AI Associate Practice Test 3 — 30 Questions

Question 1

A company uses Salesforce Data Platform to store customer data. They want to use this data to train an AI model for lead scoring, but they are concerned about data quality. Which step should they take first to ensure the data is suitable for AI?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Profile the data to identify missing values, outliers, and inconsistencies. Profiling the data is the essential first step because it systematically identifies missing values, outliers, and inconsistencies that degrade model performance. Without this baseline assessment, any subsequent normalization or labeling would be applied to flawed data, leading to unreliable lead scoring predictions. Salesforce Data Platform supports profiling via tools like Einstein Analytics or Data Prep, which scan fields for nulls, range violations, and format errors.

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Immediately normalize all numerical features

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Create a labeled dataset using historical lead outcomes

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Set up a data pipeline to stream data in real-time

Question 2

For an AI project, data must be stored in a way that supports both training and real-time inference. Which storage solution meets this requirement?

Accepted Answer

Data lake (e.g., Amazon S3 or Azure Data Lake). A data lake (e.g., Amazon S3 or Azure Data Lake) is the correct choice because it can store vast amounts of raw, unstructured, and structured data in its native format, making it ideal for training AI models on diverse datasets. At the same time, data lakes support real-time inference by enabling direct access to data via APIs or streaming services (e.g., AWS Lambda or Azure Functions) without the latency of transforming data into a schema-on-write structure. This dual capability—handling both batch processing for training and low-latency reads for inference—is a key requirement that other storage solutions cannot fulfill as effectively.

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Data warehouse (e.g., Snowflake)

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Relational database (e.g., PostgreSQL)

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In-memory cache (e.g., Redis)

Question 3

A company is using customer support tickets to train a model for auto-classifying issues. The dataset includes fields like 'Case Title', 'Description', 'Product', and 'Customer Name'. Which privacy concern is most critical to address before training?

Accepted Answer

Anonymize personal identifiable information (PII) in Description and Title. Anonymizing PII in the text fields is critical to avoid exposing customer information in model artifacts or predictions. Session tokens are irrelevant, and case numbers are not PII. Opt-in is a legal requirement but not directly about data preparation for AI.

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Encrypt session tokens used in the support system

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Remove all case numbers to prevent data leakage

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Ensure all customers have opted in before using their data

Question 4

A Salesforce admin wants to use Einstein GPT to generate personalized email content for a marketing campaign. To ensure the AI does not produce responses that include sensitive customer data or violate company policies, which Salesforce feature should the admin configure?

Accepted Answer

Einstein Trust Layer. Einstein Trust Layer is the correct feature because it acts as a governance and security layer between Salesforce and the large language model (LLM). It automatically masks sensitive customer data (e.g., personally identifiable information) before the prompt is sent to the LLM and then unmasks the response, ensuring the AI never sees or exposes sensitive information. This directly addresses the admin's need to prevent responses containing sensitive data or violating company policies.

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Prompt Builder

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Data Cloud

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Einstein Studio

Question 5

A service manager wants to automatically categorize incoming support cases based on the customer's description. Which Einstein feature should be used?

Accepted Answer

Einstein Case Classification. Einstein Case Classification uses machine learning to automatically categorize incoming support cases based on the customer's description, assigning them to predefined case fields (e.g., type, priority, product). This directly matches the requirement to automatically categorize cases from text, making it the correct choice.

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Einstein Reply Recommendations

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Einstein Bots

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Einstein Article Recommendations

Question 6

A data analyst is troubleshooting Einstein Article Recommendations that are not showing up on the site. Which TWO checks should be performed first? (Choose 2)

Accepted Answer

Check that article view events are being captured in the data. Einstein Article Recommendations rely on user interaction data, specifically article view events, to generate personalized recommendations. If these events are not being captured, the model has no input to learn from, and recommendations will not appear. Checking event capture is a fundamental first step in troubleshooting data pipeline issues.

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Ensure at least 100 articles are in the knowledge base

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Confirm that article authors have the correct profile permissions

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Increase the recommendation frequency from daily to hourly

Question 7

A data scientist is evaluating the performance of an Einstein Discovery model. They observe that the model has high accuracy but low precision for a specific prediction class. What does this indicate?

Accepted Answer

The model correctly predicts most instances but has many false positives for that class.. High accuracy with low precision for a specific class indicates that while the model correctly classifies the majority of instances overall, it produces a high number of false positives for that class. Precision measures the proportion of positive identifications that were actually correct, so low precision means many of the predicted positive cases are false alarms. In Einstein Discovery, this trade-off is critical when optimizing for business outcomes where false positives are costly.

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The model is overfitted to the training data.

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The model correctly predicts most instances but has many false negatives for that class.

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The model rarely predicts that class, leading to high accuracy.

Question 8

Which TWO actions are best practices when implementing Einstein Prediction Service?

Accepted Answer

Clean the data to handle missing values and outliers.. Data quality directly impacts the accuracy and reliability of Einstein Prediction Service models. Cleaning data to handle missing values and outliers ensures that the training data is representative and reduces the risk of skewed predictions, which is a fundamental prerequisite for any machine learning model within Salesforce's AI framework.

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Ignore correlated features to simplify the model.

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Include all available fields in the dataset for maximum information.

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Use the default field mapping without review.

Question 9

A company has a custom AI model for sentiment analysis and wants to use it in Salesforce without rebuilding. Which approach should they take?

