AI Associate Practice Test 50 — 15 Questions

Question 1

A sales team wants to automatically generate personalized follow-up emails after each meeting. Which Salesforce AI feature should they use?

Accepted Answer

Sales GPT. Sales GPT is the correct feature because it is specifically designed to generate personalized, AI-driven content like follow-up emails directly within Salesforce. It uses generative AI to create context-aware drafts based on meeting data, such as notes or summaries, without requiring custom prompts or additional configuration.

Answer

Prompt Builder

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Einstein Activity Capture

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Einstein Copilot

Question 2

A data scientist wants to use Einstein Discovery to analyze customer churn. They want to understand which factors contribute most to churn and get actionable suggestions. Which THREE outputs does Einstein Discovery provide?

Accepted Answer

Improvement suggestions (recommended actions). Einstein Discovery provides improvement suggestions (recommended actions) that guide data scientists on specific changes to reduce churn. These actionable insights are generated from the predictive model's analysis of historical data, offering concrete steps like 'increase engagement frequency' or 'offer discount' to improve outcomes.

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Prediction score for each record

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Waterfall chart (visualization of step-by-step impact)

Question 3

A data analyst wants to use Einstein Discovery to understand factors driving case resolution time. Which TWO outputs does Einstein Discovery provide?

Accepted Answer

Waterfall charts. Waterfall charts are a standard output of Einstein Discovery, used to visualize the contribution of different factors to a target outcome—in this case, case resolution time. They show how each predictor adds or subtracts from the predicted value, making it easy to identify the most influential drivers.

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Bot analytics

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Case classifications

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Lead scores

Question 4

A company wants to use AI to automatically extract invoice numbers, dates, and totals from scanned invoices. Which AI capability is MOST relevant?

Accepted Answer

Computer vision with entity extraction. Computer vision can process images of documents, and entity extraction (often part of NLP) identifies specific data points. Sentiment analysis is for emotions, intent detection for purpose, and generative AI for content creation.

Answer

Intent detection

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Generative AI to create invoices

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Sentiment analysis

Question 5

Refer to the exhibit. A data transformation configuration is shown. Which of the following describes the outcome of applying this transformation?

Accepted Answer

'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1.. The transformation configuration applies a one-hot encoder to the 'color' categorical column, creating multiple binary columns, and applies a standard scaler to the 'price' and 'weight' numerical columns, centering them to mean 0 and scaling to unit variance. This is a common preprocessing pipeline that handles mixed data types appropriately.

Answer

The transformation is invalid because one-hot encoding cannot be combined with scaling.

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Only 'color' is transformed; 'price' and 'weight' are unchanged.

Answer

'color' is scaled to [0,1] and 'price', 'weight' are one-hot encoded.

Question 6

A company wants to use Einstein Article Recommendations to suggest knowledge articles to support agents. What is a prerequisite for this feature?

Accepted Answer

Knowledge articles must be created and published.. Einstein Article Recommendations requires that knowledge articles are created and published in the Salesforce Knowledge base. The feature uses natural language processing (NLP) to match the context of a case or conversation with published articles, so unpublished or draft articles cannot be recommended. Without published articles, the AI model has no content to analyze or suggest.

Answer

Articles must be of a specific type, such as FAQ.

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The org must be enabled for Einstein features.

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A case must be open for the recommendation to appear.

Question 7

A global manufacturing company uses Sales Cloud and has implemented Einstein Opportunity Scoring to prioritize deals. The scoring model was trained on historical data and initially performed well. Over the past month, the scores have become less accurate, with many high-scoring opportunities not closing and some low-scoring ones closing. The admin notices that the sales team has been using a new discounting strategy that heavily influences deal outcomes. The admin wants to improve model performance without manual intervention. Which action should the admin take?

Accepted Answer

Retrain the Einstein Opportunity Scoring model with the latest opportunity data including discount information.. Retraining the Einstein Opportunity Scoring model with the latest opportunity data, including discount information, allows the machine learning model to automatically learn the new patterns introduced by the sales team's discounting strategy. This aligns with the AI Associate principle that models must be retrained on current data to maintain accuracy when business processes change, without requiring manual intervention.

Answer

Manually adjust the field weights for discount-related fields in the model.

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Run a data quality report to identify and clean missing discount data.

