here is that candidates confuse Einstein Email Insights with Einstein Activity Capture, assuming that syncing emails automatically implies prioritization, but Activity Capture only logs emails without any intelligent ranking.

How to eliminate wrong answers

Option A is wrong because Einstein Conversation Insights analyzes voice calls and meeting transcripts to provide coaching and sentiment analysis, not email prioritization. Option B is wrong because Einstein Activity Capture syncs emails and events from Microsoft or Google to Salesforce records, but it does not analyze or prioritize email importance. Option C is wrong because Einstein Lead Scoring assigns a numerical score to leads based on their likelihood to convert, which is unrelated to surfacing important emails from existing customers.

Full explanation →
988
MCQhard

A company is building an AI model to score sales leads. They have a dataset with historical leads, including whether they converted. The dataset contains 90% male and 10% female leads. The model will be used to prioritize leads for sales follow-ups. What is the primary ethical concern?

A.The training data is imbalanced, which may cause the model to be less accurate for female leads, leading to unfair prioritization.
B.The model will be biased against male leads because they are overrepresented.
C.The dataset is too small to build a reliable model.
D.Using historical data is unethical because it may not reflect current conditions.
AnswerA

Bias in training data leads to biased outcomes.

Why this answer

Option A is correct because the imbalance can lead to the model performing poorly for female leads, causing gender bias. Option B is wrong because the concern is not that accuracy is already biased, but that training data is biased. Option C is wrong because the dataset size is not the main issue; the imbalance is.

Option D is wrong because using historical data is not inherently unethical, but the imbalance is problematic.

Full explanation →
989
MCQhard

Refer to the exhibit. A developer wrote a trigger to call an Einstein prediction API on lead insert. When new leads are created, the trigger fails with a 'Too many SOQL queries' error. What is the most likely cause?

A.The trigger is executed before the lead is saved
B.The trigger is querying a related object without an index
C.The trigger is making API calls in a loop, causing governor limit issues
D.The Score__c field is not writeable
AnswerC

Looping API calls can exceed limits.

Why this answer

Option C is correct because the trigger is making synchronous API calls to the Einstein prediction service inside a loop that iterates over each new lead. Each API call counts against the 'Number of callouts' governor limit (default 100 per transaction), and if more leads are inserted than the limit allows, the transaction fails with a 'Too many SOQL queries' error—though the error message is misleading, the root cause is exceeding the callout limit, not SOQL queries. The trigger should batch the API calls or use asynchronous processing to stay within limits.

Exam trap

Salesforce often tests the misconception that 'Too many SOQL queries' always means too many SOQL queries, but in this context it actually masks a callout limit issue—candidates may overlook the fact that API calls count against a different governor limit that produces the same error message.

How to eliminate wrong answers

Option A is wrong because the timing of trigger execution (before vs. after save) does not cause a 'Too many SOQL queries' error; that error is a governor limit issue, not a save-order issue. Option B is wrong because querying a related object without an index would cause a 'Too many rows' or performance warning, not a 'Too many SOQL queries' error—the error specifically indicates the number of SOQL queries (or callouts) exceeded the limit, not a missing index. Option D is wrong because the Score__c field being non-writeable would produce a field-level validation or DML exception, not a governor limit error like 'Too many SOQL queries'.

Full explanation →
990
MCQmedium

A healthcare provider uses an AI system to predict patient readmission risk. The system was trained on historical data from the past five years, during which the hospital served a predominantly urban population. Recently, the hospital expanded to rural areas with different demographic and socioeconomic profiles. The AI predictions have been less accurate for rural patients, leading to misallocation of care resources. The AI Ethics committee is reviewing the system for potential bias. The model outputs a risk score from 0 to 100. The data science team has identified that the model uses features such as income, distance from hospital, and insurance type, which may correlate with race and socioeconomic status. The team wants to make the model fairer without retraining from scratch. Which approach best balances fairness and predictive accuracy?

A.Remove the features income, distance, and insurance type from the model and retrain.
B.Continue using the current model but add a disclaimer that predictions may be less accurate for rural patients.
C.Apply a post-processing calibration that adjusts the risk score thresholds separately for urban and rural populations to achieve equal false positive rates.
D.Retrain the model using only the latest year of data that includes rural patients.
AnswerC

Post-processing calibration can equalize error rates across groups without retraining, balancing fairness and accuracy.

Why this answer

Option C is correct because post-processing calibration adjusts the decision thresholds for each subgroup (urban vs. rural) to equalize a fairness metric (e.g., false positive rate) without modifying the underlying model. This approach preserves the predictive signal from the original features while directly addressing the bias caused by distribution shift, making it the most practical solution when retraining from scratch is not feasible.

Exam trap

Salesforce often tests the misconception that removing sensitive features (like income or insurance type) is sufficient to eliminate bias, when in reality proxy variables and correlated features can still perpetuate discrimination, making post-processing or reweighing techniques more effective.

