AI Associate Practice Test 7 — 10 Questions

Question 1

A data scientist is building a model to recommend products. They notice the model rarely recommends certain categories to users from a specific demographic. What should the scientist do first to address potential bias?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Audit the training data for representation and bias. The first step in addressing potential bias is to audit the training data for representation and bias. This involves examining whether certain categories or demographics are underrepresented or overrepresented in the dataset, which can lead to skewed model recommendations. Without understanding the data composition, other interventions may fail to address the root cause of bias.

Answer

Increase the model's complexity to capture more patterns

Answer

Add more features about the demographic to improve accuracy

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Remove the demographic feature from the model

Question 2

A service manager wants to automatically classify incoming cases into Type, Priority, and Reason fields based on the case description. Which Einstein feature should they configure?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and populate standard case fields—such as Type, Priority, and Reason—by analyzing the unstructured text in the case description using natural language processing (NLP) and machine learning models. This eliminates manual data entry and ensures consistent categorization across incoming cases.

Answer

Einstein Recommendation Builder

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Einstein Next Best Action

Answer

Einstein Article Recommendations

Question 3

When training an Einstein Discovery model, which data type is not supported as a predictor field?

Accepted Answer

Multi-select picklist. Multi-select picklists are not supported as predictors in Einstein Discovery. Numeric, picklist, and date fields are supported.

Answer

Numeric

Answer

Picklist

Answer

Date

Question 4

A government agency uses an AI system to allocate resources for public services such as healthcare and education. The system is designed to optimize overall efficiency based on historical usage data. After deployment, it becomes clear that underserved regions with less historical data receive significantly less funding than well-served regions. The agency's mission is to promote equity. The system's performance metrics show high efficiency, but community leaders protest the unfair distribution. What should the agency do?

Accepted Answer

Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.. The scenario describes an AI system that optimizes efficiency but produces inequitable outcomes for underserved regions, conflicting with the agency's equity mission. The correct response is to redesign the system to incorporate fairness constraints that guarantee minimum resource levels for underserved regions, aligning the AI's objective with the agency's values. This balances efficiency with equity and addresses the root cause of the bias.

Answer

Abandon the AI system and use a manual, rule-based allocation system.

Answer

Collect more historical data from underserved regions before making adjustments.

Answer

Continue using the system as is, since it maximizes efficiency.

Question 5

A company wants to use AI to automatically analyze and classify images uploaded to Salesforce records, such as identifying product defects. Which Einstein feature should they use?

Accepted Answer

Einstein Vision. Einstein Vision is the correct feature because it is specifically designed for image recognition and classification tasks, such as analyzing uploaded images to identify product defects. It uses deep learning models to detect objects, classify images, and extract text from images, making it ideal for visual inspection use cases within Salesforce.

Answer

Einstein Prediction Builder

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Einstein Next Best Action

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Einstein Language

Question 6

A Salesforce admin wants to ensure that customer data used by Einstein features is not retained by Salesforce to train foundation models. Which component of the Einstein Trust Layer enforces this commitment?

Accepted Answer

Zero Data Retention. Zero Data Retention is the Einstein Trust Layer component that contractually and technically ensures customer data sent to the LLM is not stored or used to train Salesforce's foundation models. It is a core commitment of the Trust Layer and directly addresses the admin's requirement. Other components like audit trail and PII masking serve different purposes.

Answer

Audit trail

Answer

Toxicity detection

Answer

PII masking

Question 7

A company uses Einstein Prediction Builder to predict customer churn. The data includes account creation date, number of support cases, and average payment delay. After training, the model shows low confidence scores. What is the most likely cause?

Accepted Answer

The data contains many missing values or outliers for the selected fields.. Low confidence scores in Einstein Prediction Builder often stem from data quality issues such as missing values or outliers. These anomalies distort the model's ability to learn meaningful patterns, leading to uncertain predictions. Clean, complete data is essential for the model to produce high-confidence scores.

Answer

The training dataset includes fewer than 500 records.

Answer

The prediction field is set to a numeric type instead of a picklist.

Answer

The model was trained on data refreshed daily instead of weekly.

Question 8

A company wants to automatically log all sent emails and calendar events from Gmail into Salesforce without manual user action. Which feature should be configured?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture automatically syncs emails and calendar events from Gmail (and Outlook) into Salesforce records without manual user action. It is the native feature designed for automatic activity logging at scale, matching the requirement precisely.

Answer

Einstein Conversation Insights

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Einstein Email Insights

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Salesforce Inbox

Question 9

A sales team wants to use AI to get product recommendations for customers. Which TWO types of machine learning could be used?

Accepted Answer

Unsupervised learning. Supervised learning (E) is correct because product recommendation can be framed as a classification or regression task trained on labeled historical data, such as past customer purchases and ratings, to predict which products a customer is likely to buy. Unsupervised learning (D) is also correct because techniques like clustering and association rules can group customers with similar behavior or find products frequently bought together, enabling recommendations without explicit labels. Generative AI (A) is not the intended answer here since it creates new content rather than being the core ML paradigm for recommendation, and reinforcement learning (B) is typically used for sequential decision-making and reward optimization, not standard product recommendation. Computer vision (C) is unrelated because it processes images and video, not the tabular customer-product interaction data used for recommendations.

Answer

Generative AI

Answer

Reinforcement learning

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Computer vision

Question 10

A sales rep wants to automatically log emails and events from Outlook to Salesforce without manual work. Which Einstein feature should be enabled?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (C) is the correct feature because it automatically syncs emails and events from Microsoft Outlook (or Google Workspace) to Salesforce without requiring manual logging. It uses a background sync engine that captures email headers and calendar events based on configured rules, eliminating the need for users to manually log activities via add-ins or plugins.

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Answer

Einstein Lead Scoring