AI Fundamentals

A retail company uses Einstein Prediction Service to forecast customer churn. To improve model accuracy, which data preparation step is most critical?

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

A company uses Einstein Bots to handle customer service inquiries. The bot often fails to understand complex requests, leading to escalations. Which improvement strategy is most effective?

A nonprofit uses Einstein Vision to classify images of disaster areas. What is the primary benefit of using AI for this task?

A company deploys Einstein Recommendation Builder on its e-commerce site. The recommendations are not personalized. What is the most likely cause?

A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?

Which TWO actions are best practices when implementing Einstein Prediction Service?

Which THREE are valid considerations when deploying an Einstein Bot?

Which TWO data types can be used as input for Einstein Vision?

Based on the exhibit, what does the accuracy of 0.85 indicate?

Based on the exhibit, what is the primary issue with this Einstein Bot conversation?

A company wants to use Einstein Activity Capture to log emails and events automatically. Which two considerations should the admin evaluate before enabling this feature?

Refer to the exhibit. An admin configures Einstein Next Best Action with the above JSON. The expected behavior is to recommend the top 5 actions for open leads with a score of at least 70. However, only 2 recommendations appear for some leads. Which is the most likely cause?

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?

A nonprofit organization uses Salesforce to manage donor relationships. They have implemented Einstein Prediction Builder to predict which donors are likely to upgrade their donation level in the next 90 days. The model was built using a custom object "Donation" with fields like Amount, Frequency, and Campaign. After deployment, the predictions seem random and do not correlate with donor engagement. The admin suspects the model is not trained on enough records. The organization has 500 donors with at least two donations each. What should the admin do to improve the model?

A marketing manager uses Einstein recommendations on their website, but customers are receiving suggestions for products they already purchased. What is the most likely cause?

A company wants to use Einstein Prediction Builder to predict customer churn. They have a dataset with 10,000 records and 50 features. What is the primary consideration for model accuracy?

A sales representative uses Einstein Activity Capture to log emails automatically. However, some critical emails are not being captured. What is the most likely reason?

A data scientist is evaluating the performance of an Einstein Discovery model. They observe that the model has high accuracy but low precision for a specific prediction class. What does this indicate?

A company wants to deploy an Einstein Prediction Builder model to predict lead conversion within 30 days. They have historical data from the past 12 months. Which data preprocessing step is most critical to ensure the model learns correctly?

A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?

A nonprofit organization wants to use Einstein Bots to handle inquiries on their website. They are concerned that the bot may give incorrect or insensitive responses. Which feature should they prioritize to maintain trustworthy AI?

A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)

A company is deploying Einstein Prediction Builder to predict equipment failure. Which three considerations are essential for building an accurate prediction model? (Choose 3)

A data analyst is reviewing an Einstein Discovery story and notices that one input feature has a very high influence on the predicted outcome. Which two conclusions are justified based on this observation? (Choose 2)

Refer to the exhibit. A Salesforce admin evaluates an Einstein Prediction Builder model for customer churn. What should be the admin's primary concern based on the exhibit?

Refer to the exhibit. A sales manager sees that an account has an Einstein Score of 78 with a confidence of 0.65. What is the most appropriate interpretation?

Refer to the exhibit. A Salesforce admin is troubleshooting email capture failures. Based on the log, which configuration step is most likely missing?

A Salesforce admin wants to use Einstein GPT to generate personalized email content for a marketing campaign. To ensure the AI does not produce responses that include sensitive customer data or violate company policies, which Salesforce feature should the admin configure?

A developer is creating a prompt template for Einstein GPT to summarize customer case details. The prompt must include the case subject, description, and last 3 comments, but only when the case priority is High. Which approach best achieves this in Prompt Builder?

A user asks Einstein GPT to generate a product description. The AI returns a response with a confidence score of 0.65. What does this score indicate?

In Salesforce Data Cloud, which AI capability is used to automatically generate audience segments based on customer behavior patterns?

