AI Fundamentals practice questions

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

Why wrong: This is a temporary workaround, not a fix.

Increase the sync interval to reduce the chance of duplicates.

Why wrong: Sync frequency does not cause duplicates.

Update the email client to the latest version.

Why wrong: The issue is within Salesforce, not the email client.

Manually override the lead scores for a sample of leads.

Why wrong: Manual overrides are not a best practice for improving model accuracy.

Increase the field history retention period for lead fields.

Why wrong: Field history retention does not retrain the model.

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

Why wrong: Field-level security does not affect model training.

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

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

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

Why wrong: Starting over is unnecessary; the model can be improved incrementally.

Increase the number of categories in the classification model.

Why wrong: Adding categories without tuning may degrade performance.

Enable stemming and synonyms.

Why wrong: Stemming and synonyms improve recall but do not directly enhance ranking.

Increase the number of indexed fields.

Why wrong: Increasing indexed fields can add noise and dilute relevance.

Decrease the index refresh frequency.

Why wrong: Decreasing index refresh frequency affects freshness, not the ranking algorithm.

Apply zero-shot learning without any examples

Why wrong: Zero-shot learning lacks context and does not restrict knowledge to the company's data.

Fine-tune the model on the company's data

Why wrong: Fine-tuning adjusts model weights but does not limit knowledge in real-time to the provided dataset.

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

Why wrong: Prompt engineering without grounding does not enforce the restriction to only use company data.

A fine-tuned large language model

Why wrong: Fine-tuning updates model weights but doesn't supply real-time data; it's a static improvement.

A prompt template in Prompt Builder

Why wrong: Prompt Builder helps construct prompts but does not retrieve data from external sources in real time.

A static knowledge base

Why wrong: A static knowledge base is not inherently integrated with real-time retrieval; it may be used but is not the essential component for RAG.

Sentiment analysis of customer emails

Why wrong: Sentiment analysis is typically in Einstein Service, not a core GPT for Sales feature.

Automated lead enrichment

Why wrong: Lead enrichment is a data processing task, not generative AI.

Generating Apex code

Why wrong: Apex code generation is a developer-focused feature, not sales-specific.

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

Why wrong: While sample size matters, quality and relevance are more critical.

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

Why wrong: Einstein works with Salesforce data directly; a data warehouse adds complexity.

Providing extensive user training on AI concepts.

Why wrong: User training helps adoption but not model accuracy.

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

Why wrong: Deleting data may reduce the training set and harm performance.

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

Why wrong: More recommendations do not improve relevance; quality over quantity.

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

Why wrong: Placement can help but data quality is the root cause.

Retrain the model to improve its accuracy for that lead.

Why wrong: Retraining for one lead is inefficient; insights are more direct.

Override the score manually to reflect their judgment.

Why wrong: Manual overrides reduce model trust.

Disable Einstein Lead Scoring for that lead record.

Why wrong: Disabling doesn't investigate; it removes scoring.

Einstein Segmentation

Why wrong: Segmentation groups audiences, does not generate content.

Einstein Prediction Builder

Why wrong: Prediction Builder creates predictive models, not generates content.

Einstein Analytics

Why wrong: Analytics provides dashboards, not content generation.

Create a custom scoring model specific to the business.

Why wrong: Standard model is used by default; custom is optional.

Deactivate any existing scoring rules.

Why wrong: Scoring rules can coexist with Einstein Opportunity Scoring.

Assign the Einstein Opportunity Scoring permission set to users.

Why wrong: This is needed for viewing scores, not for activation.

The user must have the 'Manage Einstein' permission.

Why wrong: The 'Manage Einstein' permission is for admins, not end users.

Field history tracking must be enabled for Opportunity.

Why wrong: Field history tracking is not required for Opportunity Scoring.

There must be at least 500 open opportunities.

Why wrong: The requirement is at least 50 closed-won opportunities, not open.

All models are available for scoring.

Why wrong: Only deployed models are available.

Case Deflection

Why wrong: Status is 'Error'; not available.

Opportunity Win Rate

Why wrong: Status is 'Training'; not yet available.

Select only the top three features based on correlation.

Why wrong: Feature selection is secondary to data quality.

Use a different algorithm like neural networks.

Why wrong: Algorithm choice is less impactful than data quality.

Increase the dataset size by collecting more customer records.

Why wrong: More data without quality can degrade performance.

The org must be on Enterprise Edition or higher.

Why wrong: It is available in Professional, Enterprise, and Unlimited editions.

The user must have an Einstein AI license.

Why wrong: Einstein Activity Capture is included in many Salesforce editions.

The user must manually enable email logging in personal settings.

Why wrong: It is configured by an admin, not the user.

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

Why wrong: This undermines the bot's purpose.

Increase the confidence threshold for intent matching to avoid misclassification.

Why wrong: Higher threshold may cause the bot to not understand more queries.

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

Why wrong: This limits the bot's capability.

It requires less training data than manual methods.

Why wrong: AI typically needs substantial data.

It eliminates all classification errors.

Why wrong: AI models are not perfect.

It can only classify images of specific disaster types.

Why wrong: Models can be trained for various types.

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

Why wrong: Einstein Recommendation Builder is designed for business users.

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

Why wrong: Real-time sync is not necessary for basic personalization.

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

Why wrong: The model can handle large catalogs.

Historical conversion data from closed opportunities.

Why wrong: That is used for lead scoring, not engagement.

Lead demographic information like industry and company size.

Why wrong: Demographics are not the primary input.

Social media posts and mentions of the company.

Why wrong: Social media is not part of Einstein Engagement Scoring.