AI Associate Practice Exam 2 — 20 Questions

Question 1

A company has set up Einstein Next Best Action with a recommendation strategy. They want to ensure that recommendations are personalized based on the customer's recent behavior. What data should be used?

Accepted Answer

Streaming data from Data Cloud that includes recent website interactions.. Option B is correct because Einstein Next Best Action requires real-time or near-real-time data to personalize recommendations based on recent customer behavior. Streaming data from Data Cloud captures website interactions as they happen, enabling the recommendation engine to use the most current signals (e.g., page views, clicks) to adjust offers dynamically.

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Event data from the website tracked via Google Analytics.

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Static profile fields like customer age and location.

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Historical data from a data warehouse updated daily.

Question 2

A data scientist is building a predictive model for customer churn using Salesforce data. The dataset has 20 features, and the target variable is highly imbalanced (5% churn, 95% non-churn). Which technique should be applied to handle the class imbalance before training?

Accepted Answer

Use Synthetic Minority Over-sampling Technique (SMOTE). SMOTE (Synthetic Minority Over-sampling Technique) is the correct choice because it generates synthetic samples for the minority class (churn) by interpolating between existing minority instances, effectively balancing the dataset without simply duplicating data. This prevents the model from being biased toward the majority class (non-churn) and improves recall for the churn class, which is critical in imbalanced classification problems.

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Apply Principal Component Analysis (PCA) for dimensionality reduction

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Create interaction features between existing variables

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Use accuracy as the evaluation metric

Question 3

A company is implementing Einstein Prediction Builder to predict whether a support case will escalate. Which TWO data preparation steps should the admin take to improve model accuracy?

Accepted Answer

Ensure missing values are handled appropriately (e.g., imputed or excluded). Correct: Removing redundant fields (like record IDs) and handling missing values are crucial for model accuracy. Option A is wrong because more fields can introduce noise. Option C is wrong because data encryption is about security, not accuracy. Option D is wrong because all cases should be included to represent the full pattern.

Answer

Include as many fields as possible to provide more context

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Encrypt all fields containing personally identifiable information

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Exclude cases that were closed without escalation

Question 4

A Salesforce admin notices that Einstein Account Scoring is not generating scores for all accounts. Some accounts have no score even though they meet the data requirements. What is the most likely cause?

Accepted Answer

The org has fewer than 100 account records. Einstein Account Scoring requires a minimum of 100 account records to generate meaningful scores. If the org has fewer than 100 accounts, the model cannot establish a baseline for scoring, resulting in no scores for any account. This is a hard threshold enforced by the Einstein AI engine, not a configurable setting.

Answer

Einstein features are not enabled in the org

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Users do not have the 'View Scores' permission

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The accounts lack custom fields for scoring

Question 5

A company wants to use Einstein to predict which customers are likely to churn in the next 30 days. Which type of prediction should be created in Einstein Prediction Builder?

Accepted Answer

Binary classification. Einstein Prediction Builder is designed for binary outcomes, such as whether a customer will churn (yes/no) within a specified time frame. A binary classification model predicts one of two possible labels, making it the correct choice for this churn prediction use case.

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Numeric prediction

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Multi-class classification

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Regression

Question 6

An admin is setting up Einstein Bot for a customer service chat. The bot needs to collect the customer's account number before transferring to a human agent. What should the admin configure?

Accepted Answer

Create a Dialog to ask for the account number. Option B is correct because a Dialog is the component that handles a conversation flow, including collecting information. Option A is incorrect because a Variable stores data but does not collect it. Option C is incorrect because a Rule determines logic, not collection. Option D is incorrect because a Set Value action sets a variable, but the overall collection happens within a Dialog.

Answer

Add a Variable with the account number field

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Define a Rule to validate the account number

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Use a Set Value action to assign the account number

Question 7

A company uses Einstein Prediction Builder to predict whether a lead will convert. The model's confidence score is low, and the admin wants to improve accuracy. What is the most effective action?

Accepted Answer

Increase the amount of historical data for training. Option B is correct because increasing the amount of high-quality historical data provides more examples for the model to learn from, improving accuracy. Option A is incorrect because more features can improve accuracy but only if they are relevant; just adding any field may cause noise. Option C is incorrect because Einstein models train automatically on your data, and you cannot directly increase training frequency. Option D is incorrect because training multiple models is not supported and doesn't improve the single model.

Answer

Create multiple prediction models and average their scores

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Add more custom fields as predictor fields

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Increase the frequency of model retraining

Question 8

A sales rep wants to generate personalized email drafts for leads using AI. Which feature should the admin enable?

Accepted Answer

Einstein GPT. Einstein GPT is the correct feature because it is Salesforce's native generative AI tool that can automatically create personalized email drafts for leads by leveraging CRM data and natural language processing. Unlike other options, Einstein GPT is specifically designed for AI-driven content generation within the Salesforce ecosystem.

Answer

Workflow Rules

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Process Builder

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Email Templates

Question 9

A marketing manager wants to prioritize leads with the highest likelihood of conversion. Which Einstein feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring predicts conversion probability for each lead. Custom formulas are manual, approval processes and data export are not predictive.

Answer

Approval Processes

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Custom Formula Fields

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Data Export

Question 10

A service team uses Einstein Discovery to analyze customer churn. The story shows 'Average Resolution Time' is a key driver. What is the best action?

Accepted Answer

Reduce Average Resolution Time through process changes. Einstein Discovery identifies drivers; reducing resolution time directly addresses root cause. Custom reports just show data, flows send alerts, processes update records but don't reduce time.

