AI Associate AI Fundamentals — 50 Questions

Question 1

An admin notices that Einstein Activity Capture is logging duplicate email records. Which action should be taken to resolve this?

Accepted Answer

Verify that each contact has only one primary email address in Salesforce.. Einstein Activity Capture logs email activities by matching the sender or recipient email addresses to Contact records in Salesforce. If a Contact has multiple email addresses marked as primary, the system can become confused about which record to associate the email with, leading to duplicate log entries. Verifying that each Contact has only one primary email address resolves this by ensuring a unique mapping for email-to-record association.

Answer

Disable Einstein Activity Capture and re-enable it after 24 hours.

Answer

Increase the sync interval to reduce the chance of duplicates.

Answer

Update the email client to the latest version.

Question 2

A sales team is using Einstein Lead Scoring, but the scores for new leads seem inconsistent and not reflecting recent conversion patterns. The admin checks the model and finds it was trained three months ago. Which action should the admin take to improve model accuracy?

Accepted Answer

Retrain the Einstein Lead Scoring model with the latest lead data.. Retraining the Einstein Lead Scoring model with the most recent lead data will incorporate current conversion patterns, improving accuracy. Option B is incorrect because manually overriding scores undermines the model's machine learning basis and is not scalable. Option C is incorrect because increasing field history retention affects historical tracking, not model retraining or score accuracy. Option D is incorrect because field-level security controls data access for users, not model training inputs.

Answer

Manually override the lead scores for a sample of leads.

Answer

Increase the field history retention period for lead fields.

Answer

Adjust field-level security to allow the model to access more fields.

Question 3

A Salesforce admin notices that Einstein Case Classification in Service Cloud is suggesting categories that frequently require manual correction. Which action should the admin take first?

Accepted Answer

Provide feedback by correcting the predictions to improve the model.. Einstein models learn from user feedback; correcting predictions helps improve accuracy. Option A is wrong because routing before fixing classification may propagate errors. Option C is wrong because starting over is unnecessary; the model can be improved incrementally. Option D is wrong because adding categories without tuning may degrade performance.

Answer

Enable Einstein Case Routing to automatically route based on predicted categories.

Answer

Delete the training data and start over with a new model.

Answer

Increase the number of categories in the classification model.

Question 4

An organization uses Einstein Search to power a portal's search functionality. Users report that search results are not ranking relevant documents highly. Which configuration change is most likely to improve relevance?

Accepted Answer

Tune the search algorithm by promoting frequently accessed content.. Tuning the search algorithm to promote frequently accessed content can improve relevance ranking by prioritizing popular or authoritative documents. Option A is incorrect because stemming and synonyms improve recall but do not directly enhance ranking. Option C is incorrect because increasing indexed fields can add noise and dilute relevance. Option D is incorrect because decreasing index refresh frequency affects freshness, not the ranking algorithm.

Answer

Enable stemming and synonyms.

Answer

Increase the number of indexed fields.

Answer

Decrease the index refresh frequency.

Question 5

A company uses Einstein GPT to answer customer inquiries. To improve response relevance, the admin wants to restrict the AI's knowledge to only the company's product catalog and knowledge articles. Which approach should the admin use?

Accepted Answer

Use grounding data in the prompt template to provide relevant context. Correct: Grounding the prompt with specific data sources (Option A) restricts the AI's knowledge to the provided context (product catalog and knowledge articles). Option C (fine-tuning) adjusts model weights but does not limit knowledge in real-time. Option D (prompt engineering) without grounding does not enforce the restriction. Option B (zero-shot learning) lacks context and does not restrict knowledge.

Answer

Apply zero-shot learning without any examples

Answer

Fine-tune the model on the company's data

Answer

Use prompt engineering to instruct the model to only use company data

Question 6

A developer is implementing retrieval augmented generation (RAG) for a customer service bot. Which component is essential for supplying real-time data to the prompt?

Accepted Answer

A vector database for embedding and retrieval. Correct: A vector database stores embeddings and enables efficient similarity search, allowing retrieval of relevant context in real-time for RAG. Option A: A fine-tuned large language model is static and does not supply real-time data. Option B: A prompt template in Prompt Builder is used for structuring prompts, not for real-time data retrieval. Option C: A static knowledge base alone does not provide dynamic retrieval; it must be indexed and queried via a vector database for real-time augmentation.

Answer

A fine-tuned large language model

Answer

A prompt template in Prompt Builder

Answer

A static knowledge base

Question 7

Which TWO capabilities are available in Einstein GPT for Sales?