A data scientist is evaluating a custom Einstein model for a lead scoring use case. The model's precision is 0.9, recall is 0.5. What is the most important improvement priority?

A retail company implements an AI chatbot to recommend products. After launch, they notice the chatbot frequently suggests expensive items to budget-conscious customers. Which AI bias is most likely occurring?

What is the primary purpose of Einstein Studio in the Salesforce AI ecosystem?

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

Which THREE factors can affect the accuracy of an Einstein GPT response?

Refer to the exhibit. A developer configured a grounding policy for Einstein GPT. What is the effect of the fallbackBehavior set to 'USE_MODEL_KNOWLEDGE'?

Refer to the exhibit. A Salesforce admin runs an audit command for an Einstein model. What conclusion can be drawn from the output?

Refer to the exhibit. A data scientist built a model using training data where 80% of leads were won. The model achieved 80% accuracy. What is the main issue with this evaluation?

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?

A company wants to use Einstein Discovery to analyze sales data and automatically uncover key drivers of deal closure. What must the admin provide to create a story?

A service team trains an Einstein Bot on historical chat transcripts. After deployment, the bot frequently fails to understand customer intents. Which action is most likely to improve performance?

An admin creates a predictive model in Einstein Prediction Builder to forecast customer churn. The model shows high accuracy on test data but poor performance in production. What is the most likely cause?

A data scientist notices that an Einstein Discovery model predicts a low probability of conversion for all leads in a new campaign, even though the campaign targets high-value accounts. Which initial diagnostic step should be taken?

A marketing manager wants to use AI to recommend next-best actions for customers based on their previous purchases. Which Einstein feature is most appropriate?

An admin sets up Einstein Sentiment scoring for case comments. After a week, they notice that most comments are scored as 'Neutral' even when customer sentiment is clearly negative. What should the admin check first?

During an AI ethics review, a stakeholder asks how Salesforce ensures that Einstein models do not discriminate based on protected attributes. Which mechanism addresses this concern?

A company wants to use Einstein to predict the optimal discount amount for each deal. Which type of machine learning problem does this represent?

Which TWO of the following are common causes of model drift in Einstein Discovery?

Which THREE of the following are best practices for training an Einstein Bot?

Which TWO of the following are key principles of trustworthy AI according to Salesforce's AI ethics guidelines?

Refer to the exhibit. The prediction API returns a probability of 0.85 for the label 'High Value'. What does this value represent?

Refer to the exhibit. A bot developer sees this error during Einstein Bot deployment. What is the correct action to resolve the issue?

Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?

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?

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?

A company wants to use Einstein Bots to handle customer support queries. Which preparation is most important before deploying the bot?

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 company wants to deploy an Einstein AI model that uses sensitive customer data. Which practice should they follow to comply with data privacy regulations?

Which TWO actions are recommended when preparing data for an Einstein Prediction Builder model?

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

According to Salesforce's AI Trust Principles, which TWO practices are essential for ethical AI deployment?

Refer to the exhibit. A Salesforce admin configured the Einstein Trust Layer policy shown. What is the effect of this policy on AI model usage?

Refer to the exhibit. A Salesforce CLI output shows the status of Einstein models in the org. Which model should the administrator investigate first?

A mid-sized company uses Salesforce for sales and service. They have implemented Einstein Prediction Builder on a custom object 'Support_Ticket__c' to predict whether a ticket will be escalated (field: 'Escalated__c' Boolean). The model was trained with 10,000 records and 15 fields including 'Subject', 'Description_Summary__c', 'Priority__c', 'Hours_to_Resolution__c', and others. After deployment, the model's precision for escalated tickets is only 30%, while recall is 80%. The business finds too many false positives. The admin notices that the 'Priority__c' field has many missing values (60% null) and that the field 'Is_Critical__c' (a formula field) was included though it flags tickets as critical only rarely. The data spans 12 months but the last 3 months have a significantly higher escalation rate due to a product bug that has since been fixed. Which course of action will most likely improve the model's precision without harming recall?