How to eliminate wrong answers

Option A is wrong because simply removing correlated features (income, distance, insurance type) does not guarantee fairness—proxy variables or remaining features can still encode the same biases, and the model may lose important predictive signal, reducing accuracy for all groups. Option B is wrong because adding a disclaimer does not mitigate the misallocation of care resources; it merely acknowledges the problem without taking any corrective action, which fails the ethical requirement to actively reduce bias. Option D is wrong because retraining on only the latest year of data would likely produce a model with insufficient sample size for rural patients, leading to high variance and poor generalization, and it ignores the valuable historical data that could still be useful for urban patients.

Full explanation →
991
Multi-Selecteasy

An admin wants to create a custom AI model to predict lead conversion using Einstein Prediction Builder. Which TWO items must they select when creating the model? (Choose two)

Select 2 answers
A.Model algorithm type
B.Prediction explanation settings
C.Data set (records to train on)
D.Features (input fields)
E.Prediction field (the field to predict)
AnswersC, E

The dataset defines which records are used for training.

Why this answer

Option C is correct because the data set defines the records (e.g., leads, opportunities) that the model will use for training. Without specifying which records to train on, the model has no source of historical data to learn patterns from. Einstein Prediction Builder requires you to select a data set (such as a report or object) to provide the training examples.

Exam trap

The trap here is that candidates confuse the required selections (data set and prediction field) with optional or automated settings like algorithm type or feature selection, leading them to pick options that are not mandatory.

Full explanation →
992
MCQmedium

A developer notices that an AI model performs differently for different age groups. What should be done?

A.Retrain with more data from all ages.
B.Remove age as a feature.
C.Investigate the cause and evaluate fairness metrics.
D.Ignore it if overall accuracy is high.
AnswerC

This is the proper first step.

Why this answer

Option B is correct because investigating the cause and evaluating fairness metrics is the proper first step. Option A is wrong ignoring can lead to unethical outcomes. Option C is wrong retraining with more data may help but without investigation may not address root cause.

Option D is wrong removing age as a feature may not eliminate bias and could reduce model accuracy.

Full explanation →
993
Multi-Selectmedium

A company must comply with GDPR when using Einstein Features to process customer data for AI predictions. Which THREE actions are required under GDPR that relate directly to AI-driven decisions?

Select 3 answers
A.Implement a data retention policy that deletes customer data after 30 days
B.Ensure that only data necessary for the prediction is used (data minimisation)
C.Provide customers with the right to explanation for automated decisions that significantly affect them
D.Audit AI models for bias on a quarterly basis
E.Obtain explicit consent from customers for using their data in AI models
AnswersB, C, E

Data minimisation is a key principle in GDPR; only relevant data should be processed.

Why this answer

GDPR requires a lawful basis for processing (e.g., consent), the right to explanation for automated decisions, and data minimization. Data retention policy is important but not exclusive to AI decisions; and bias auditing is a best practice but not a GDPR requirement.

Full explanation →
994
MCQmedium

An admin wants to compare the AI-generated forecast with a rep's commit forecast to identify gaps. Which feature should they use?

A.Einstein Prediction Builder
B.Einstein Discovery
C.Einstein Forecasting
D.Einstein Opportunity Scoring
AnswerC

Forecasting offers AI predictions and comparison to rep commits.

Why this answer

Einstein Forecasting is the correct feature because it directly compares AI-generated forecasts with a rep's commit forecast to identify gaps. It uses historical data and predictive models to generate a baseline forecast, which can be overlaid with the rep's manual commit to highlight discrepancies for coaching and adjustment.

Exam trap

The trap here is that candidates may confuse Einstein Discovery's data insights or Einstein Opportunity Scoring's predictive scoring with the specific forecast comparison functionality, but only Einstein Forecasting directly provides the AI vs. rep commit gap analysis.

How to eliminate wrong answers

Option A is wrong because Einstein Prediction Builder is a tool for creating custom predictive models on any object or field, not specifically for comparing AI forecasts with rep commits. Option B is wrong because Einstein Discovery is an analytics tool that surfaces insights and explanations from data, but it does not provide a dedicated forecast comparison feature. Option D is wrong because Einstein Opportunity Scoring predicts the likelihood of an opportunity closing, but it does not compare AI-generated forecasts with rep commits.

Full explanation →
995
MCQeasy

Which of the following is a key feature of Salesforce Einstein Trust Layer that protects customer data when using AI?

A.Zero data retention
B.Automatic model retraining
C.Data masking for all PII
D.Open-source model deployment
AnswerA

Zero data retention means customer data is not stored or used to retrain foundation models.

Why this answer

Option A is correct because the Salesforce Einstein Trust Layer is designed with a zero data retention policy, meaning that once an AI model processes a request and returns a response, Salesforce does not store the customer's data. This ensures that sensitive information is not retained on Salesforce servers, protecting customer privacy and complying with data protection regulations. The zero data retention feature is a core architectural principle that prevents data leakage from AI interactions.

