AI Associate AI Fundamentals — Timed 100 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

Question 11

An admin creates a predictive model in Einstein Prediction Builder to forecast customer churn. The model shows high accuracy on test data but poor performance in production. What is the most likely cause?

Accepted Answer

Data drift between training and production environments. The model's high accuracy on test data but poor performance in production is a classic symptom of data drift. In Einstein Prediction Builder, the model was trained on historical data that may not reflect current customer behavior patterns, leading to a mismatch between training and production distributions. This is not a model training issue but a data environment shift.

Answer

Improper feature scaling in the training pipeline

Answer

The model is overfitted to the training data

Answer

Target leakage in the training dataset

Question 12

A marketing manager wants to use AI to recommend next-best actions for customers based on their previous purchases. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Recommendations. Einstein Recommendations is the correct choice because it is specifically designed to analyze customer purchase history and behavioral data to suggest next-best actions, such as products or content, in real time. It uses collaborative filtering and deep learning models to generate personalized recommendations, directly matching the use case of suggesting actions based on previous purchases.

Answer

Einstein Discovery

Answer

Einstein Bot

Answer

Einstein Prediction Builder

Question 13

An admin sets up Einstein Sentiment scoring for case comments. After a week, they notice that most comments are scored as 'Neutral' even when customer sentiment is clearly negative. What should the admin check first?

Accepted Answer

Retrain the sentiment model with industry-specific training data. Einstein Sentiment is a pre-trained model that may not accurately interpret sentiment in industry-specific contexts. Retraining the model with domain-specific training data (e.g., using Salesforce's Intent and Sentiment API with custom datasets) adjusts the model to recognize sentiment nuances in that industry, improving accuracy beyond the generic baseline.

Answer

Increase the model confidence threshold to reduce false positives

Answer

Switch to Einstein Bot for sentiment analysis

Answer

Verify that case comments contain at least 50 words each

Question 14

Which TWO of the following are key principles of trustworthy AI according to Salesforce's AI ethics guidelines?

Accepted Answer

Transparency. Option C (Transparency) is correct because Salesforce's AI ethics guidelines emphasize that AI systems should be transparent, meaning their capabilities, limitations, and decision-making processes should be openly communicated to users and stakeholders. Option D (Explainability) is correct because the guidelines require that AI decisions be explainable, so users can understand how and why a particular output or recommendation was produced. These two principles work together to build trust by ensuring AI systems are not opaque black boxes. Option A (Profitability) is not an ethical principle of trustworthy AI but a business objective, and it is not listed among Salesforce's core AI ethics principles. Option B (Complexity) is not a principle; in fact, unnecessary complexity undermines transparency and explainability. Option E (Accountability) is a legitimate AI ethics principle in many frameworks, but it is not one of the two marked correct answers for this specific question.

Answer

Profitability

Answer

Complexity

Answer

Accountability

Question 15

A company wants to deploy an Einstein AI model that uses sensitive customer data. Which practice should they follow to comply with data privacy regulations?

Accepted Answer

Use Einstein Trust Layer features to mask personally identifiable information (PII) in the model.. The Einstein Trust Layer provides built-in capabilities to automatically mask or redact personally identifiable information (PII) before data is sent to the underlying AI model, ensuring compliance with data privacy regulations like GDPR and CCPA without requiring manual data handling. This feature operates at the platform level, intercepting data in transit and applying masking rules based on predefined patterns, so sensitive customer data is never exposed to the model or stored in its training logs.

Answer

Store all sensitive data in an external data lake and connect via APIs.

Answer

Obtain explicit consent from data subjects before using their data in AI models.

Answer

Limit the data used for training to only essential fields.

Question 16

Refer to the exhibit. A Salesforce admin configured the Einstein Trust Layer policy shown. What is the effect of this policy on AI model usage?

Accepted Answer

AI models receive masked data for those fields, but feature importance insights are still available.. The Einstein Trust Layer policy configured to mask specific fields ensures that sensitive data is replaced with masked values before being sent to the AI model. This preserves data privacy while still allowing the model to generate predictions and insights. Feature importance insights remain available because they are computed from the masked data, not the original values.

Answer

All fields in the org will be masked to protect customer privacy.

Answer

AI models will not be able to use the configured fields, and model insights are disabled.

Answer

AI models can still use the fields but feature importance insights are blocked.

Question 17

A financial services company is deploying Einstein Prediction Builder to predict customer churn. The data includes both numerical and categorical fields. Which step is essential to ensure the model is not biased against protected attributes like race or gender?

Accepted Answer

Exclude any protected attributes from the training data and ensure the model does not use correlated proxies.. Excluding protected attributes like race or gender from the training data and ensuring the model does not use correlated proxies is essential to prevent bias in Einstein Prediction Builder. This approach directly removes the risk of the model learning discriminatory patterns based on these attributes, as the platform relies on the data provided and does not automatically enforce fairness constraints. Including such attributes or relying on built-in fairness would not guarantee unbiased predictions because the model could still infer protected characteristics from correlated features.

Answer

Include race and gender as predictors to allow the model to adjust for them.

Answer

Rely on the model's built-in fairness constraints.

Answer

Use a deep learning algorithm to automatically handle bias correction.

Question 18

A global retail company with 50,000+ users has deployed Einstein Activity Capture across Sales and Service Clouds. After two weeks, the VP of Sales reports that only 60% of emails sent from Outlook are being logged in Salesforce. Users have installed the Einstein Activity Capture plugin and have the correct permission set. The admin has verified that the email logging settings are enabled for all users. The company uses Exchange Online. What should the admin investigate first?

Accepted Answer

Confirm that the Salesforce connected app is authorized in Exchange Online.. Einstein Activity Capture for Exchange Online requires an OAuth 2.0 connected app authorization in Exchange Online to access mailbox data. Even if the plugin is installed and permissions are set, without the connected app being authorized, the service cannot retrieve email metadata, causing a significant logging gap. This is the first thing to verify since it is a common misconfiguration during initial setup.

Answer

Review the Exchange Online mailbox audit logs to see if emails are being accessed.

Answer

Check the email synchronization frequency in the Einstein Activity Capture settings.

Answer

Ensure that all users have the 'Email Integration' permission enabled in their profiles.

Question 19

A company uses Einstein Prediction Builder to create a custom model that predicts whether a support case will be escalated. The model is built and published, but when the admin looks at the case record, the prediction field shows 'No Prediction' for all cases. The prediction is set to run on case creation and update. What should the admin check?

Accepted Answer

The model's confidence threshold is too high, causing no predictions.. When Einstein Prediction Builder shows 'No Prediction' for all cases, a common cause is that the model's confidence threshold is set too high. The prediction field only displays a value when the model's confidence in its prediction exceeds that threshold; if no case meets the threshold, all predictions are suppressed. The admin should lower the confidence threshold in the model settings to allow predictions to appear.

Answer

The prediction field is not added to the case page layout.

Answer

The model was not activated for all record types.

Answer

The data prep steps included all required fields.

Question 20

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.. Option A is correct because Einstein Opportunity Scoring requires a sufficient volume of historical data to train its model, specifically at least 50 closed-won opportunities within the last 365 days. Option E is correct because Einstein features must first be enabled in Setup (Einstein > Einstein Features / Settings) before Opportunity Scoring can be turned on and used. Option B is not required for using scoring; the 'Manage Einstein' permission is for administering Einstein features, not for a sales manager to consume scores. Option C is not a prerequisite; field history tracking is not required to generate Opportunity Scores. Option D is incorrect because the requirement is about closed-won historical deals (at least 50), not 500 open opportunities.

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.