AI Associate Practice Test 24 — 15 Questions

Question 1

A Salesforce admin has enabled Einstein Opportunity Scoring but notices that some opportunities are not being scored. The admin checks the data and finds that the opportunities have all required fields. What could be another reason for missing scores?

Accepted Answer

The org has fewer than 100 opportunities with activities. Einstein Opportunity Scoring requires a minimum of 100 opportunities with activities (e.g., emails, events, tasks) in the last 90 days to generate scores. Even if all required fields are present, the model needs sufficient historical data to calculate a predictive score. Option C correctly identifies this data volume threshold as the likely cause.

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Opportunity team members have not been assigned

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The org is on a Professional Edition

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The opportunities are missing custom fields

Question 2

A company wants to use AI to automatically recommend the best next action for service agents during a live chat with a customer. Which Einstein feature should be enabled?

Accepted Answer

Einstein Next Best Action. Option D, Einstein Next Best Action, is correct because it is specifically designed to recommend the optimal next step for service agents during live interactions. It uses AI to analyze the customer context, conversation history, and business rules to surface the most relevant action, such as offering a discount or escalating the case, directly within the agent's console.

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Einstein Article Recommendations

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Einstein Bots

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Einstein Reply Recommendations

Question 3

A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?

Accepted Answer

The model is unfair to Spanish-speaking customers.. The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.

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The model is not interpretable.

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The model has low accuracy for Spanish reviews.

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The model violates privacy regulations.

Question 4

An AI model for predicting employee performance is found to have a higher false positive rate for women than for men. What is the best course of action?

Accepted Answer

Investigate the cause and retrain the model to reduce bias. Option A is correct because a higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.

Answer

Lower the decision threshold for women to equalize false positive rates

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Proceed with deployment because the overall accuracy is acceptable

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Use the model but require manual review for all female candidates

Question 5

A sales team uses Einstein Lead Scoring. They notice leads from certain industries are always low-scored. What should they do?

Accepted Answer

Review training data for bias. Option D is correct because low scores for specific industries often indicate bias in the training data, where historical lead data may have underrepresented or mislabeled those industries. Reviewing the training data for bias allows the team to identify and correct such imbalances, ensuring the Einstein Lead Scoring model produces fair and accurate predictions across all segments.

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Retrain the model weekly

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Ignore the scores

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Use a different AI system

Question 6

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should be used?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it uses predictive models to automatically rank leads based on their likelihood to convert, enabling the sales manager to prioritize follow-up efforts. It analyzes historical lead data and engagement patterns to assign a score, directly addressing the requirement for automated prioritization without manual rules.

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Einstein Prediction Builder

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Einstein Relationship Health

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Einstein Activity Capture

Question 7

A company wants to use Einstein Bots to handle customer support queries. Which preparation is most important before deploying the bot?

Accepted Answer

Ensure the knowledge base articles are well-organized and cover common issues.. Option C is correct because Einstein Bots rely on a well-organized knowledge base to retrieve accurate answers for customer queries. Without properly structured articles covering common issues, the bot cannot effectively resolve tickets, leading to poor deflection rates and user frustration. This preparation directly impacts the bot's ability to understand and respond to natural language inputs using Salesforce's NLP and article matching.

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Train all agents on how to monitor the bot.

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Set up a new email channel for the bot.

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Create a custom object to store bot conversations.

Question 8

A retail company uses Einstein Prediction Service to forecast customer churn. To improve model accuracy, which data preparation step is most critical?

Accepted Answer

Clean the dataset by handling missing values and outliers.. Handling missing values and outliers is the most critical data preparation step for Einstein Prediction Service because the underlying gradient boosting models (like XGBoost) are sensitive to data quality issues. Missing values can introduce bias or cause the model to misinterpret patterns, while outliers can disproportionately influence split decisions, reducing predictive accuracy for churn scenarios.

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Select only the top three features based on correlation.

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Use a different algorithm like neural networks.

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Increase the dataset size by collecting more customer records.

Question 9

Based on the exhibit, what does the accuracy of 0.85 indicate?

Accepted Answer

85% of the model's predictions matched the actual outcomes.. Accuracy is defined as the ratio of correctly predicted instances (both true positives and true negatives) to the total number of predictions. An accuracy of 0.85 means that 85% of the model's predictions (whether 'won' or 'lost') matched the actual outcomes in the dataset. This is a standard classification metric that evaluates overall correctness, not just one class.

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85% of the features are important for prediction.

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85% of predictions that the opportunity will be won are correct.

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85% of opportunities in the training data were won.

Question 10

A system administrator receives an error when running a Data Cloud data transform: 'Row-level security settings are preventing access to the source data.' The admin has appropriate permissions. What is the most likely cause?

Accepted Answer

The source object has sharing rules that restrict access for the data transform's running user.. Option D is correct because Data Cloud data transforms run under a specific running user context, and row-level security (RLS) settings on the source object can restrict that user's access to rows, even if the admin has broad permissions. The error indicates that the running user lacks visibility to certain source data rows due to sharing rules or RLS policies, which is a common cause of this specific error message.

Answer

The target object has field-level security.

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The data stream is scheduled during maintenance.

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The data transform is set to run as the admin's default user.

Question 11

An admin reviews the Einstein service configuration JSON. Based on the exhibit, which statement is true?

Accepted Answer

Opportunities will not have Einstein scores. Option B is correct because the JSON exhibit shows that the Einstein Opportunity Scoring feature is disabled (the 'status' field is set to 'Disabled'), meaning opportunities will not have Einstein scores. This is determined by the configuration flag that controls whether Einstein scoring is active for opportunities in the org.

Answer

Lead scoring is disabled for the org

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The lead scoring model retrains daily

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The admin has not configured any scoring fields for leads

Question 12

Which TWO actions help ensure transparency in AI systems according to Salesforce's ethical AI guidelines?

Accepted Answer

Documenting model assumptions and limitations.. Option D is correct because documenting model assumptions and limitations is a core transparency practice under Salesforce's ethical AI guidelines. It ensures stakeholders understand the boundaries and potential biases of the AI system, enabling informed trust and accountability.

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Limiting access to model outputs to only a few people.

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Using complex deep learning models without explanation.

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Automatically retraining models weekly.

Question 13

An administrator is configuring a Salesforce AI model that uses historical sales data. The data includes fields like 'Amount', 'Close_Date', and 'Lead_Source'. What is the primary purpose of data preprocessing in this context?

Accepted Answer

To clean and transform data into a format suitable for model training. Data preprocessing is essential for AI models because raw historical sales data often contains missing values, inconsistent formats, and noise. Cleaning (e.g., handling nulls in 'Amount') and transforming (e.g., encoding 'Lead_Source' into numerical features) ensure the model can learn patterns effectively, directly impacting training accuracy and convergence.

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To generate visualizations for business stakeholders

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To increase the storage capacity of the database

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To enforce data access permissions for different user roles

Question 14

Which THREE factors should an AI Associate consider when evaluating a model for potential bias?

Accepted Answer

Whether features are correlated with protected attributes.. Option B is correct because if a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.

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The complexity of the model architecture.

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The date the model was last deployed.

Question 15

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.

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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