AI Associate Practice Test 40 — 15 Questions

Question 1

A financial services firm uses Einstein Next Best Action to offer credit products. The model recommends high-interest loans more often to minority groups. The AI Associate must mitigate this. What is the most effective approach?

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

Accepted Answer

Apply post-processing fairness adjustments to the recommendations.. Post-processing fairness adjustments directly modify the model's output to enforce demographic parity or equal opportunity, reducing biased recommendations without retraining the model. This approach is practical when the firm cannot easily change the underlying training data or model architecture, and it allows the AI Associate to intervene at the decision point to ensure fair lending practices.

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Remove the model and use a rule-based system.

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Use SHAP values to explain predictions.

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Add a disclaimer that recommendations may be biased.

Question 2

A healthcare organization uses Einstein Prediction Builder to predict patient no-show rates. They want to ensure that protected health information (PHI) like patient names and social security numbers are not used in the model. Which Salesforce Trusted AI principle or feature directly addresses this requirement?

Accepted Answer

Data minimisation. Data minimisation is a key principle: only use relevant features for the model and avoid including sensitive PII unnecessarily. The Einstein Trust Layer's PII masking also helps, but the principle that directly addresses using only relevant fields is data minimisation.

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Zero data retention

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Explainability

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Human oversight

Question 3

A company wants to automatically categorize incoming support cases by Type, Priority, and Reason. They have historical case data with these fields populated. Which Einstein feature should they configure?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is specifically designed to automatically categorize incoming support cases by fields like Type, Priority, and Reason using historical case data. It uses machine learning models trained on past case records to predict these categories, enabling automated routing and prioritization without manual rules.

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Einstein Lead Scoring

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Einstein Article Recommendations

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Einstein Prediction Builder

Question 4

A company uses Einstein Bots in Service Cloud. They want the bot to understand when a customer types 'I want to return a product' and route them appropriately. What must the admin configure first?

Accepted Answer

Define an intent named 'Return Product' with sample phrases. Einstein Bots use Natural Language Understanding (NLU) to interpret customer intents. Defining an intent named 'Return Product' with sample phrases trains the bot to recognize variations of that request and route the conversation accordingly. Without an intent, the bot cannot understand the customer's goal.

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Create a dialogue flow with a menu of options

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Enable Einstein Bots API for external integration

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Configure a handoff rule to a human agent for all queries

Question 5

A company uses Salesforce Data Platform to store customer data. They want to use this data to train an AI model for lead scoring, but they are concerned about data quality. Which step should they take first to ensure the data is suitable for AI?

Accepted Answer

Profile the data to identify missing values, outliers, and inconsistencies. Profiling the data is the essential first step because it systematically identifies missing values, outliers, and inconsistencies that degrade model performance. Without this baseline assessment, any subsequent normalization or labeling would be applied to flawed data, leading to unreliable lead scoring predictions. Salesforce Data Platform supports profiling via tools like Einstein Analytics or Data Prep, which scan fields for nulls, range violations, and format errors.

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Immediately normalize all numerical features

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Create a labeled dataset using historical lead outcomes

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Set up a data pipeline to stream data in real-time

Question 6

A Salesforce administrator is setting up Einstein Next Best Action for a marketing campaign. They want to ensure that customer consent preferences are respected. Which action should they take?

Accepted Answer

Configure the recommendation strategy to check the 'HasOptedOutOfEmail' field and exclude customers who have opted out.. Einstein Next Best Action recommendations must respect customer consent and communication preferences. Configuring the recommendation strategy to check the 'HasOptedOutOfEmail' field and exclude opted-out customers ensures compliance with consent requirements. This directly enforces the marketing preference at the point of recommendation generation.

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Include all customers and let the AI decide who to target, as consent is not relevant for recommendations.

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Anonymize customer data before running the AI model so consent is not needed.

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Use a data extension to store consents and manually update it weekly.

Question 7

A company wants to recommend products to visitors on their Experience Cloud site based on browsing behavior and past purchases. Which Einstein feature should be used?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it is specifically designed to deliver personalized product recommendations on Experience Cloud sites based on visitor browsing behavior and past purchase history. It uses AI to analyze customer interactions and transaction data to surface the most relevant products, directly matching the use case described.