Accepted Answer

Summarizing call transcripts. Correct: D and E. Option A: Sentiment analysis of customer emails is more relevant to Einstein for Service, not Sales. Option B: Automated lead enrichment is not a primary feature of Einstein GPT for Sales; it focuses on content generation and summarization. Option C: Generating Apex code is a capability for developers within Salesforce, not specific to Einstein GPT for Sales. Option D: Summarizing call transcripts is a key feature of Einstein GPT for Sales, helping sales reps quickly review conversations. Option E: Generating personalized email drafts is another core capability, enabling efficient communication with prospects and customers.

Answer

Sentiment analysis of customer emails

Answer

Automated lead enrichment

Answer

Generating Apex code

Question 8

An organization is implementing Einstein AI for sales forecasting. They have multiple custom objects and complex approval processes. Which design consideration is most critical for ensuring accurate AI predictions?

Accepted Answer

Implementing model explainability to verify that predictions align with business logic.. Model explainability is critical to verify that the AI predictions align with the business logic, especially when complex approval processes and custom objects are involved. Option A is incorrect because the number of records required depends on various factors; 100,000 per prediction field is an arbitrary number and not a design consideration. Option B is incorrect because creating a separate data warehouse is not necessary; Einstein AI can work directly with Salesforce data. Option C is incorrect: while user training is beneficial, it does not directly address the accuracy of AI predictions; the core issue is ensuring the model's outputs are understandable and aligned with business rules.

Answer

Ensuring the data set includes at least 100,000 records per prediction field.

Answer

Creating a separate data warehouse to aggregate data from all objects.

Answer

Providing extensive user training on AI concepts.

Question 9

A nonprofit uses Einstein Recommendations to suggest donations. They notice that the recommendations are not relevant. Which best practice should they follow to improve relevance?

Accepted Answer

Verify that the Recommendation object has enough historical interaction data and that events are correctly tracked.. Einstein Recommendations rely on historical interaction data to learn user preferences and make personalized suggestions. Ensuring that the Recommendation object has enough interaction data and that events are correctly tracked is essential for accurate predictions. Option B is incorrect because deleting records older than 30 days removes valuable historical data, which reduces the model's ability to detect patterns and improve relevance. Option C is incorrect because increasing the number of recommended items does not inherently improve relevance; it can lead to decision fatigue and may serve less relevant suggestions. Option D is incorrect because displaying recommendations on every page, especially on a donation receipt page where users are not actively exploring, is unlikely to generate meaningful interactions and can degrade relevance.

Answer

Set a data retention policy to delete records older than 30 days to keep data fresh.

Answer

Increase the number of recommended items to 10 to give more choices.

Answer

Display recommendations on every page, including the donation receipt page.

Question 10

A sales manager sees that the Einstein Lead Score for a high-profile lead is low but expects it to be high. What should the manager do to investigate the discrepancy?

Accepted Answer

Use the Einstein Lead Score Insights to analyze the factors that contributed to the score.. Einstein Lead Score Insights provides transparency into the factors influencing the score, allowing the manager to investigate why the score is low. Option A is wrong because retraining the model is not a diagnostic step for a single lead. Option B is wrong because overriding manually defeats the purpose of an AI-driven score and is not a recommended practice. Option C is wrong because disabling scoring for a record is extreme and does not help investigate the discrepancy.

Answer

Retrain the model to improve its accuracy for that lead.

Answer

Override the score manually to reflect their judgment.

Answer

Disable Einstein Lead Scoring for that lead record.

Question 11

Which Einstein feature would allow a company to automatically generate personalized email content for marketing campaigns?

Accepted Answer

Einstein GPT. Einstein GPT uses generative AI to create personalized content at scale. Option A (Einstein Segmentation) is wrong because it focuses on audience segmentation, not content generation. Option C (Einstein Prediction Builder) is wrong because it builds predictive models, not content. Option D (Einstein Analytics) is wrong because it provides analytics, not content creation.

Answer

Einstein Segmentation

Answer

Einstein Prediction Builder

Answer

Einstein Analytics

Question 12

A sales operations admin wants to use Einstein Opportunity Scoring. Which two steps are required to activate Einstein Opportunity Scoring? (Select two answers.)

Accepted Answer

Enable Einstein in the Einstein Setup menu.. To activate Einstein Opportunity Scoring, you need to enable Einstein in the Einstein Setup menu (Option A) and have at least 100 opportunities won in the last 18 months (Option B). Option C is optional if you want a custom model, but not required. Option D is unnecessary, and Option E is for user access after activation. Therefore, the correct answers are A and B.

