AI Associate Multiple Select — 10 Questions

Question 1

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.

Answer

Uniqueness

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Consistency

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Timeliness

Question 2

Which TWO actions are required to prepare data for an Einstein Discovery model?

Accepted Answer

Ensure the data is stored in a Salesforce object or a connected data source.. Options A and C are correct. A: Data must be in a supported object (standard or custom). C: The outcome field must be specified. Option B is not required; Einstein handles missing values. Option D is not required; data can be within Salesforce or connected. Option E is not required; features can be auto-selected.

Answer

Remove all records with missing values in any field.

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Select exactly 10 predictor fields manually.

Answer

Create a separate dataset for training and validation.

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

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.

Answer

It is only available for lead scoring models.

Answer

It only supports predictions on the Opportunity object.

Answer

The prediction model automatically retrains every 24 hours.

Question 5

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

Answer

Automated lead enrichment

Answer

Generating Apex code

Question 6

According to Salesforce's AI Trust Principles, which TWO practices are essential for ethical AI deployment?

Accepted Answer

Hold the organization accountable for AI-driven outcomes.. Option D is correct because Salesforce's AI Trust Principles emphasize organizational accountability for AI-driven outcomes, ensuring that the organization takes responsibility for the decisions and impacts of its AI systems. This principle aligns with ethical AI deployment by requiring governance, oversight, and mechanisms to address unintended consequences, rather than shifting blame to the technology itself.

Answer

Use AI to replace human jobs entirely.

Answer

Ensure models achieve 100% accuracy before deployment.

Answer

Fully automate decision-making without human review.

Question 7

A sales operations admin wants to use Einstein Opportunity Scoring. Which two steps are required to activate Einstein Opportunity Scoring? (Select two answers.)

Accepted Answer

Enable Einstein in the Einstein Setup menu.. Options B and C are correct: you need historical data (100 won opps in 18 months) and enable Einstein. Option A is incorrect because the permission set is needed for user access but not for activation. Option D is optional, not required. Option E is incorrect because you don't need to deactivate other scoring rules.

Answer

Create a custom scoring model specific to the business.

Answer

Deactivate any existing scoring rules.

Answer

Assign the Einstein Opportunity Scoring permission set to users.

Question 8

Which TWO data preparation steps are critical for ensuring high-quality training data?

Accepted Answer

Removing duplicate records.. Option B is correct because duplicate records in a dataset can cause the model to overfit to repeated patterns, biasing the learned distribution and reducing generalization. Removing duplicates ensures each data point contributes equally to training, which is essential for robust model performance.

Answer

Increasing dataset size by adding noise.

Answer

Normalizing all features.

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Using only labeled data.

Question 9

A data scientist is using Einstein Discovery to analyze sales data. The model results show a high correlation between two predictor variables. Which TWO actions should the data scientist take?

Accepted Answer

Combine them into a single feature.. Removing one correlated variable or combining them reduces multicollinearity.

Answer

Apply regularization.

Answer

Include both to capture more information.

Answer

Increase the sample size.

Question 10

A data scientist is preparing numeric features for a regression model. Which TWO transformations are commonly applied to improve model performance?

Accepted Answer

Normalize to a 0-1 range. Normalizing features to a 0-1 range (min-max scaling) ensures that all numeric features contribute equally to the model, preventing features with larger magnitudes from dominating the gradient descent optimization. This is especially important for distance-based algorithms like k-nearest neighbors or neural networks, where feature scale directly impacts convergence speed and model accuracy.

Answer

Remove outliers beyond 3 standard deviations

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Convert numbers to string labels

Answer

Apply one-hot encoding