AI Associate Practice Test 24 — 15 Questions

Question 1

An admin is using Einstein Prediction Builder to create a model predicting whether a support case will be escalated. Which THREE steps are required during the prediction creation process?

Accepted Answer

Select features (input fields) for the model. Selecting features (input fields) is a fundamental step in building a prediction model with Einstein Prediction Builder. These features are the independent variables that the model uses to learn patterns and make predictions about the target field (e.g., case escalation). Without selecting relevant features, the model cannot be trained effectively.

Answer

Run Einstein Discovery to validate the model

Answer

Configure Einstein Copilot to trigger the prediction

Question 2

A data scientist notices that the model accuracy drops significantly after retraining with new data. Upon inspection, they find that many records have missing values for a key feature. Which data quality improvement should be prioritized first?

Accepted Answer

Implement imputation for missing feature values.. The core issue is that missing values in a key feature introduce noise and bias, directly degrading model performance. Imputation (option A) is the most direct and impactful first step because it preserves the dataset size and feature set, allowing the model to learn from complete patterns. Without addressing missing data first, other quality improvements like normalization or feature reduction would be applied to corrupted data, failing to resolve the root cause.

Answer

Normalize the feature range.

Answer

Reduce the number of features.

Answer

Remove duplicate records.

Question 3

A sales rep wants to generate personalized email drafts to send to prospects based on opportunity data. Which feature should they use within Salesforce?

Accepted Answer

Sales GPT. Sales GPT is the correct feature because it uses generative AI to create personalized email drafts based on opportunity data, such as deal stage, product interest, and customer interactions. It leverages large language models to generate context-aware content directly within Salesforce, enabling the sales rep to quickly produce tailored communications without manual effort.

Answer

Einstein Conversation Insights

Answer

Einstein Opportunity Scoring

Answer

Einstein Email Insights

Question 4

A financial services firm deployed an AI model to automate loan approvals. The model was trained on historical loan data from the past 10 years, which shows that applicants from certain zip codes have higher default rates. After six months, the company's compliance team receives complaints that applicants from predominantly low-income neighborhoods are being rejected at a much higher rate than applicants from affluent areas, even when their financial profiles are similar. The model's overall accuracy remains high (95%), and the loan default rate has decreased by 15% since deployment. The company wants to address the ethical concerns without sacrificing performance. Which course of action should the company take?

Accepted Answer

Retrain the model with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints.. Retraining with balanced data mitigates the representation bias, addressing the root cause. Option A ignores the fairness issue. Option C removes a feature that may be a proxy for other factors, but it may not eliminate bias if other correlated features remain. Option D adjusts thresholds only for some groups, which could be considered unfair and may not be accepted by regulators.

Answer

Remove the zip code feature from the model inputs.

Answer

Adjust the approval threshold lower only for applicants from low-income neighborhoods.

Answer

Continue using the existing model since it has high accuracy and reduces defaults.

Question 5

A marketing manager wants to understand why a specific lead received a high score from an Einstein model. Which Salesforce feature provides the most detailed explanation?

Accepted Answer

Einstein Discovery. Einstein Discovery provides model explanations, including score factors (most influential fields) for individual predictions. This is the standard way to explain why a particular lead scored as it did.

Answer

Einstein Bots

Answer

Einstein Activity Capture

Answer

Einstein Automate

Question 6

A developer needs to use the Einstein Vision and Language Platform to classify images and extract named entities from text. Which THREE API capabilities should they use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is a core API capability of the Einstein Vision and Language Platform for extracting named entities (e.g., people, organizations, locations) from unstructured text. It directly addresses the requirement to extract named entities from text, making option A correct.

Answer

Fine-tuning BERT models

Answer

Deploying custom models on edge devices

Question 7

A data scientist notices that an Einstein model for predicting customer churn has unusually high accuracy on training data but performs poorly on validation data. Which data issue is the most likely cause?

Accepted Answer

A field containing future information (e.g., 'churn_date') was included in features. Including a field like 'churn_date' in the feature set introduces target leakage, where the model has access to information that would not be available at prediction time. This causes the model to appear highly accurate on training data (since it can directly 'see' the outcome) but fails to generalize to validation data where such future information is absent. In Salesforce Einstein, features must be strictly historical or static to avoid this data leakage issue.

Answer

The dataset has an imbalanced class distribution

Answer

The dataset contains many missing values

Answer

The model was trained on stale data from a different season

Question 8

A company wants to use AI to automatically categorize incoming cases into predefined types and priorities. Which feature should they configure?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically categorize incoming cases into predefined types and priorities using machine learning models trained on historical case data. It analyzes case attributes such as subject, description, and custom fields to predict the most appropriate case type and priority, enabling automated routing and prioritization without manual intervention.

Answer

Einstein Discovery

Answer

Einstein Prediction Builder

Answer

Einstein Article Recommendations

Question 9

A company wants to use Einstein Vision and Language Platform to automatically classify images of products and extract text from labels. Which TWO capabilities of the platform can be used for this requirement? (Select two.)

Accepted Answer

Image classification. Image classification is the correct capability because it allows the platform to automatically assign predefined labels (e.g., product categories) to images based on their visual content. This directly meets the requirement to classify images of products using the Einstein Vision and Language Platform.

Answer

Sentiment analysis

Answer

Text extraction (OCR)

Answer

Named Entity Recognition (NER)

Question 10

An administrator is setting up Einstein Next Best Action to recommend a discount offer to sales reps when an opportunity is at risk. The recommendation logic should consider the opportunity stage, amount, and close date. Which tool should the administrator use to define the recommendation strategy?

Accepted Answer

Strategy Builder with a Flow. Einstein Next Best Action uses Strategy Builder to define recommendation logic, which evaluates conditions like opportunity stage, amount, and close date to surface a discount offer. Strategy Builder allows administrators to create decision trees and rules that trigger actions, such as displaying a recommendation, without requiring code. A Flow can be embedded within Strategy Builder to execute complex logic or update records when the recommendation is accepted.

Answer

Einstein Prediction Builder

Answer

Einstein Discovery

Answer

Process Builder

Question 11

A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. Which issue is most likely occurring?

Accepted Answer

Overfitting. Overfitting: the model learns training data patterns too well, including noise, failing to generalize to new data.

Answer

Concept drift

Answer

Data leakage

Answer

Underfitting

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 service team trains an Einstein Bot on historical chat transcripts. After deployment, the bot frequently fails to understand customer intents. Which action is most likely to improve performance?

Accepted Answer

Add more diverse training phrases per intent. Adding more diverse training phrases per intent directly addresses the root cause of the bot's failure to understand customer intents: insufficient coverage of the varied ways customers express the same goal. Einstein Bot uses natural language understanding (NLU) models that rely on example phrases to learn intent patterns; increasing the diversity of these phrases improves the model's ability to generalize to unseen utterances, reducing misclassification.

Answer

Increase the confidence threshold to 90%

Answer

Use a hierarchical intent structure

Answer

Reduce the number of intents to two

Question 14

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 15

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.