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Create a custom field for discount percentage and add it to the model.

Question 8

An admin wants to use Einstein GPT to generate personalized sales emails for reps. They need to ensure the emails include the latest product inventory data from an external system. Which approach should they take?

Accepted Answer

Create a Prompt Template in Prompt Builder that calls an Apex class to fetch inventory data, then use it in Sales GPT. It leverages Prompt Builder to create a custom prompt template that calls an Apex class, enabling real-time retrieval of external inventory data via an API callout. This ensures the personalized sales emails generated by Einstein Sales GPT always include the latest product inventory without manual intervention or stale data.

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Embed a report snapshot in the email using Einstein Analytics

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Use Einstein Sales GPT with a standard template and manually update the inventory data weekly

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Use Einstein Copilot to ask the rep to check inventory before sending each email

Question 9

A data analyst is evaluating data quality for an Einstein model. Which TWO dimensions are most critical for model accuracy?

Accepted Answer

Accuracy. Completeness (no missing values) and accuracy (correct values) are fundamental to model performance.

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Uniqueness

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Consistency

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Timeliness

Question 10

A data scientist discovers that an AI model used for loan approval predicts high default risk disproportionately for a specific demographic group. What is the first step to address this issue?

Accepted Answer

Audit the training data for bias. Auditing the training data for bias helps identify if the model learned biased patterns. Option A is wrong because retraining with more data may not solve the bias if the new data also contains bias. Option C is wrong because removing demographic features may not eliminate bias if other correlated features exist. Option D is wrong because changing the algorithm does not address biased data.

Answer

Use a different algorithm

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Remove demographic features from the model

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Retrain the model with more data

Question 11

A data architect is designing a data model for Einstein Discovery. The data includes categorical variables with high cardinality (e.g., postal codes). What is the best practice to handle such features?

Accepted Answer

Group them into higher-level categories (e.g., region).. Grouping high-cardinality categories into broader categories reduces overfitting and improves model stability.

Answer

Encode them as one-hot vectors.

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Exclude them from the model.

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Use the raw values without transformation.

Question 12

A company's Einstein Discovery model for customer lifetime value shows a significant correlation between predicted value and customer's postal code. The company is concerned about ethical implications. What is the most appropriate response?

Accepted Answer

Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints. (Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints) is correct because postal code can be a proxy for race or income. Option A (removing postal code outright) may not be straightforward. Option C (ignoring correlation as coincidental) is unethical. Option D (adding more demographic data) could increase bias.

Answer

Remove the postal code field from the model immediately

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Add more demographic data to the model to improve its accuracy

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Ignore the correlation since the model is predicting business value, not demographic attributes

Question 13

A company is subject to GDPR and wants to use customer purchase history to predict future buying behavior. What is the primary requirement they must fulfill under GDPR when using customer data for AI predictions?

Accepted Answer

Obtain explicit consent from customers to use their data for AI predictions. GDPR requires a lawful basis for processing personal data, such as consent or legitimate interest. The right to explanation is also relevant for automated decisions, but the primary requirement is obtaining a lawful basis (e.g., consent) before using the data.

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Ensure the AI model is hosted in the same country as the customer

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Provide a discount to customers who opt in to data usage

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Anonymize all customer data before training the model

Question 14

An AI system used for recruitment has been found to be biased. Which THREE steps should be taken to address this? (Choose three.)

Accepted Answer

Audit the training data for bias. Auditing training data, retraining with balanced data, and monitoring outcomes are essential corrective actions.

Answer

Deploy the model without changes

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Remove demographic data from the model

Question 15

A company deployed an AI chatbot for customer service. After a week, they receive complaints that the chatbot responds differently based on customer accent. The ethical issue is most likely due to:

Accepted Answer

Bias in the training data leading to discriminatory behavior. The chatbot's differing responses based on accent indicate bias in the training data or model. Option D (bias in training data leading to discriminatory behavior) is correct because AI models learn from data, and if the data contains accents correlated with negative outcomes, the model perpetuates that. Option A (insufficient compute resources) is unrelated. Option B (lack of chatbot personality) is not ethical. Option C (user interface design) is not the cause.

Answer

Lack of personality in the chatbot responses

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Insufficient computational resources allocated to the chatbot

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Poor user interface design