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Einstein Next Best Action

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Einstein Article Recommendations

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Einstein Lead Scoring

Question 8

A marketing team wants to use AI to predict which leads are most likely to convert. The CRM contains historical lead data with conversion outcomes. Which type of machine learning should be used?

Accepted Answer

Supervised learning. Supervised learning uses labeled data (historical outcomes) to predict future outcomes, making it ideal for lead scoring.

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Unsupervised learning

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Generative AI

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Reinforcement learning

Question 9

A company uses Einstein Prediction Builder to predict customer churn. They notice the model is less accurate for a certain segment. What is the best approach to mitigate bias?

Accepted Answer

Retrain with balanced data. Retraining with balanced data directly addresses the root cause of bias: an imbalanced training set where the model underperforms for a specific segment. By ensuring the segment is adequately represented, the model learns more equitable patterns, reducing bias without sacrificing overall accuracy. This aligns with ethical AI practices in Einstein Prediction Builder, where data quality and representation are critical for fair predictions.

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Increase model complexity

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Add more features

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Remove the segment from training

Question 10

An admin is setting up Einstein Article Recommendations. Which type of data is essential for the model to learn which articles are relevant?

Accepted Answer

Article view events from users. Einstein Article Recommendations uses a collaborative filtering model that learns article relevance from user interaction signals, specifically article view events. The model analyzes patterns of which articles users view together to identify related content, making view events the essential training data for generating recommendations.

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Article publication dates

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User job titles

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Article author names

Question 11

A data scientist notices that a sentiment analysis model performs well on general product reviews but fails to correctly classify negative sentiment in industry-specific jargon (e.g., 'the API is flaky'). The most likely cause is:

Accepted Answer

The training data lacked examples of industry-specific language. The model was trained on general product reviews and therefore never learned the vocabulary and phrasing of industry-specific jargon like 'the API is flaky.' Sentiment models rely on learned lexical and contextual patterns; without domain examples, terms that carry negative sentiment in a technical context are treated as neutral or unknown. This is a classic domain-shift / out-of-distribution vocabulary problem.

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The model is overfitted to training data

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The model architecture is too simple

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Sentiment analysis cannot handle jargon

Question 12

A developer creates a custom AI model using Salesforce's AI platform. They want to ensure the model is fair. What should they do first?

Accepted Answer

Collect diverse training data. Collecting diverse training data is the foundational step to ensure fairness in an AI model because it helps mitigate bias at the source. Without diverse data representing all relevant groups, the model may learn skewed patterns that lead to discriminatory outcomes, regardless of subsequent testing or monitoring.

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Use a pre-trained model

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Test on a small sample

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Deploy and monitor

Question 13

Which type of machine learning is used to predict customer churn based on historical labeled data?

Accepted Answer

Supervised learning. Supervised learning trains on labeled historical data where each example has a known outcome (churned vs. not churned), allowing the model to learn the mapping from features to labels. Customer churn prediction is a classic binary classification problem solved with supervised algorithms like logistic regression, random forests, or gradient boosting.

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Reinforcement learning

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Unsupervised learning

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Self-supervised learning

Question 14

An admin is using Einstein Prediction Builder to predict whether a case will escalate. They have selected the prediction field (binary) and the dataset. After training, they notice the model uses all available fields. What should they do to improve model performance and reduce noise?

Accepted Answer

Select relevant features (input fields) and exclude irrelevant ones. Einstein Prediction Builder automatically includes all available fields by default during training, which can introduce noise and reduce model accuracy. By manually selecting only relevant features (input fields) and excluding irrelevant ones, the admin reduces dimensionality, minimizes overfitting, and improves the model's predictive performance. This feature selection step is a standard best practice in machine learning to ensure the model focuses on meaningful predictors.

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Increase the dataset size

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Use a different algorithm by default

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Change the prediction field to a different binary field

Question 15

A sales operations manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should they use to achieve this?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is specifically designed to automatically prioritize leads based on their likelihood to convert. It uses a predictive model that analyzes historical lead data and assigns a score (0–100) to each lead, enabling sales teams to focus on high-conversion leads without manual effort.

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Einstein Prediction Builder

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Einstein Discovery

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Einstein Opportunity Scoring