AI Associate Practice Test 40 — 15 Questions

Question 1

Refer to the exhibit. An admin built a prediction model for case closure within 24 hours. The model accuracy is 72% with 500 training records. Which change would most likely improve accuracy?

Accepted Answer

Increase the training sample size to 5000 records. Increasing the training sample size from 500 to 5000 records provides the model with more data to learn patterns from, which reduces overfitting and improves generalization. In CRM AI models, larger datasets typically lead to higher accuracy because the algorithm can better capture underlying relationships without being skewed by noise in a small sample.

Answer

Change the outcome to 'Escalated'

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Remove the 'Subject' field from the model

Answer

Add more fields like 'Comments'

Question 2

A multi-national company wants to use Einstein Activity Capture to automatically log emails from Outlook to Salesforce. They have a requirement that emails to certain external domains (e.g., competitors) must never be logged. How should they configure this?

Accepted Answer

Configure excluded addresses in Einstein Activity Capture settings. Einstein Activity Capture provides a native configuration to exclude specific email addresses or domains from being logged. By adding competitor domains to the 'Excluded Email Addresses' list in the Activity Capture settings, the system prevents those emails from ever being captured, ensuring compliance without post-processing workarounds.

Answer

Use a Process Builder to delete the logged emails

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Use Einstein Email Insights to filter out those domains after logging

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Create a validation rule on the Email Message object

Question 3

A large retail company uses Data Cloud to consolidate customer data from e-commerce, POS, and loyalty programs. They plan to use Einstein Studio to build a churn prediction model. The data architect notices that the churn model's accuracy is below expectations. Upon investigation, they find that the customer entity in Data Cloud has multiple records for the same customer with slightly different spellings and addresses. The data comes from different streams. What should the data architect do to improve the model?

Accepted Answer

Create a Data Transform to merge duplicate records based on fuzzy matching on name and address fields. The best course of action because creating a Data Transform with fuzzy matching merges duplicates into a single clean record, improving data quality for the model. Option B is flawed because increasing frequency does not fix existing duplicates. Option C aggregates but doesn't resolve the duplication. Option D changes the primary key but duplicates remain.

Answer

Increase the data stream frequency to get more recent data

Answer

Change the primary key in the data model to use a different identifier

Answer

Use a Calculated Insight to aggregate customer behavior over time

Question 4

A company deploys an AI-powered email composer for sales reps. The legal team requires that every AI-generated email be reviewed by a human before sending to a customer. Which approach aligns with Salesforce's Trusted AI Principle of Empathy and Human Oversight?

Accepted Answer

Require the sales rep to click 'Approve' before the email is sent. It directly implements human oversight by requiring the sales rep to click 'Approve' before the email is sent, aligning with Salesforce's Trusted AI Principle of Empathy and Human Oversight. This principle mandates that AI systems should include mechanisms for human review and control, especially in high-stakes communications, ensuring that AI-generated content is vetted for accuracy, tone, and compliance before reaching customers. The approval step creates a clear human-in-the-loop checkpoint, preventing automated sending without human judgment.

Answer

Allow the AI to send emails automatically but log all sent emails for audit

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Use a rule to block specific words in AI-generated emails

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Send a daily report of all AI-generated emails to the legal team after they are sent

Question 5

A company is deploying an AI system to recommend products to customers. To comply with GDPR's right to explanation, which TWO practices should they implement? (Choose 2)

Accepted Answer

Provide a list of the most influential factors for each recommendation. GDPR's right to explanation requires that individuals can understand the logic behind automated decisions. Providing a list of the most influential factors for each recommendation directly addresses this by offering transparency into the model's decision-making process, such as feature importance scores from a tree-based or linear model.

Answer

Store all recommendation logs for at least 10 years

Answer

Allow customers to opt out of all AI recommendations

Answer

Use only anonymized data for recommendations

Question 6

An administrator is configuring Einstein Lead Scoring. After activation, lead scores are visible in the lead record page. However, some leads that are clearly not interested (e.g., bounced email) are scored 90+. What is the MOST likely reason?

Accepted Answer

The administrator did not exclude bounced leads from the training population. Einstein Lead Scoring uses historical conversion data. If the history includes leads that converted despite bounces or the model learns from patterns that don't match current behavior, scores may be inaccurate. But the question describes a scenario where the model is not trained on the correct audience. The best answer is that the administrator did not exclude inappropriate records from the training set.

Answer

Einstein Lead Scoring only works for imported leads

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The lead score field is not added to the page layout

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The model requires at least 2000 converted leads to be accurate

Question 7

Which TWO of the following are key principles of trustworthy AI according to Salesforce's AI ethics guidelines?