Answer

Create a custom scoring model specific to the business.

Answer

Deactivate any existing scoring rules.

Answer

Assign the Einstein Opportunity Scoring permission set to users.

Question 13

A sales manager wants to use Einstein Opportunity Scoring to prioritize deals. Which two requirements must be met?

Accepted Answer

There must be at least 50 closed-won opportunities in the last 365 days.. For Einstein Opportunity Scoring, two requirements must be met: Einstein must be enabled in Setup (Option E), and there must be at least 50 closed-won opportunities in the last 365 days to train the model (Option A). Option B is incorrect because the 'Manage Einstein' permission is an admin permission, not a user requirement. Option C is incorrect because field history tracking is not a prerequisite for Opportunity Scoring. Option D is incorrect because the requirement is for closed-won opportunities, not open ones.

Answer

The user must have the 'Manage Einstein' permission.

Answer

Field history tracking must be enabled for Opportunity.

Answer

There must be at least 500 open opportunities.

Question 14

Refer to the exhibit. A Salesforce admin runs the Einstein model list command and sees the output. Which model is currently available for use in predictive scoring?

Accepted Answer

Lead Score Model. (Lead Score Model) is correct because it has the status 'Deployed', indicating it is ready for production use. The other models are not available: 'Training' means the model is still being built, 'Error' means a failure occurred, and 'All models' is incorrect because only one is deployed.

Answer

All models are available for scoring.

Answer

Case Deflection

Answer

Opportunity Win Rate

Question 15

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

Accepted Answer

Clean the dataset by handling missing values and outliers.. Handling missing values and outliers is the most critical data preparation step for Einstein Prediction Service because the underlying gradient boosting models (like XGBoost) are sensitive to data quality issues. Missing values can introduce bias or cause the model to misinterpret patterns, while outliers can disproportionately influence split decisions, reducing predictive accuracy for churn scenarios.

Answer

Select only the top three features based on correlation.

Answer

Use a different algorithm like neural networks.

Answer

Increase the dataset size by collecting more customer records.

Question 16

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

Accepted Answer

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

Answer

The org must be on Enterprise Edition or higher.

Answer

The user must have an Einstein AI license.

Answer

The user must manually enable email logging in personal settings.

Question 17

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?

Accepted Answer

Train the bot with additional intents and example phrases for complex scenarios.. Training the bot with additional intents and example phrases directly addresses the root cause of the bot's failure: insufficient training data for complex scenarios. By expanding the training corpus, the natural language understanding (NLU) model can better recognize and classify nuanced user inputs, reducing misclassifications and unnecessary escalations.

Answer

Route all complex requests directly to human agents without bot interaction.

Answer

Increase the confidence threshold for intent matching to avoid misclassification.

Answer

Reduce the number of dialogue options to simplify the bot's logic.

Question 18

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

Accepted Answer

It reduces manual effort and speeds up damage assessment.. Einstein Vision automates the classification of disaster images, significantly reducing the manual effort required for damage assessment. By processing large volumes of images rapidly, it accelerates the time to insight, enabling faster response and resource allocation. This aligns with the core benefit of AI: augmenting human effort with speed and scale.

Answer

It requires less training data than manual methods.

Answer

It eliminates all classification errors.

Answer

It can only classify images of specific disaster types.

Question 19

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

Accepted Answer

The model has not been trained with enough user behavior data.. Einstein Recommendation Builder relies on user interaction data to personalize. If insufficient data exists, recommendations become generic. Option A is correct. Option B is wrong because real-time sync is not required. Option C is wrong because the builder can work without a data scientist. Option D is wrong because the model can recommend products beyond categories.

Answer

The company did not hire a data scientist to tune the model.

Answer

The recommendation engine is not syncing in real-time with the website.

Answer

The product catalog is too large for the model to process.

Question 20

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

Accepted Answer

Lead interaction history with emails and web activity.. Einstein Engagement Scoring analyzes lead interactions (email opens, clicks, web visits) to calculate engagement scores. Option A is correct. Option B is wrong because demographic data is not the primary input. Option C is wrong because historical conversion data is used for predictive scoring, not engagement. Option D is wrong because social media data is not a direct input.

Answer

Historical conversion data from closed opportunities.

Answer

Lead demographic information like industry and company size.

Answer

Social media posts and mentions of the company.