AI Associate AI Fundamentals — 10 Questions

Question 1

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 2

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 3

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?

Accepted Answer

Users must link their email client (Gmail or Outlook).. Einstein Activity Capture requires users to link their email client (Gmail or Outlook) to Salesforce via OAuth 2.0 authentication. This linkage allows the feature to automatically log emails and events from the connected email and calendar systems without manual user intervention.

Answer

The feature is only available for Sales Cloud.

Answer

All lead and contact fields must be visible to users.

Answer

Users must install a browser plugin.

Question 4

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?

Accepted Answer

Ensure that at least 500 records exist where the donor actually upgraded, and retrain the model.. Einstein Prediction Builder requires a minimum number of positive outcome records (upgrade events) to train a reliable model. With only 500 donors and likely far fewer upgrades, the model lacks sufficient signal. Ensuring at least 500 actual upgrade records provides the necessary positive examples for the algorithm to learn meaningful patterns, reducing randomness in predictions.

Answer

Increase the prediction window from 90 to 180 days to capture more upgrade events.

Answer

Use a different field as the prediction outcome, such as 'donation amount increase'.

Answer

Add more fields to the model, such as donor age and geographic location.

Question 5

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.

Answer

All features must be numerical and normalized.

Answer

The model needs to be retrained daily.

Question 6

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 7

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 8

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 9

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

Accepted Answer

Einstein AI (Data Cloud's machine learning capabilities). Einstein AI in Salesforce Data Cloud provides machine learning capabilities that automatically analyze customer behavior patterns, such as purchase history and engagement metrics, to generate predictive audience segments. This enables marketers to target specific groups without manual rule creation, leveraging Data Cloud's unified data model and Einstein's propensity scoring.

Answer

Prompt Builder

Answer

Einstein Discovery

Answer

Einstein GPT

Question 10

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

Accepted Answer

The model's average response time of 350ms is within typical performance expectations. The output shows an average response time of 350ms for the Einstein model, which is within the typical performance expectation of under 500ms for real-time AI inference in Salesforce. This indicates the model is responding efficiently without significant latency issues, making option C correct.

Answer

Token usage indicates the average response length is 300 tokens

Answer

The model experienced significant slowdowns

Answer

The model has a high error rate and needs retraining