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 marketing manager wants to use Einstein Send Time Optimization. To generate personalized send time recommendations, which data does the model primarily rely on?

A financial services company is deploying Einstein Prediction Builder to predict customer churn. The data includes both numerical and categorical fields. Which step is essential to ensure the model is not biased against protected attributes like race or gender?

A service manager wants to use Einstein Case Classification to automatically categorize incoming cases. What is a prerequisite for training the model?

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

A data scientist is evaluating Salesforce's Einstein features for predictive analytics. Which three statements accurately describe Einstein Discovery? (Select three answers.)

A global retail company with 50,000+ users has deployed Einstein Activity Capture across Sales and Service Clouds. After two weeks, the VP of Sales reports that only 60% of emails sent from Outlook are being logged in Salesforce. Users have installed the Einstein Activity Capture plugin and have the correct permission set. The admin has verified that the email logging settings are enabled for all users. The company uses Exchange Online. What should the admin investigate first?

A marketing director wants to use Einstein Engagement Scoring to prioritize leads. She has enabled Einstein and assigned the permission set to users. However, the Engagement Score field is not visible on any lead record. The admin checked the field-level security and it is visible to all profiles. What should the admin do next?

A sales rep noticed that the Einstein Lead Scoring prediction bar shows 'No score available' for many leads. The admin confirmed that Einstein Lead Scoring is enabled and the permission set is assigned. What is the most likely cause?

A company uses Einstein Prediction Builder to create a custom model that predicts whether a support case will be escalated. The model is built and published, but when the admin looks at the case record, the prediction field shows 'No Prediction' for all cases. The prediction is set to run on case creation and update. What should the admin check?

A service organization wants to use Einstein Reply Recommendations to suggest responses to customer chats. The feature is enabled, but agents report that no recommendations appear. The admin has ensured the permission set is assigned and the chat data is flowing. What should the admin examine next?

A sales manager wants to implement Einstein Automated Contacts to automatically create contacts from email interactions. The admin enables the feature and assigns the permission set. However, no contacts are being created automatically. What is the most likely reason?

A company has been using Einstein Lead Scoring for six months. Recently, the lead score confidence has dropped from 85% to 60%. The admin reviews the model and finds that many leads have missing data in custom fields used by the model. The admin also notices that field history tracking is not enabled on the Lead object. The lead volume is adequate with over 10,000 leads. What should the admin do to improve the model's confidence?

A marketing manager wants to predict which customers are most likely to respond to a new email campaign. Which type of machine learning is most appropriate?

A sales team notices that their lead scoring model assigns high scores to leads that rarely convert. The model was trained on data from the past 5 years. What is the most likely cause?

A customer support center wants to automatically route incoming cases to the appropriate department based on the issue description. Which NLP task is most relevant?

A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. Which issue is most likely occurring?

A company’s AI model recommends products to customers. The team wants to measure how often the recommended products are actually purchased. Which metric is most appropriate?

A financial services firm uses an AI model to approve loan applications. They discover that the model denies loans at a higher rate for a certain demographic group, even when financial indicators are similar. What is the primary ethical concern?

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?

A retailer wants to use computer vision to automatically identify products from images uploaded by customers for a return process. Which computer vision task is required?

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?

A data analyst is preparing data for a machine learning model. They notice that many records have missing values for the 'industry' field. What is the best first step?

A company uses an NLP model to detect customer intent from chat messages. The model correctly identifies 'billing question' 90% of the time for actual billing questions, but also flags many non-billing messages as billing (false positives). Which metric should the team prioritize to reduce false alarms?

A sales director wants to implement lead scoring but has no historical data on which leads converted. What approach can the team use to start?

A company is building a sentiment analysis model for customer reviews. They want to measure its performance. Which TWO metrics are most appropriate for evaluating a classification model?

