AI Associate Practice Exam 52 — 20 Questions

Question 1

A company uses an AI model to screen job applicants. They discover the model is rejecting candidates from a certain demographic at a higher rate. Which ethical principle is most clearly violated?

Accepted Answer

Fairness. Fairness requires that AI systems do not discriminate against groups. The model's disparate impact violates fairness.

Answer

Transparency

Answer

Privacy

Answer

Accountability

Question 2

Refer to the exhibit. An AI loan approval policy is defined as a JSON rule set. Which ethical issue is most prominent based on this policy?

Accepted Answer

Potential for geographic discrimination due to zip code condition. The use of zip_code as an approval condition can lead to geographic discrimination (redlining). Option A is wrong because the rules are transparent (explicitly shown). Option B is wrong because income and credit score may be relevant, but zip code is problematic. Option D is wrong because the rules are defined, but accuracy is not directly addressed.

Answer

Use of irrelevant attributes like income and credit score

Answer

Insufficient accuracy due to simple rules

Answer

Lack of transparency in decision-making

Question 3

A Salesforce customer uses Einstein Sentiment Analysis to analyze customer feedback. They find the model is less accurate for non-English languages. What ethical concern does this raise?

Accepted Answer

Bias. The varying accuracy across languages indicates bias in the model, which is a fairness concern.

Answer

Accountability

Answer

Privacy

Answer

Security

Question 4

Which two actions are consistent with Salesforce's ethical AI principles when deploying a custom AI model on Salesforce?

Accepted Answer

Document the model's intended use and limitations.. Documenting intended use (transparency) and providing a challenge mechanism (accountability) align with ethical AI principles.

Answer

Use only structured data for training.

Answer

Use the model to make decisions without human review.

Answer

Optimize for accuracy over all other metrics.

Question 5

To comply with Salesforce's AI ethics principles when using Einstein Bots, which two practices should be implemented?

Accepted Answer

Allow users to escalate to a human agent.. Disclosing bot identity (transparency) and allowing human escalation (accountability) are key ethical practices.

Answer

Use the bot to make all customer decisions autonomously.

Answer

Store all conversation transcripts indefinitely.

Answer

Minimize data collection to only what is necessary.

Question 6

A sales team uses an AI model to prioritize leads. The model's predictions are not improving despite adding more data. Which THREE factors could explain this? (Choose three.)

Accepted Answer

The new data is noisy or of low quality. Poor data quality, irrelevant features, and model underfitting can all cause lack of improvement. Adding more data helps only if it's high-quality; more features can help if relevant, but not necessarily. Overfitting would show good training performance.

Answer

The model is already overfitting to the training data

Answer

The team has not added enough features to the model

Question 7

A government agency uses an AI system to allocate resources for public services such as healthcare and education. The system is designed to optimize overall efficiency based on historical usage data. After deployment, it becomes clear that underserved regions with less historical data receive significantly less funding than well-served regions. The agency's mission is to promote equity. The system's performance metrics show high efficiency, but community leaders protest the unfair distribution. What should the agency do?

Accepted Answer

Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.. Incorporating fairness constraints ensures equitable distribution while still using AI to optimize. Option A ignores the fairness issue. Option C is good but may not be sufficient if the model still biases against underrepresented areas. Option D reverts to a less efficient system.

Answer

Abandon the AI system and use a manual, rule-based allocation system.

Answer

Collect more historical data from underserved regions before making adjustments.

Answer

Continue using the system as is, since it maximizes efficiency.

Question 8

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

Answer

Encrypt all fields containing personally identifiable information

Answer

Exclude cases that were closed without escalation

Question 9

An AI model for predicting employee performance is found to have a higher false positive rate for women than for men. What is the best course of action?

Accepted Answer

Investigate the cause and retrain the model to reduce bias. A higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.

Answer

Lower the decision threshold for women to equalize false positive rates

Answer

Proceed with deployment because the overall accuracy is acceptable

Answer

Use the model but require manual review for all female candidates

Question 10

A data scientist wants to use Einstein Vision to detect defects in product images. Which type of model should they create?

Accepted Answer

Object detection. Einstein Vision supports object detection models to identify and locate multiple objects in an image, suitable for defect detection.

Answer

Image classification

Answer

Named entity recognition

Answer

Text classification

Question 11

Refer to the exhibit. This JSON snippet is from the Einstein Trust Layer configuration. What is the purpose of this configuration?

Accepted Answer

To detect biased predictions based on gender and race. The configuration enables bias detection on the specified sensitive attributes (gender and race).