Exam trap

Cisco often tests the misconception that data masking or encryption is the primary protection mechanism, when in fact the Einstein Trust Layer's zero data retention policy is the key differentiator that prevents data from being stored at all.

How to eliminate wrong answers

Option B is wrong because automatic model retraining is not a feature of the Einstein Trust Layer; it is a machine learning operational practice that may occur on the Salesforce platform but does not directly protect customer data during AI inference. Option C is wrong because data masking for all PII is not a built-in feature of the Einstein Trust Layer; while Salesforce offers data masking capabilities in other contexts, the Trust Layer specifically focuses on zero data retention rather than masking data in transit or at rest. Option D is wrong because open-source model deployment is not a characteristic of the Einstein Trust Layer; Salesforce uses proprietary models and does not deploy open-source models as part of its trust layer architecture.

Full explanation →
996
MCQhard

A financial services firm uses an AI model to approve loan applications. They discover the model denies loans at a higher rate for a protected demographic. What is the most likely root cause?

A.The model is overfitted
B.The training data contains historical bias
C.The model uses too few features
D.The model has low precision
AnswerB

If historical loan decisions were biased, the model will learn that bias.

Why this answer

Historical bias in training data can cause models to learn and perpetuate discrimination.

Full explanation →
997
Multi-Selecthard

Which TWO are best practices when implementing Einstein Recommendation Builder?

Select 2 answers
A.Use a dataset that reflects recent customer interactions
B.Disable negative feedback to simplify the model
C.Include all available data, even if it is old or unrelated
D.Use a single dataset that contains the entire history of the org
E.Regularly review and test the recommendation model
AnswersA, E

Recent data provides timely recommendations.

Why this answer

Option A is correct because Einstein Recommendation Builder relies on recent customer interaction data to generate accurate and relevant product or content recommendations. Using stale or outdated data can lead to irrelevant suggestions, as the model learns from historical patterns that may no longer reflect current customer preferences or behaviors.

Exam trap

Salesforce often tests the misconception that 'more data is always better' or that 'simplifying the model by disabling feedback improves performance,' when in reality, data quality and feedback signals are critical for accurate recommendations.

Full explanation →
998
MCQeasy

Refer to the exhibit. An admin sees this error in the Einstein activity log. What is the most likely cause?

A.Sentiment analysis is generating PII that data masking cannot hide.
B.The model output should not contain any text.
C.The Einstein Trust Layer is completely disabled.
D.Data masking is configured but not applied to the sentiment analysis model.
AnswerD

Since sentiment analysis is off, the PII leak must be from another component without masking.

Why this answer

The error indicates that sentiment analysis is not enabled, but PII is leaking, likely because data masking is not applied to the model generating the output.

Full explanation →
999
Multi-Selectmedium

Which TWO actions are required to prepare data for an Einstein Discovery model?

Select 2 answers
A.Remove all records with missing values in any field.
B.Select exactly 10 predictor fields manually.
C.Ensure the data is stored in a Salesforce object or a connected data source.
D.Create a separate dataset for training and validation.
E.Define the outcome field that the model will predict.
AnswersC, E

Einstein Discovery requires data accessible via Salesforce.

Why this answer

Options A and C are correct. A: Data must be in a supported object (standard or custom). C: The outcome field must be specified.

Option B is not required; Einstein handles missing values. Option D is not required; data can be within Salesforce or connected. Option E is not required; features can be auto-selected.

Full explanation →
1000
MCQeasy

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

A.Chatter must be disabled for the organization
B.Sales Cloud Einstein licenses for all users
C.The feature is automatically enabled once email integration is configured
D.Users must grant access to their email and calendar via OAuth
AnswerD

Users must authorize Salesforce to access their email and calendar.

Why this answer

Einstein Activity Capture requires users to explicitly grant access to their email and calendar via OAuth (Option D). This OAuth-based authentication allows Salesforce to securely sync emails and meetings from the user's email provider (e.g., Gmail, Outlook) into Salesforce records. Without this explicit consent, the feature cannot access or log the user's activity data.

Exam trap

Salesforce often tests the misconception that Einstein Activity Capture is automatically enabled or requires a separate Einstein license, when in reality it requires explicit user-level OAuth consent and is available with standard Salesforce licenses.

How to eliminate wrong answers

Option A is wrong because Chatter does not need to be disabled for Einstein Activity Capture to work; in fact, Chatter can remain enabled and is unrelated to the email/calendar sync process. Option B is wrong because Sales Cloud Einstein licenses are not a prerequisite for Einstein Activity Capture; the feature is available with standard Salesforce licenses and does not require an add-on Einstein license. Option C is wrong because Einstein Activity Capture is not automatically enabled when email integration is configured; it requires explicit setup, including OAuth authorization from each user, and is not a default consequence of email integration.

Full explanation →
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