A data scientist is building a churn prediction model. The dataset has 95% non-churn and 5% churn. Which THREE actions should the data scientist take to address the class imbalance?

A company uses a generative AI model to create marketing copy. They want to ensure the output is accurate and not misleading. Which TWO practices should they implement?

A sales operations manager wants to predict which leads are most likely to convert to deals. The CRM has historical data on thousands of leads with outcomes (converted or not). Which type of machine learning should they use?

A company uses an AI model to classify customer support cases into categories (billing, technical, general). The model performs well on training data but poorly on new cases. Which issue is MOST likely occurring?

A customer service team wants to automatically detect the intent of incoming chat messages (e.g., complaint, inquiry, purchase). Which AI technique is BEST suited for this task?

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?

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?

A company wants to generate personalized product recommendations for each customer based on their purchase history and browsing behavior. Which approach is MOST appropriate?

What is the primary difference between narrow AI and general AI?

A team trains a model to predict customer lifetime value (CLV) using CRM data. The model's predictions are way off for new customers who have only been with the company for a month. Which factor is MOST likely contributing to this issue?

A company wants to use AI to automatically extract invoice numbers, dates, and totals from scanned invoices. Which AI capability is MOST relevant?

A data scientist notices that a churn prediction model has high variance: small changes in training data cause large changes in predictions. Which technique is BEST to address this?

Which statement best describes 'inference' in the context of machine learning?

A company wants to generate personalized marketing email content for each customer, including product recommendations and tailored copy. Which AI approach is BEST?

A company is developing a sentiment analysis model for customer reviews. The team wants to ensure the model is fair and does not exhibit bias. Which TWO actions are MOST effective? (Choose two.)

A sales team uses an AI model to prioritize leads. The model's predictions are not improving despite adding more data. Which THREE factors could explain this? (Choose three.)

A company wants to use AI to automatically route customer support emails to the appropriate department (billing, technical, sales). Which THREE AI capabilities are needed? (Choose three.)

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Which type of machine learning is used to predict customer churn based on historical labeled data?

A data scientist trains a lead scoring model that achieves 99% accuracy on training data but only 65% accuracy on a held-out test set. What is the most likely issue?

A sales team wants to prioritize leads that are most likely to convert. They have historical data on lead attributes and conversion outcomes. Which AI technique should be used?

What is the primary difference between narrow AI and general AI?

A company uses AI to automatically categorize customer support tickets into 'Billing', 'Technical', or 'General'. The model is trained on thousands of past tickets labeled by agents. What type of AI task is this?

A predictive model for opportunity scoring shows high precision but low recall. Which business impact is most likely?

Which of the following is an ethical concern when using AI to make decisions about customers?

A retailer wants to recommend products to customers based on their purchase history and browsing behavior. Which AI approach is most suitable?

A model trained on CRM data predicts customer lifetime value (CLV) with high accuracy, but when deployed, predictions are significantly off for new customer segments. What is the most likely cause?

A company wants to automatically extract key information like order numbers and dates from customer emails. Which NLP technique should be used?

What is the term for when an AI model produces confident but incorrect information, often in generative AI?

A company wants to use AI to analyze customer feedback from surveys and social media. Which TWO capabilities are most relevant?

A data scientist is building a churn prediction model. What THREE factors are most critical for model success?

Which TWO statements correctly describe predictive AI compared to generative AI?

Which type of machine learning is used when a model is trained on historical sales data that includes both input features and the known outcome (e.g., closed won/lost) to predict whether a new lead will convert?

A sales operations team wants to automatically categorize incoming support cases into predefined categories (e.g., Billing, Technical, General). The team has thousands of historical cases with correct category labels. Which AI approach should they use?

A data scientist trains a churn prediction model on CRM data that includes customer tenure, support ticket count, and last purchase date. The model achieves 95% accuracy on training data but only 60% on a holdout validation set. What is the most likely issue?