Accepted Answer

Transparency. Transparency is a core principle of trustworthy AI because it requires that AI systems operate in a way that is open and understandable, allowing stakeholders to see how decisions are made. Salesforce's AI ethics guidelines emphasize transparency to ensure that users can trust the system's outputs and that any biases or limitations are visible.

Answer

Profitability

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Complexity

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Accountability

Question 8

A company is building an autonomous AI agent with Agentforce. They need to define what the agent can do and how it responds. Which THREE components must be configured in Agent Builder?

Accepted Answer

Topics. A is correct because Topics define the scope of what the autonomous AI agent can handle by grouping related intents and conversations. They are the primary mechanism in Agent Builder to specify the agent's capabilities and how it should respond to user inputs, acting as the foundational building block for agent behavior.

Answer

Business outcomes

Answer

Prompt templates

Question 9

A marketing manager wants to use AI to recommend next-best actions for customers based on their previous purchases. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Recommendations. Einstein Recommendations is the correct choice because it is specifically designed to analyze customer purchase history and behavioral data to suggest next-best actions, such as products or content, in real time. It uses collaborative filtering and deep learning models to generate personalized recommendations, directly matching the use case of suggesting actions based on previous purchases.

Answer

Einstein Discovery

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Einstein Bot

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Einstein Prediction Builder

Question 10

A financial services firm wants to use AI to detect fraudulent transactions. They have a dataset with 1% fraudulent and 99% legitimate transactions. Which THREE actions should they take to address class imbalance? (Select THREE.)

Accepted Answer

Use F1 score as the evaluation metric. Class imbalance can be addressed by resampling (oversampling minority, undersampling majority), using class weights, or using metrics like F1 that consider precision and recall.

Answer

Use a larger dataset even if it is more imbalanced

Answer

Use accuracy as the primary evaluation metric

Question 11

A sales rep wants to quickly generate a summary of a phone call recorded in Salesforce for their records. Which Einstein feature can automatically generate call summaries from recorded conversations?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it uses natural language processing (NLP) to automatically analyze recorded phone calls and generate concise summaries, key topics, and action items. This allows sales reps to quickly capture call outcomes without manual note-taking, directly within Salesforce.

Answer

Sales GPT

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Einstein Activity Capture

Answer

Einstein Email Insights

Question 12

A company uses Einstein Conversation Insights to analyze sales call recordings. They want to identify calls where the competitor name 'Acme Corp' is mentioned and track the talk time of the sales rep vs. customer. How can they achieve this?

Accepted Answer

Configure Einstein Conversation Insights to track the keyword 'Acme Corp' and use the talk-time analysis feature. Einstein Conversation Insights is specifically designed to analyze sales call recordings, track keywords (like 'Acme Corp'), and provide talk-time analysis to compare sales rep vs. customer speaking time. This directly matches the requirement to identify competitor mentions and measure talk-time distribution.

Answer

Use Einstein Article Recommendations to suggest articles about Acme Corp

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Use Einstein Email Insights to search for Acme Corp in emails

Answer

Enable Einstein Lead Scoring to score leads mentioning Acme Corp

Question 13

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 14

A company uses Einstein Prediction Builder to predict customer churn. The model's accuracy is low. The admin reviews the training data and notices that only 2% of records are churned. What should the admin do to improve the model?

Accepted Answer

Use oversampling techniques.. When a dataset has severe class imbalance (only 2% churned records), the model becomes biased toward predicting the majority class (non-churned), leading to low accuracy despite high apparent performance. Oversampling techniques, such as SMOTE or random oversampling, artificially increase the number of churned records in the training set to balance the classes, allowing Einstein Prediction Builder to learn patterns for the minority class more effectively.

Answer

Remove the churned records.

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Increase the amount of training data.

Answer

Change the prediction field.

Question 15

A service organization wants to use Einstein Reply Recommendations to suggest responses to customer chats. The feature is enabled, but agents report that no recommendations appear. The admin has ensured the permission set is assigned and the chat data is flowing. What should the admin examine next?

Accepted Answer

The language settings for the chat channels.. Einstein Reply Recommendations require the chat channel language to be set to a supported language (e.g., English, Spanish, French, German, Portuguese, or Japanese). If the language setting is unsupported or mismatched, the model cannot generate suggestions, even if permissions and data flow are correct. This is a common misconfiguration because the feature silently fails when the language is not recognized.

Answer

The number of closed cases with successful chat histories.

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The model training schedule.

Answer

The user's profile settings for chat.