AI Associate Timed Practice Test 5 — 10 Questions

Question 1

Which Einstein feature would allow a company to automatically generate personalized email content for marketing campaigns?

Accepted Answer

Einstein GPT. Option B is correct because Einstein GPT uses generative AI to create personalized content at scale. Option A is wrong as it's for predictions, not content generation. Option C is wrong as it's for segmentation, not content. Option D is wrong as it's for analytics, not content.

Answer

Einstein Segmentation

Answer

Einstein Prediction Builder

Answer

Einstein Analytics

Question 2

A financial services company is deploying Einstein Prediction Builder to predict customer churn. The data includes both numerical and categorical fields. Which step is essential to ensure the model is not biased against protected attributes like race or gender?

Accepted Answer

Exclude any protected attributes from the training data and ensure the model does not use correlated proxies.. Excluding protected attributes like race or gender from the training data and ensuring the model does not use correlated proxies is essential to prevent bias in Einstein Prediction Builder. This approach directly removes the risk of the model learning discriminatory patterns based on these attributes, as the platform relies on the data provided and does not automatically enforce fairness constraints. Including such attributes or relying on built-in fairness would not guarantee unbiased predictions because the model could still infer protected characteristics from correlated features.

Answer

Include race and gender as predictors to allow the model to adjust for them.

Answer

Rely on the model's built-in fairness constraints.

Answer

Use a deep learning algorithm to automatically handle bias correction.

Question 3

A developer is implementing retrieval augmented generation (RAG) for a customer service bot. Which component is essential for supplying real-time data to the prompt?

Accepted Answer

A vector database for embedding and retrieval. Correct: A vector database stores embeddings for efficient retrieval of relevant context. Option A: A fine-tuned LLM is static. Option B: Prompt Builder is for templates. Option D: A knowledge base alone doesn't provide real-time retrieval augmentation unless indexed.

Answer

A fine-tuned large language model

Answer

A prompt template in Prompt Builder

Answer

A static knowledge base

Question 4

Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?

Accepted Answer

To ensure both features contribute equally to the model. Normalization scales features like 'AnnualRevenue' and 'NumberOfEmployees' to a comparable range (e.g., 0–1 or with zero mean and unit variance). Without normalization, a feature with larger numeric values (e.g., revenue in millions) would dominate distance-based calculations in models like k-nearest neighbors or gradient descent, causing the model to undervalue the smaller-scale feature. By normalizing, both features contribute equally to the model's learning process, which is essential for many machine learning algorithms used in Einstein.

Answer

To detect outliers in the data

Answer

To remove rows with missing values

Answer

To reduce the number of features from 30 to 2

Question 5

A sales team wants to use Einstein Lead Scoring to prioritize leads. What is the primary benefit of using Einstein Lead Scoring over manual scoring?

Accepted Answer

It uses historical data to predict which leads are most likely to convert.. Einstein Lead Scoring uses historical data and machine learning models to analyze patterns from past leads and their conversion outcomes. This allows it to assign a predictive score to each new lead, indicating the likelihood of conversion, which is more accurate and data-driven than manual scoring based on subjective criteria.

Answer

It automatically sends personalized emails to leads.

Answer

It ensures all leads are contacted within 24 hours.

Answer

It replaces the need for any manual lead qualification process.

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

An organization is implementing Einstein AI for sales forecasting. They have multiple custom objects and complex approval processes. Which design consideration is most critical for ensuring accurate AI predictions?

Accepted Answer

Implementing model explainability to verify that predictions align with business logic.. Option C is correct because model interpretability is essential to understand why predictions are made, especially in complex environments. Option A is wrong while relevant, user training is secondary. Option B is wrong as too much data can be noise, but it's not the most critical. Option D is wrong because a data warehouse may not be necessary.

Answer

Ensuring the data set includes at least 100,000 records per prediction field.

Answer

Creating a separate data warehouse to aggregate data from all objects.

Answer

Providing extensive user training on AI concepts.

Question 9

Refer to the exhibit. A Salesforce admin configured the Einstein Trust Layer policy shown. What is the effect of this policy on AI model usage?

Accepted Answer

AI models receive masked data for those fields, but feature importance insights are still available.. The Einstein Trust Layer policy configured to mask specific fields ensures that sensitive data is replaced with masked values before being sent to the AI model. This preserves data privacy while still allowing the model to generate predictions and insights. Feature importance insights remain available because they are computed from the masked data, not the original values.

Answer

All fields in the org will be masked to protect customer privacy.

Answer

AI models will not be able to use the configured fields, and model insights are disabled.

Answer

AI models can still use the fields but feature importance insights are blocked.

Question 10

A nonprofit uses Einstein Recommendations to suggest donations. They notice that the recommendations are not relevant. Which best practice should they follow to improve relevance?

Accepted Answer

Verify that the Recommendation object has enough historical interaction data and that events are correctly tracked.. Option B is correct because Einstein Recommendations depend on user interaction data; ensuring event tracking is accurate and sufficient is key. Option A is wrong because more options can lead to analysis paralysis. Option C is wrong because more recommendations per page can overwhelm users. Option D is wrong because data retention policies don't directly improve relevance.

Answer

Set a data retention policy to delete records older than 30 days to keep data fresh.

Answer

Increase the number of recommended items to 10 to give more choices.

Answer

Display recommendations on every page, including the donation receipt page.