A company wants to use AI to automatically extract key information (e.g., invoice number, date, total amount) from scanned PDF invoices. Which AI capability should they use?

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

A customer service chatbot misinterprets user requests and often provides irrelevant answers. The development team wants to improve the chatbot's understanding of user intent. Which NLP component should they focus on?

A predictive model for lead scoring shows high precision but low recall. Which business impact is most likely?

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

A company uses an AI model to predict customer churn. The model's predictions are used to automatically assign discounts to high-risk customers. A customer complains about receiving a discount offer they did not request. Which ethical concern is most relevant?

A sales director wants to use AI to prioritize leads that are most likely to convert. The company has historical data on leads that includes whether they converted (yes/no) and various attributes. Which machine learning type should be used?

A data scientist notices that a sentiment analysis model performs well on general product reviews but fails to correctly classify negative sentiment in industry-specific jargon (e.g., 'the API is flaky'). The most likely cause is:

What does the term 'hallucination' refer to in the context of generative AI?

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.)

A financial services firm wants to use AI to detect fraudulent transactions. They have a dataset with 1% fraudulent and 99% legitimate transactions. Which THREE actions should they take to address class imbalance? (Select THREE.)

A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)

A marketing team wants to use AI to predict which leads are most likely to convert. The CRM contains historical lead data with conversion outcomes. Which type of machine learning should be used?

A company uses an AI model to classify customer support cases into categories. The model often misclassifies cases from a specific region, leading to longer resolution times. What is the most likely cause?

A sales manager wants to predict which deals are likely to close this quarter. The CRM has rich historical data on won/lost opportunities, deal amount, and sales stage. Which AI approach is best suited for this task?

A data scientist trains a model to predict customer churn. The model achieves 98% accuracy on training data but only 72% on test data. What issue is most likely occurring?

A customer service team wants to automatically route incoming emails to the appropriate department based on content. Which NLP capability is essential for this task?

An e-commerce company uses AI to provide product recommendations. The model suggests popular items but fails to personalize for individual users. Which type of learning could improve personalization?

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

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 generative AI chatbot sometimes produces factually incorrect responses about a company's products. What is this phenomenon called?

A company uses computer vision to scan receipts for expense reporting. The model performs well on high-resolution scans but poorly on blurry photos. Which improvement is most effective?

A company uses an NLP model to detect intent in customer messages. The model works well for English but fails for Spanish messages. What is the most likely cause?

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

A company wants to use AI to reduce customer churn. Which TWO approaches are most appropriate? (Select 2)

Which THREE factors are most important for ensuring the accuracy of an AI model in a CRM context? (Select 3)

A company is deploying an AI chatbot for customer service. Which THREE ethical considerations should be addressed? (Select 3)

A sales operations manager wants to use AI to predict which leads are most likely to convert. The CRM has historical data on past leads, including whether they were won or lost, along with demographic and behavioral attributes. Which machine learning type should be used?

A data scientist evaluates a churn prediction model. On the test set, the model achieves 99% accuracy, but the business reports that the model rarely flags actual churners. Which metric should the data scientist focus on to improve the model?

A customer support team wants to automatically categorize incoming cases into predefined categories such as Billing, Technical, or Account. Which NLP task is most appropriate?

A CRM administrator is planning to implement predictive AI for lead scoring. Which TWO actions should be taken to ensure data quality?

A company uses AI to generate personalized email content for marketing campaigns. They notice the AI occasionally produces factually incorrect statements. Which THREE actions should they take to mitigate this?

A sales team wants to use AI to get product recommendations for customers. Which TWO types of machine learning could be used?

A data scientist trains a model to predict customer churn. The model performs well on training data but poorly on test data. Which TWO issues are most likely?

A company wants to use AI to automatically extract key information (e.g., invoice number, date, total amount) from scanned invoices. Which THREE technologies should be combined?

A bank is implementing an AI system to approve small business loans. Which TWO ethical considerations should be addressed?