AI Associate AI Fundamentals Practice Test 4 — 15 Questions

Question 1

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 deleting the training data and starting over is unnecessary; the model can be improved incrementally. Option D is wrong because increasing the number of 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 2

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?

Accepted Answer

The model correctly predicts most instances but has many false positives for that class.. High accuracy with low precision for a specific class indicates that while the model correctly classifies the majority of instances overall, it produces a high number of false positives for that class. Precision measures the proportion of positive identifications that were actually correct, so low precision means many of the predicted positive cases are false alarms. In Einstein Discovery, this trade-off is critical when optimizing for business outcomes where false positives are costly.

Answer

The model is overfitted to the training data.

Answer

The model correctly predicts most instances but has many false negatives for that class.

Answer

The model rarely predicts that class, leading to high accuracy.

Question 3

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?

Accepted Answer

Ensure the target variable is computed based on conversion status at exactly 30 days from creation.. The target variable for a time-based prediction model like Einstein Prediction Builder must be computed at a precise, consistent point in time—in this case, exactly 30 days from lead creation. If the target is computed at varying intervals, the model will learn incorrect patterns, as it cannot distinguish between leads that converted at 31 days versus those that never converted, leading to biased or invalid predictions.

Answer

Normalize all numerical features to a 0-1 range.

Answer

Remove leads that converted after 30 days.

Answer

Include only leads that were assigned to a sales rep.

Question 4

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?

Accepted Answer

The model training data size and distribution of values.. Low confidence scores in Einstein Object Detection typically indicate that the model has insufficient or imbalanced training data. Einstein AI requires a minimum number of labeled records (e.g., at least 100 per class) and a balanced distribution across predicted values to learn effectively. Without adequate data size and diversity, the model cannot generalize well, resulting in confidence scores below 80%.

Answer

The number of fields used as input features.

Answer

The API version used in the integration.

Answer

The permission set for the Einstein AI feature.

Question 5

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 6

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?

Accepted Answer

Implement a fallback to human agent for uncertain queries.. Implementing a fallback to a human agent for uncertain queries directly addresses the risk of incorrect or insensitive responses by ensuring that when the bot cannot confidently answer, the conversation is escalated to a human. This aligns with the principle of maintaining trustworthy AI, as it prevents the bot from generating potentially harmful or inaccurate responses and provides a safety net for complex or sensitive inquiries.

Answer

Use a larger training dataset from generic internet sources.

Answer

Allow the bot to generate responses only from pre-authored articles.

Answer

Disable sentiment analysis to avoid misinterpretation.

Question 7

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

Accepted Answer

Ensure the training data includes both converted and unconverted leads.. Option D is correct because Einstein Lead Scoring is a supervised machine-learning model that learns patterns distinguishing successful outcomes from failures, so the training dataset must contain a balanced mix of converted (won) and unconverted (lost) leads to build a predictive model. Option E is correct because the model's accuracy depends on continuous feedback: as sales reps update lead statuses and conversion outcomes in Salesforce, the model retrains on that fresh labeled data, so regularly reviewing and providing accurate conversion feedback keeps scores relevant and improves predictive quality. Option A is incorrect because restricting scoring to demographic data alone ignores behavioral and engagement signals (such as email opens, website visits, and activity history) that are among the strongest predictors in Einstein Lead Scoring. Option B is incorrect because a single fixed score threshold for all users ignores differing sales processes and territories; thresholds should be configurable per user or business unit. Option C is incorrect because disabling the model for low-volume leads would prevent scoring exactly where prioritization help is needed, and Einstein Lead Scoring requires sufficient historical data rather than being turned off for low-volume segments.

Answer

Include only demographic data for scoring.

Answer

Set a fixed score threshold for all users.

Answer

Disable the model for low-volume leads.

Question 8

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

Accepted Answer

Missing values in features should be handled appropriately.. Missing values in features can introduce bias or cause errors in the predictive model. Einstein Prediction Builder automatically handles missing data through imputation, but understanding how missing values are treated is essential for model accuracy, as inappropriate handling can distort relationships between features and the target outcome.

Answer

All input features must be numerical.

Answer

The model should be retrained only once after initial deployment.

Question 9

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)

Accepted Answer

The feature could be a surrogate for other correlated features.. A feature with high influence in an Einstein Discovery story may be acting as a proxy for other correlated features, meaning its apparent importance could be due to shared variance with other predictors. This is a known phenomenon in machine learning where collinearity can inflate a feature's influence score without it being uniquely causal.

Answer

The feature has a causal relationship with the outcome.

Answer

Removing the feature will significantly improve model performance.

Answer

The model is likely overfitted to that feature.

Question 10

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

Accepted Answer

The email domain 'acme.org' needs to be added to the Einstein Activity Capture allowed domains.. The log indicates that emails from 'acme.org' are being captured but not linked to contacts, which is a classic symptom of a missing domain in the Einstein Activity Capture allowed domains list. Without adding the domain, the system cannot associate emails from that domain with the correct contact records, even if the email addresses are valid. Option B directly addresses this configuration gap.

Answer

The connected email service is not authenticated for the organization.

Answer

The contact with ID 003B0000007v4ZW does not have a valid email address.

Answer

The user 'admin@mycompany.com' has exceeded their email capture quota.

Question 11

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?

Accepted Answer

Einstein Trust Layer. Einstein Trust Layer is the correct feature because it acts as a governance and security layer between Salesforce and the large language model (LLM). It automatically masks sensitive customer data (e.g., personally identifiable information) before the prompt is sent to the LLM and then unmasks the response, ensuring the AI never sees or exposes sensitive information. This directly addresses the admin's need to prevent responses containing sensitive data or violating company policies.

Answer

Prompt Builder

Answer

Data Cloud

Answer

Einstein Studio

Question 12

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?

Accepted Answer

Use merge fields with a conditional IF statement to include fields only when priority is High. Prompt Builder supports conditional merge fields using IF statements, allowing dynamic inclusion of case subject, description, and last 3 comments only when the case priority is High. This approach ensures the prompt is concise and relevant, avoiding unnecessary tokens or manual edits.

Answer

Hardcode example cases in the prompt and rely on the model to generalize

Answer

Include all fields in the prompt and use a token limiter to truncate the response

Answer

Create a static prompt and manually edit it for each High-priority case

Question 13

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?

Accepted Answer

The model is 65% confident that the response is accurate. The confidence score in AI models like Einstein GPT quantifies the model's internal certainty that its generated output is factually correct or contextually appropriate. A score of 0.65 means the model estimates a 65% probability that the response is accurate based on its training and inference algorithms, not that it matches training data or has a fixed length.

Answer

There is a 65% probability that the response exactly matches the training data

Answer

The response is 65% shorter than the optimal length

Answer

The model has a 65% likelihood of generating the same response again

Question 14

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 15

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?

Accepted Answer

Increase recall to reduce false negatives. With a precision of 0.9 and recall of 0.5, the model is highly selective but misses many actual leads (high false negatives). In lead scoring, false negatives mean lost sales opportunities, which is typically more costly than false positives. Therefore, increasing recall to capture more true positives is the most important improvement priority.

Answer

Increase precision to reduce false positives

Answer

Optimize for an F1 score of 0.7

Answer

Improve overall accuracy above 80%