Answer

Update account records via process builder

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Configure a flow to send churn alerts

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Create a custom report on churn

Question 11

To identify common customer issues from chat transcripts using AI, which feature should be used?

Accepted Answer

Einstein Conversation Mining. Einstein Conversation Mining analyzes unstructured chat data to surface common themes. Surveys collect feedback, reports and dashboards show aggregated data but not insights from text.

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Post-Interaction Survey

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Reports

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Dashboards

Question 12

Which TWO statements are true about Einstein Prediction Builder in Salesforce? (Choose two.)

Accepted Answer

It can use related object fields as predictors in the model.. Option D is correct because Einstein Prediction Builder can include fields from related objects (e.g., child objects or lookup objects) as predictors in the model, enabling richer data inputs for predictions. This is achieved through the platform's ability to traverse relationships and aggregate data from related records, which significantly enhances model accuracy.

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It is only available for lead scoring models.

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It only supports predictions on the Opportunity object.

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The prediction model automatically retrains every 24 hours.

Question 13

Refer to the exhibit. A data scientist built a model using training data where 80% of leads were won. The model achieved 80% accuracy. What is the main issue with this evaluation?

Accepted Answer

Accuracy is not a reliable metric because the data is imbalanced. Correct: Accuracy is misleading due to class imbalance; a model that always predicts 'won' would get 80% accuracy. Option A: Data size is fine. Option B: XGBoost is good for tabular data. Option D: Confidence score not provided.

Answer

The model lacks confidence scoring

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The algorithm choice (XGBoost) is inappropriate

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The training data size is insufficient

Question 14

A Salesforce admin notices that Einstein Case Classification in Service Cloud is suggesting categories that frequently require manual correction. Which action should the admin take first?

Accepted Answer

Provide feedback by correcting the predictions to improve the model.. Option B is correct because Einstein models learn from user feedback; correcting predictions helps improve accuracy. Option A is wrong because routing before fixing classification may propagate errors. Option C is wrong because adding categories without tuning may degrade performance. Option D is wrong because starting over is unnecessary.

Answer

Enable Einstein Case Routing to automatically route based on predicted categories.

Answer

Delete the training data and start over with a new model.

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Increase the number of categories in the classification model.

Question 15

An organization uses Einstein Search to power a portal's search functionality. Users report that search results are not ranking relevant documents highly. Which configuration change is most likely to improve relevance?

Accepted Answer

Tune the search algorithm by promoting frequently accessed content.. Option B is correct because search relevance is improved by promoting popular or authoritative content. Option A is wrong because more fields may add noise. Option C is wrong because refresh frequency affects freshness but not ranking algorithm. Option D is wrong because stemming and synonyms help recall but not necessarily ranking.

Answer

Enable stemming and synonyms.

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Increase the number of indexed fields.

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Decrease the index refresh frequency.

Question 16

A company uses Einstein GPT to answer customer inquiries. To improve response relevance, the admin wants to restrict the AI's knowledge to only the company's product catalog and knowledge articles. Which approach should the admin use?

Accepted Answer

Use grounding data in the prompt template to provide relevant context. Correct: Ground the prompt with specific data sources. Option A: Fine-tuning adjusts model weights, not real-time data. Option B: Prompt engineering alone doesn't restrict knowledge without grounding. Option D: Zero-shot learning uses no examples; doesn't limit knowledge.

Answer

Apply zero-shot learning without any examples

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Fine-tune the model on the company's data

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Use prompt engineering to instruct the model to only use company data

Question 17

Which TWO capabilities are available in Einstein GPT for Sales?

Accepted Answer

Summarizing call transcripts. Correct: B and D. Option A: Sentiment analysis is not a primary feature of Einstein GPT for Sales (it's more in Service). Option C: Automated data entry is not a direct GPT feature. Option E: Code generation is for developers, not sales.

Answer

Sentiment analysis of customer emails

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Automated lead enrichment

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Generating Apex code

Question 18

Refer to the exhibit. A Salesforce admin runs an audit command for an Einstein model. What conclusion can be drawn from the output?

Accepted Answer

The model's average response time of 350ms is within typical performance expectations. The output shows an average response time of 350ms for the Einstein model, which is within the typical performance expectation of under 500ms for real-time AI inference in Salesforce. This indicates the model is responding efficiently without significant latency issues, making option C correct.

Answer

Token usage indicates the average response length is 300 tokens

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The model experienced significant slowdowns

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The model has a high error rate and needs retraining

Question 19

A company wants to use Einstein to predict the optimal discount amount for each deal. Which type of machine learning problem does this represent?

Accepted Answer

Regression. Predicting a continuous numerical value, such as the optimal discount amount for a deal, is a regression problem. In the context of Einstein, this would use a regression model to learn from historical deal data and output a specific discount percentage or dollar amount, rather than a category or cluster.

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Reinforcement learning

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Classification

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Clustering

Question 20

A sales manager sees that the Einstein Lead Score for a high-profile lead is low but expects it to be high. What should the manager do to investigate the discrepancy?

Accepted Answer

Use the Einstein Lead Score Insights to analyze the factors that contributed to the score.. Option C is correct because Einstein Leaderboard provides user-level analytics to compare individual scores and understand distributions. Option A is wrong because changing the score manually defeats the purpose. Option B is wrong as disabling the model is drastic. Option D is wrong because retraining may not be needed for a single case.

Answer

Retrain the model to improve its accuracy for that lead.

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Override the score manually to reflect their judgment.

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Disable Einstein Lead Scoring for that lead record.