AI Associate AI Fundamentals Practice Test 3 — 15 Questions

Question 1

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?

Accepted Answer

The dataset must be balanced between churned and non-churned customers.. Einstein Prediction Builder uses automated machine learning (AutoML) to train models, and class imbalance is a critical factor that directly impacts model accuracy. If the dataset is highly skewed (e.g., 95% non-churned, 5% churned), the model may achieve high accuracy by simply predicting the majority class, but it will fail to identify actual churners. Einstein Prediction Builder includes built-in handling for imbalanced data, but the user must ensure the dataset is reasonably balanced or use techniques like oversampling to improve predictive performance.

Answer

The dataset size is too small for reliable predictions.

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All features must be numerical and normalized.

Answer

The model needs to be retrained daily.

Question 2

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?

Accepted Answer

The sender or recipient is not a Salesforce user with a license.. Einstein Activity Capture relies on Salesforce user licenses to associate email activities with records. If the sender or recipient is not a licensed Salesforce user, the system cannot link the email to a user identity, causing it to be skipped during capture. This is the most common reason for missing emails because the feature is designed to log activities only for licensed users.

Answer

The emails were sent from a non-Outlook or Gmail client.

Answer

The Einstein Activity Capture feature is disabled for that specific user.

Answer

The emails contain attachments that exceed the size limit.

Question 3

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.

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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 4

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 5

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 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.

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

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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.. Einstein Lead Scoring requires training data that includes both converted and unconverted leads to build a predictive model that can distinguish between leads likely to convert and those that are not. Without unconverted leads, the model cannot learn the negative patterns, leading to biased predictions and poor accuracy.

Answer

Include only demographic data for scoring.

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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.

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

Accepted Answer

The model has detected data drift, indicating the training data may no longer represent current patterns.. The exhibit shows a data drift alert from Einstein Prediction Builder, which indicates that the statistical properties of the input data have changed over time. This is the admin's primary concern because a model trained on outdated patterns will produce unreliable predictions, even if its accuracy or AUC were initially high. Data drift directly undermines the model's validity in production.

Answer

The model accuracy is too low for production use.

Answer

The AUC is low, so the model is not better than random.

Answer

The model uses too few features to be reliable.

Question 11

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?

Accepted Answer

The account is predicted to convert, but the model's confidence is relatively low, suggesting the prediction should be verified.. The Einstein Score is a predictive lead scoring model that outputs a conversion probability (0 to 100), and the confidence score (0 to 1) indicates the model's certainty in that prediction. A confidence of 0.65 is below the typical threshold (e.g., 0.75 or higher), meaning the prediction is less reliable and should be manually verified before acting on it.

Answer

The score indicates the account has been contacted 78 times, with a 65% satisfaction rate.

Answer

The account is predicted to have a 78% chance of converting, and the model is 65% confident in that prediction.

Answer

The account is in the top 78% of scoring accounts, with a 65% chance of being accurate.

Question 12

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 13

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 14

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 15

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

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The response is 65% shorter than the optimal length

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