Accepted Answer

Use Bring Your Own Model (BYOM) for Einstein. Bring Your Own Model (BYOM) for Einstein allows companies to deploy their own pre-trained AI models directly into Salesforce without rebuilding them. This approach leverages Salesforce's infrastructure for inference while keeping the custom model intact, making it the ideal solution for integrating a custom sentiment analysis model.

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Use Data Export and import into external system

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Use MuleSoft

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Build in Apex

Question 10

A healthcare provider uses an AI system to predict patient readmission risk. The system was trained on historical data from the past five years, during which the hospital served a predominantly urban population. Recently, the hospital expanded to rural areas with different demographic and socioeconomic profiles. The AI predictions have been less accurate for rural patients, leading to misallocation of care resources. The AI Ethics committee is reviewing the system for potential bias. The model outputs a risk score from 0 to 100. The data science team has identified that the model uses features such as income, distance from hospital, and insurance type, which may correlate with race and socioeconomic status. The team wants to make the model fairer without retraining from scratch. Which approach best balances fairness and predictive accuracy?

Accepted Answer

Apply a post-processing calibration that adjusts the risk score thresholds separately for urban and rural populations to achieve equal false positive rates.. Post-processing calibration adjusts the decision thresholds for each subgroup (urban vs. rural) to equalize a fairness metric (e.g., false positive rate) without modifying the underlying model. This approach preserves the predictive signal from the original features while directly addressing the bias caused by distribution shift, making it the most practical solution when retraining from scratch is not feasible.

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Remove the features income, distance, and insurance type from the model and retrain.

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Continue using the current model but add a disclaimer that predictions may be less accurate for rural patients.

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Retrain the model using only the latest year of data that includes rural patients.

Question 11

A company wants to use Einstein Article Recommendations to surface relevant knowledge articles to its support agents. What two data components are required to set up this feature?

Accepted Answer

Knowledge Article View event data and Case records. Einstein Article Recommendations uses historical Knowledge Article View event data to understand which articles agents have found useful in the past, and Case records to provide context about the current issue. By analyzing patterns between case attributes and article views, the AI can predict and surface the most relevant articles for a given case. Without both data components, the recommendation engine cannot learn the association between case details and article usefulness.

Answer

Email-to-case logs and Knowledge Article feedback

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Knowledge Article categories and Case priority

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Community user activity and Knowledge Article ratings

Question 12

Which THREE of the following are Einstein features available for Sales Cloud? (Choose three.)

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is a correct Einstein feature for Sales Cloud because it uses predictive AI models to automatically score leads based on historical conversion data, helping sales reps prioritize high-quality leads. It is natively integrated into Sales Cloud without requiring additional licenses or complex setup, leveraging standard Salesforce objects and fields.

Answer

Einstein Bot

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Einstein Analytics

Question 13

An AI Associate reviews the Lead Scoring model exhibit. What is the primary ethical concern with this model?

Accepted Answer

The model has low recall, potentially missing minority class leads.. The primary ethical concern is that the model has low recall, meaning it fails to identify a significant portion of actual positive leads (the minority class). In a lead scoring context, this can result in missed business opportunities and potential bias against certain customer segments, as the model systematically overlooks valuable leads that do not fit the majority pattern.

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The model uses too many features.

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The model is not explainable.

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The training data is imbalanced.

Question 14

A company is deploying an AI-powered chatbot to handle customer service inquiries. The bot uses historical chat data for training. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Obtaining consent from customers whose data is used for training. The most critical ethical consideration is obtaining consent from customers whose historical chat data is used to train the chatbot. Under regulations like GDPR and CCPA, personal data (including chat transcripts) requires explicit consent for processing, especially when used to train an AI model. Deploying without consent violates data privacy laws and erodes user trust, regardless of the chatbot's technical performance.

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Maximizing the chatbot's response accuracy

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Ensuring the chatbot can handle high traffic volumes

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Designing a human handoff protocol for complex issues

Question 15

A marketing team wants to predict which segment of customers will likely purchase a new product. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Segment Prediction. Einstein Segment Prediction uses predictive modeling and machine learning to analyze historical customer data and identify which segments are most likely to purchase a new product. It is specifically designed for predictive segmentation based on behavioral and demographic patterns, making it the most appropriate choice for this use case.

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Custom Reports

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List Views

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Campaigns

Question 16

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 17

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

Accepted Answer

Users must have the Einstein Relationship Intelligence permission set.. The Einstein Relationship Intelligence permission set grants users access to the feature's insights and data processing capabilities. Without this permission set, users cannot view or interact with Einstein Relationship Intelligence data, even if the feature is enabled at the org level.

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Exchange or Gmail must be used for email.

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The feature is only available in Unlimited Edition.

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All users must opt in to email logging.

Question 18

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

Accepted Answer

It predicts a binary outcome (e.g., convert or not). Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

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It predicts a numeric value

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It predicts the value of a text field

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It is currently retraining the model

Question 19

An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?

Accepted Answer

Fairness. The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.

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Transparency

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Accountability

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Privacy

Question 20

A company is training a customer service chatbot using historical conversation logs. Which TWO data preparation practices should be followed to ensure data quality?

Accepted Answer

Include answers with varied phrasing to enhance language variety. Training a chatbot on varied phrasing (e.g., synonyms, different sentence structures) improves its ability to understand and generate natural language responses. This practice enhances the model's robustness and generalization, preventing overfitting to a narrow set of expressions and ensuring it can handle the diverse ways customers phrase their queries.

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Exclude all user identifiers to protect privacy

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Include only successful interactions that were resolved

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Filter only English conversations for consistency