AI Associate Practice Test 29 — 15 Questions

Question 1

A company wants to build an autonomous AI agent using Agentforce that can handle customer inquiries about order status and reset passwords. Which components must be defined in Agent Builder? (Choose TWO.)

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

Accepted Answer

Topics. In Agent Builder, Topics are the primary mechanism for defining the scope of an autonomous agent's capabilities. Each topic represents a distinct customer intent (e.g., 'Order Status' or 'Password Reset') and contains the instructions, prompts, and conversation flow logic for handling that specific type of inquiry. Without defining Topics, the agent would have no structured way to route or process customer requests.

Answer

Entities

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Dialogues

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Intents

Question 2

A marketing team wants to recommend products to customers based on their past purchases and browsing behavior. Which type of AI is most appropriate?

Accepted Answer

Predictive AI. Product recommendation is a predictive AI task that uses historical data to predict which products a customer is likely to purchase.

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Generative AI

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Reinforcement learning

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Unsupervised learning

Question 3

A credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?

Accepted Answer

Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.. Replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias. Option D is wrong because transparency alone doesn't fix bias.

Answer

Remove zip code from the feature set and retrain.

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Keep zip code but add a fairness penalty to the loss function.

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Increase transparency by publishing the model's decision criteria.

Question 4

Refer to the exhibit. A data file for click-through model training has the above content. Which data quality issue is most critical to address before training?

Accepted Answer

Missing value in the Conversions column for the third row. Missing values in the Conversions column directly impact the supervised learning target variable. If the label (conversion) is missing for a training instance, the model cannot learn the correct mapping from features to outcome, leading to biased or incomplete training. This is a critical data quality issue that must be addressed before training, typically via imputation or row removal.

Answer

The header row is missing a column name for the last field

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Inconsistent date formats across rows

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Clicks column is an integer but may need scaling

Question 5

After applying a log transformation to a numeric feature, an Einstein model’s performance dropped significantly. What is the most likely cause?

Accepted Answer

The feature contained zero or negative values. Log transformation is undefined for zero or negative values because log(0) is negative infinity and log of a negative number is not a real number. In Salesforce Einstein, numeric features with such invalid transformed values can cause the model to fail or produce erratic results, leading to a significant drop in performance. This is the most likely cause given the symptom described.

Answer

The data volume was reduced by the transformation

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The feature was normally distributed after transformation

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The transformation introduced multicollinearity with other features

Question 6

A service team needs an AI bot that can handle complex customer issues requiring integration with external systems and escalate to a human agent when confidence is low. Which Einstein feature should be used?

Accepted Answer

Einstein Bots. Einstein Bots are designed to handle complex, multi-turn customer issues by integrating with external systems via Apex actions or REST APIs, and they include a built-in confidence threshold that triggers escalation to a human agent when the bot's confidence in resolving the query drops below a configurable level. This directly matches the requirement for handling complex issues with external system integration and low-confidence escalation.

Answer

Agentforce

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Einstein Case Classification

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Einstein GPT (Service GPT)

Question 7

A business wants to build a custom AI model to classify images of products and also create a chatbot that answers customer queries about those products. Which THREE Einstein features would they likely use? (Choose 3)

Accepted Answer

Einstein Bots. Einstein Bots is correct because it enables the creation of a chatbot that can answer customer queries about products using natural language processing and predefined dialogue flows, directly addressing the requirement for a customer query chatbot.

Answer

Einstein Forecasting

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Einstein Lead Scoring

Question 8

An admin is setting up Einstein Prediction Builder to predict whether a lead will convert. The admin has selected the prediction field and data set. What is the next step in the configuration wizard?

Accepted Answer

Select features (input fields) to train the model. The Einstein Prediction Builder wizard proceeds: select prediction field, select data set, select features, define prediction field, then train.

