AI Associate Practice Test 2 — 10 Questions

Question 1

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Accepted Answer

Summarizing case conversations from service interactions.. Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Answer

Generating report charts and dashboards.

Answer

Creating workflow rules based on user behavior.

Question 2

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

Accepted Answer

Einstein Trust Layer with model explainability features.. The Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

Answer

Data Mask to protect sensitive information.

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Salesforce Shield with encryption and monitoring.

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Field Audit Trail to track changes to data.

Question 3

An organization uses Einstein to predict sales opportunities. They notice the model performs poorly for small businesses. What is the most ethical approach?

Accepted Answer

Retrain the model with more data from small businesses.. Retrain the model with more data from small businesses. Poor performance for small businesses most likely reflects underrepresentation in the training data, so adding more small-business examples improves the model's accuracy and fairness for that segment without abandoning it. This is the most ethical approach because it directly addresses the bias rather than hiding or accepting it. Option A does not fix the underlying data imbalance and may reduce overall accuracy, option B creates a fragmented solution that can still be biased and harder to govern, and option D is unethical because it ignores a customer segment based on revenue rather than addressing the model's deficiency.

Answer

Reduce the model's complexity to improve performance.

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Use a completely different model for small businesses.

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Ignore small businesses as they contribute little revenue.

Question 4

A sales team uses an AI tool to recommend products to customers. The tool recommends high-commission products over what best fits the customer. Which ethical principle is being violated?

Accepted Answer

Fairness. Fairness, because the AI tool is prioritizing high-commission products over what best fits the customer, which creates a biased and inequitable recommendation outcome that disadvantages the customer for the company's financial gain. Fairness in AI requires that systems avoid unjust bias and treat stakeholders equitably, and here the model is optimizing an inappropriate objective that skews recommendations. Privacy (A) is not implicated because no personal data misuse or unauthorized disclosure is described. Transparency (B) is not the core violation because the issue is not necessarily a lack of disclosure about how the tool works, but the biased recommendation itself. Accountability (D) is also not the primary violation because the scenario does not focus on who is responsible or answerable for the tool's outcomes.

Answer

Privacy

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Transparency

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Accountability

Question 5

An admin wants to create a custom AI model to predict lead conversion using Einstein Prediction Builder. Which TWO items must they select when creating the model? (Choose two)

Accepted Answer

Data set (records to train on). The data set defines the records (e.g., leads, opportunities) that the model will use for training. Without specifying which records to train on, the model has no source of historical data to learn patterns from. Einstein Prediction Builder requires you to select a data set (such as a report or object) to provide the training examples.

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Model algorithm type

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Prediction explanation settings

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Features (input fields)

Question 6

A sales operations manager wants to automatically log all emails and events from sales reps' Outlook accounts to Salesforce without manual setup. Which feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct feature because it automatically syncs emails and events from Microsoft 365 or Google Workspace into Salesforce without requiring manual setup or user-installed add-ins. It uses a server-side integration that logs activities directly to Salesforce records, meeting the requirement for automatic logging of Outlook emails and events.

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Einstein Conversation Insights

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Einstein Lead Scoring

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Einstein Email Insights

Question 7

A retail company deploys an AI system that adjusts prices dynamically based on customer browsing history. The system charges higher prices to returning customers. This practice is known as:

Accepted Answer

Unfair price discrimination. Unfair price discrimination, because the system charges different prices to customers based on their personal browsing history, specifically penalizing returning customers with higher prices, which is a discriminatory pricing practice. This goes beyond legitimate personalization since it exploits customer loyalty data to charge more, raising ethical and fairness concerns. Option A, lack of transparency, is a related but distinct issue—the problem here is the discriminatory pricing itself, not merely that it is undisclosed. Option C, personalization, refers to tailoring offerings or experiences to users, but it does not inherently involve charging different prices to different customers. Option D, dynamic pricing, involves adjusting prices based on factors like demand, time, or inventory, not on individual customer identity or history.

Answer

Lack of transparency

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Personalization

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Dynamic pricing

Question 8

A company wants to use customer purchase history to train a recommendation model. Which action is essential to comply with data privacy regulations?

Accepted Answer

Obtain explicit consent from customers.. Data privacy regulations such as GDPR and CCPA require a lawful basis for processing personal data, and explicit consent is a primary lawful basis when using customer purchase history for training a recommendation model. Without obtaining explicit consent, the company would be processing personal data without a valid legal ground, violating regulations that mandate transparency and user control over their data.

Answer

Use only publicly available data.

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Ignore regulations because data is internal.

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Anonymize the data after training.

Question 9

A mid-sized company uses Salesforce for sales and service. They have implemented Einstein Prediction Builder on a custom object 'Support_Ticket__c' to predict whether a ticket will be escalated (field: 'Escalated__c' Boolean). The model was trained with 10,000 records and 15 fields including 'Subject', 'Description_Summary__c', 'Priority__c', 'Hours_to_Resolution__c', and others. After deployment, the model's precision for escalated tickets is only 30%, while recall is 80%. The business finds too many false positives. The admin notices that the 'Priority__c' field has many missing values (60% null) and that the field 'Is_Critical__c' (a formula field) was included though it flags tickets as critical only rarely. The data spans 12 months but the last 3 months have a significantly higher escalation rate due to a product bug that has since been fixed. Which course of action will most likely improve the model's precision without harming recall?

Accepted Answer

Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority.. It addresses both the data drift and data quality issues that degrade precision. Excluding the last 3 months removes the biased escalation pattern caused by a fixed product bug, ensuring the model learns from stable historical patterns. Imputing missing 'Priority__c' values with the most common priority reduces noise from nulls without discarding the field entirely, which helps maintain recall by preserving predictive signal.

Answer

Roll back the model to the version trained 6 months ago when escalation rates were lower.

Answer

Exclude the 'Priority__c' field from the model and retrain.

Answer

Remove the 'Is_Critical__c' field and increase training data to 50,000 records.

Question 10

A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)

Accepted Answer

Including seasonality and trend components from historical data. Option B is correct because retail sales data almost always exhibits recurring seasonal peaks (e.g., holidays, back-to-school) and long-term trends, so explicitly modeling seasonality and trend components (as in SARIMA, Prophet, or seasonal decomposition) materially improves forecast accuracy. Option D is correct because missing values, outliers, and inconsistent records in five years of monthly data bias parameter estimation and can cause the model to learn spurious patterns, so cleaning and imputing the training data is a prerequisite for reliable predictions. Option E is correct because sales are driven by exogenous events such as holiday calendars, promotions, and markdowns; incorporating these as external regressors (e.g., in Prophet or SARIMAX) captures demand shifts that pure historical autocorrelation cannot explain. Option A does not belong because algorithm popularity is irrelevant to accuracy; the right choice depends on data characteristics, and a trendy algorithm can underperform a simple, well-tuned one. Option C does not belong because deep learning is not inherently more accurate for a small tabular dataset of five years of monthly sales, and simpler models often generalize better with limited data.

Answer

Choosing the most popular AI algorithm on the market

Answer

Using a deep learning model instead of a simpler model