Answer

To block all predictions involving gender or race

Answer

To anonymize gender and race data

Answer

To remove gender and race from the model

Question 12

A healthcare provider uses an AI model to predict patient readmission risk. The model is trained on historical data that underrepresents minority populations. What is the MOST significant ethical risk?

Accepted Answer

The model may produce biased predictions against minorities. Training on historical data that underrepresents minority populations leads to a model that has insufficient examples to learn patterns for those groups, resulting in biased predictions that systematically disadvantage minorities. This is a direct violation of fairness in AI ethics, as the model's outputs will be less accurate or equitable for underrepresented groups, potentially causing harm in critical healthcare decisions like readmission risk assessment.

Answer

The model may overfit to the majority population

Answer

The model cannot scale to real-time predictions

Answer

The model lacks explainability

Question 13

What is the primary purpose of Einstein Studio in the Salesforce AI ecosystem?

Accepted Answer

Train, test, and deploy custom AI models using AutoML. Einstein Studio is specifically designed to allow users to train, test, and deploy custom AI models using AutoML, without requiring deep data science expertise. It provides a no-code interface for building models on Salesforce Data Cloud data, enabling predictive scoring and recommendations directly within the Salesforce ecosystem.

Answer

Create and manage prompt templates

Answer

Unify customer data from multiple sources

Answer

Deploy pre-built Einstein GPT models

Question 14

An admin is building a custom AI prediction with Einstein Prediction Builder for a binary classification problem. Which THREE steps are required in the configuration? (Choose 3)

Accepted Answer

Select a prediction field (the field to predict). The required steps are: select prediction field, select data set, and select features. Apex and triggers are not needed.

Answer

Define a custom Apex class for data transformation

Answer

Configure a trigger to retrain the model daily

Question 15

A sales team wants to use AI to get product recommendations for customers. Which TWO types of machine learning could be used?

Accepted Answer

Unsupervised learning. Unsupervised learning can find patterns (e.g., customer segments) and supervised learning can predict likelihood to buy, both enabling recommendations.

Answer

Generative AI

Answer

Reinforcement learning

Answer

Computer vision

Question 16

A marketing manager wants to recommend personalized products to customers on a community portal. Which Einstein feature should they use?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it allows marketers to create and deploy personalized product recommendations on a community portal without requiring custom code. It leverages AI to analyze customer behavior and preferences, then surfaces the most relevant products directly within the portal experience.

Answer

Einstein Next Best Action

Answer

Einstein Article Recommendations

Answer

Einstein Prediction Builder

Question 17

Which TWO actions are recommended when preparing data for an Einstein Prediction Builder model?

Accepted Answer

Ensure the data set contains at least 500 records with the outcome field populated.. Einstein Prediction Builder requires a minimum of 500 records with the outcome field populated to ensure statistical significance and reliable model training. Fewer records can lead to overfitting or insufficient pattern recognition, making the model less accurate.

Answer

Include all available fields on the object, even if unrelated.

Answer

Use external data sources and upload CSV files without any preprocessing.

Answer

Include data from the last two days only for the most current trends.

Question 18

Refer to the exhibit. An AI model's accuracy is shown for four demographic groups. Which group should be investigated for potential bias?

Accepted Answer

Group Beta. Group Beta has significantly lower accuracy, indicating possible bias or underperformance.

Answer

Group Alpha

Answer

Group Gamma

Answer

Group Delta

Question 19

A sales rep wants to use Einstein Email Insights to prioritize which emails to respond to first. Which TWO statements about Einstein Email Insights are true?

Accepted Answer

It uses AI to identify high-priority emails. Einstein Email Insights uses AI to analyze email content and sender behavior, automatically identifying high-priority emails based on factors like sender importance, email sentiment, and response patterns. This prioritization helps sales reps focus on the most critical communications first, directly supporting efficient workflow management in Sales Cloud.

Answer

It is a chatbot that answers email-related questions

Answer

It automatically sends reply suggestions

Answer

It logs all emails to Salesforce automatically

Question 20

A company is building an Einstein Bot for customer support. They need to ensure the bot can understand user intents and extract key information. Which THREE components are essential for this? (Choose three)

Accepted Answer

NLP training. NLP training is essential because it enables the Einstein Bot to understand natural language inputs from users. By training the bot with NLP models, it can accurately interpret user intents and extract relevant information from conversations, which is fundamental for effective customer support automation.

Answer

Handoff to agent

Answer

Dialog flows