AI Associate Practice Test 45 — 15 Questions

Question 1

A company uses Einstein Discovery to analyze their sales pipeline. They see a waterfall chart showing the expected revenue changes from one stage to the next. What does the waterfall chart primarily help identify?

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

Accepted Answer

The expected revenue at each stage and the changes between stages.. A waterfall chart in Einstein Discovery shows how each stage or factor contributes incrementally to a predicted outcome, starting from a baseline and ending at the final prediction. For a sales pipeline, it visualizes the expected revenue at each stage and the positive or negative deltas between stages, making it easy to see where revenue is gained or lost. This is the primary purpose of the waterfall visualization.

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The accuracy of the AI prediction compared to actuals.

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The improvement suggestions from Einstein Discovery.

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The top reasons why deals are won or lost.

Question 2

A sales rep wants to use Einstein Email Insights to prioritize which emails to respond to first. Which TWO statements about Einstein Email Insights are true?

Accepted Answer

It uses AI to identify high-priority emails. Option C is correct because Einstein Email Insights applies AI to analyze incoming email activity and engagement signals, identifying which messages are high-priority so the rep knows what to address first. Option E is correct because the feature surfaces those prioritized emails directly within Sales Cloud, giving the sales rep an actionable view of messages needing attention. The unmarked options do not belong: Einstein Email Insights is not a chatbot (A), it does not automatically send reply suggestions (B), and it does not log all emails to Salesforce automatically (D), as email logging is handled by other capabilities such as Einstein Activity Capture or Outlook/Gmail integration.

Answer

It is a chatbot that answers email-related questions

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It automatically sends reply suggestions

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It logs all emails to Salesforce automatically

Question 3

A lead scoring model trained on historical sales data is found to assign lower scores to leads from certain postal codes. What is the MOST likely cause?

Accepted Answer

The training data contains biased outcomes from past human decisions. The model assigns lower scores to leads from certain postal codes because the training data reflects historical human biases, such as sales representatives prioritizing leads from affluent areas. Machine learning models learn patterns from the data they are trained on; if past sales decisions were biased against certain regions, the model will replicate those biases. This is a classic case of bias in training data leading to biased model outcomes, not an inherent flaw in the algorithm itself.

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The model's algorithm is inherently biased against certain regions

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The model overfits to the training data

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The model was not trained long enough

Question 4

A company is implementing Einstein Lead Scoring and wants to ensure transparency for sales reps. According to Salesforce's Trusted AI principles, what should the company communicate to users about the AI-generated scores?

Accepted Answer

The scores are AI-generated, the key factors influencing each score are available, and reps should use their judgment.. Salesforce's Trusted AI principles emphasize transparency, accountability, and human oversight. For Einstein Lead Scoring, the company should inform users that scores are AI-generated, provide access to key factors influencing each score, and remind reps to use their judgment. This aligns with the principle of explainability.

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The scores are based on a secret algorithm to prevent gaming the system.

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Only managers can see the score factors to avoid confusion.

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The scores are 100% accurate and should be followed without question.

Question 5

A bank uses Einstein Discovery to generate insights about loan approval decisions. After deployment, they notice the model denies loans to a higher percentage of applicants from a certain postal code. Which action should be taken to ensure responsible AI?

Accepted Answer

Audit model outcomes for fairness across demographic groups and retrain if needed. Responsible AI requires auditing model outcomes for fairness across demographic groups, even when the disparity correlates with a non-protected attribute like postal code. In Einstein Discovery, postal code can act as a proxy for protected attributes such as race or socioeconomic status, and ignoring this could lead to discriminatory lending practices. Auditing allows the team to detect and mitigate bias, and retraining with fairness constraints ensures the model aligns with ethical AI principles.

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Ignore the discrepancy because postal code is not a protected attribute

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Retrain the model using only recent loan data

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Remove the postal code field from the model

Question 6

An organization uses Salesforce Data Cloud to unify customer data from multiple sources. They want to ensure that data lineage is tracked for AI models. Which practice supports data lineage?

Accepted Answer

Maintain metadata that records source, transformations, and dependencies.. Maintaining metadata that records source, transformations, and dependencies is the correct practice because data lineage for AI models requires a complete audit trail of where data originated, how it was transformed, and its dependencies. In Salesforce Data Cloud, this metadata is captured through the Data Catalog and Data Lineage feature, which tracks the flow of data from source objects through calculated insights and segments to AI model inputs, ensuring transparency and reproducibility.

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Use data partitioning to improve query performance.

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Implement role-based access control on datasets.

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Regularly run data profiling to check completeness.

Question 7

A company uses Einstein Bots to handle customer service inquiries. The bot often fails to understand complex requests, leading to escalations. Which improvement strategy is most effective?

Accepted Answer

Train the bot with additional intents and example phrases for complex scenarios.. Training the bot with additional intents and example phrases directly addresses the root cause of the bot's failure: insufficient training data for complex scenarios. By expanding the training corpus, the natural language understanding (NLU) model can better recognize and classify nuanced user inputs, reducing misclassifications and unnecessary escalations.

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Route all complex requests directly to human agents without bot interaction.

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Increase the confidence threshold for intent matching to avoid misclassification.