Answer

Train the model immediately

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Define the prediction explanation

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Choose the prediction score field

Question 9

A service manager wants to analyze recorded customer service calls to identify top keywords, measure talk-time ratios, and capture next steps automatically. Which Einstein product should they use?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct Einstein product for analyzing recorded customer service calls because it uses natural language processing (NLP) to transcribe calls, extract top keywords, measure talk-time ratios (e.g., agent vs. customer speaking time), and automatically capture next steps or action items. This product is specifically designed for post-call analytics on voice interactions, unlike other Einstein tools that focus on chatbots, email, or predictive modeling.

Answer

Einstein Bots

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Einstein Email Insights

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Einstein Discovery

Question 10

A company is using Einstein Discovery to predict customer churn. The model was created six months ago and has been making predictions. Recently, the model's accuracy has dropped significantly. The data scientist confirms that the data schema has not changed. What is the most likely reason for the drop in accuracy?

Accepted Answer

The model is suffering from concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate even though the data schema remains unchanged. In Einstein Discovery, models are trained on historical data, and if the underlying patterns of customer churn evolve (e.g., due to market shifts or new competitor behavior), the model's learned relationships become stale. Since the data schema is confirmed unchanged, concept drift is the most likely cause of the accuracy drop.

Answer

The data source is not being refreshed daily

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The model's features have become irrelevant

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The model needs to be retrained weekly instead of monthly

Question 11

A company wants to use AI to automatically assign a priority (High, Medium, Low) to incoming cases based on the case description and account history. Which feature should they configure?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is specifically designed to automatically assign categories or priorities (like High, Medium, Low) to incoming cases based on case descriptions and account history, using a pre-built AI model that learns from historical case data. This feature directly addresses the requirement without needing custom model training or flow configuration.

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Einstein Discovery with a recipe

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Einstein Next Best Action with a flow

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Einstein Prediction Builder with a three-class model

Question 12

Which TWO data preparation steps are critical for ensuring high-quality training data?

Accepted Answer

Removing duplicate records.. Duplicate records in a dataset can cause the model to overfit to repeated patterns, biasing the learned distribution and reducing generalization. Removing duplicates ensures each data point contributes equally to training, which is essential for robust model performance.

Answer

Increasing dataset size by adding noise.

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Normalizing all features.

Answer

Using only labeled data.

Question 13

Which TWO are best practices when implementing Einstein Recommendation Builder?

Accepted Answer

Use a dataset that reflects recent customer interactions. Einstein Recommendation Builder relies on recent customer interaction data to generate accurate and relevant product or content recommendations. Using stale or outdated data can lead to irrelevant suggestions, as the model learns from historical patterns that may no longer reflect current customer preferences or behaviors.

Answer

Disable negative feedback to simplify the model

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Include all available data, even if it is old or unrelated

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Use a single dataset that contains the entire history of the org

Question 14

A healthcare CRM administrator wants to use AI to recommend treatment plans based on patient data. Which combination of Salesforce Trusted AI principles is MOST critical to consider?

Accepted Answer

Accuracy and Empathy. In healthcare AI, accuracy ensures that treatment recommendations are clinically valid and based on reliable patient data, while empathy ensures the system respects patient well-being and avoids harmful or insensitive suggestions. Without accuracy, the model could recommend incorrect treatments; without empathy, it might ignore patient context or ethical considerations. These two principles together directly address the dual need for clinical correctness and human-centered care in treatment planning.

Answer

Safety and Honesty

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Honesty and Transparency

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Transparency and Safety

Question 15

A company uses Einstein GPT to generate email responses. They want to automatically audit generated responses for potentially harmful or biased language before sending. Which Salesforce feature should they use?

Accepted Answer

Einstein Trust Layer.. Einstein Trust Layer is the correct feature because it provides a governance layer that automatically audits AI-generated content for toxicity, bias, and harmful language before the email is sent. It intercepts the output from Einstein GPT and applies content safety filters, ensuring compliance with responsible AI practices without requiring manual review.

Answer

Permission Set.

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Einstein Analytics.

Answer

Data Mask.