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Reduce the number of dialogue options to simplify the bot's logic.

Question 8

A retailer wants to recommend products to customers based on their purchase history and browsing behavior. Which AI approach is most suitable?

Accepted Answer

Supervised learning to predict purchase probability for each product. Recommending products based on purchase history and browsing behavior is a classic supervised learning problem: you train a model on historical user-item interactions labeled with purchase outcomes, then predict purchase probability for each candidate product and rank recommendations. Supervised learning (classification or ranking) directly fits this pattern.

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Reinforcement learning with real-time rewards

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Natural language generation to create product descriptions

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Computer vision to analyze product images

Question 9

A sales operations manager wants to predict which leads are most likely to convert to deals. The CRM has historical data on thousands of leads with outcomes (converted or not). Which type of machine learning should they use?

Accepted Answer

Supervised learning. Supervised learning is correct because the CRM dataset contains labeled historical examples — each lead has a known outcome (converted or not) — which is the defining characteristic of supervised classification. The model learns a mapping from input features (lead attributes) to the binary target label and can then predict conversion probability for new leads. This is a classic binary classification problem solved with algorithms like logistic regression, gradient boosting, or random forests.

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Unsupervised learning

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Deep learning

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Reinforcement learning

Question 10

A sales team uses Einstein Lead Scoring. They notice the model gives low scores to leads from certain industries. The AI Associate suspects bias. What should they do to validate?

Accepted Answer

Analyze the distribution of scores across industry segments.. Analyzing the distribution of scores across industry segments directly validates whether the model exhibits systematic bias. By comparing score distributions, the associate can identify if certain industries are consistently under-scored, which would indicate a biased pattern rather than random variation. This approach aligns with ethical AI practices that require transparency and fairness assessment before any model adjustments.

Answer

Run a holdout test to check prediction accuracy.

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Retrain the model with balanced data.

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Review the model's confidence intervals.

Question 11

A company wants to use Einstein Prediction Builder to predict customer churn. Which data preparation step is essential before building the model?

Accepted Answer

Define the prediction objective and the target date field.. Einstein Prediction Builder requires you to define the prediction objective (e.g., 'Will this customer churn?') and specify the target date field that marks the event. This step is essential as it tells the model what to predict and over what time window, enabling the automated feature engineering and model training process.

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Ensure the data is in a Salesforce connected data source like Data Cloud.

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Create a formula field to calculate the churn probability.

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Create a new custom object to store the prediction results.

Question 12

A retail company wants to increase average order value by showing personalized product recommendations on their website. They currently use Salesforce Commerce Cloud and have Einstein Recommendations enabled. However, they notice that recommendations are not reflecting recent customer interactions, such as items added to cart but not purchased. What should the administrator do to improve recommendation relevance?

Accepted Answer

Enable the 'Add to Cart' and 'Checkout' events in the Einstein Recommendations data integration to capture real-time data.. Einstein Recommendations relies on data integration events to capture real-time customer behavior. By enabling 'Add to Cart' and 'Checkout' events, the system can ingest recent interactions (e.g., items added to cart but not purchased) and adjust recommendations accordingly, improving relevance without requiring a full model reset.

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Reset the Einstein Recommendations model and retrain from scratch.

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Implement an Einstein Bot to directly ask customers about their preferences.

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Disable Einstein Recommendations for email and ads, leaving only web recommendations.

Question 13

Which data transformation is most appropriate for converting categorical variables into numerical format for a machine learning model?

Accepted Answer

One-hot encoding.. One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.

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Normalization.

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Principal component analysis.

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Standardization.

Question 14

A company uses Einstein Prediction Builder to recommend products. They notice the model often recommends high-priced items to users in affluent areas, potentially excluding others. What should the AI Associate do first?

Accepted Answer

Check the training data for representation and bias.. The correct first step is to check the training data for representation and bias because the model's tendency to recommend high-priced items to affluent areas suggests the training data may be skewed or contain historical biases. Einstein Prediction Builder relies on historical data to learn patterns, and if the data over-represents affluent users or under-represents others, the model will perpetuate those biases. Auditing the data for fairness and representation is the foundational step before any remediation, as per responsible AI practices.

Answer

Remove the model from production immediately.

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Ignore the issue because the model predictions are accurate overall.

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Add more features about customer income.

Question 15

Which two actions are consistent with Salesforce's ethical AI principles when deploying a custom AI model on Salesforce?

Accepted Answer

Document the model's intended use and limitations.. Option D is correct because Salesforce's ethical AI principles emphasize transparency and accountability, which require documenting a model's intended use, scope, and known limitations so stakeholders understand where and how the model should be applied. Option E is correct because providing a mechanism for users to challenge or appeal model decisions supports the principles of accountability, human oversight, and fairness, ensuring affected individuals have recourse when outcomes are disputed. Options A, B, and C are not consistent with these principles: restricting training to structured data is not a Salesforce ethical AI requirement, deploying a model to make decisions without human review undermines human-centered oversight, and optimizing solely for accuracy ignores other critical dimensions such as fairness, bias, and explainability.

Answer

Use only structured data for training.

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Use the model to make decisions without human review.

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Optimize for accuracy over all other metrics.