AI Associate AI Fundamentals Practice Test 5 — 15 Questions

Question 1

Refer to the exhibit. A bot developer sees this error during Einstein Bot deployment. What is the correct action to resolve the issue?

Accepted Answer

Add at least 5 training phrases to the 'OrderStatus' intent. The error indicates that the 'OrderStatus' intent has fewer than the required minimum number of training phrases. Einstein Bot requires at least 5 training phrases per intent to ensure reliable natural language processing (NLP) model training. Adding at least 5 training phrases to the 'OrderStatus' intent satisfies this requirement and resolves the deployment error.

Answer

Reduce the minimum required phrases in bot settings

Answer

Remove the 'OrderStatus' intent from the bot

Answer

Increase the confidence threshold for all intents

Question 2

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 3

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 4

A company notices that Einstein Prediction Builder predictions for 'Churn' are less accurate than expected. Which action should the administrator take first to improve model performance?

Accepted Answer

Review the training data for missing values and ensure relevant fields are included in the model.. Option B is correct because the first step in improving Einstein Prediction Builder model performance is to review the training data for missing values and ensure relevant fields are included. Missing values or irrelevant fields can introduce noise and bias, directly degrading predictive accuracy. Einstein Prediction Builder relies on high-quality, complete training data to learn meaningful patterns, so data quality issues must be addressed before any other tuning steps.

Answer

Enable field history tracking on all object fields used in the prediction.

Answer

Change the prediction outcome to a different field to see if accuracy improves.

Answer

Retrain the model with the same data but increase the number of training iterations.

Question 5

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 6

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.

Question 7

A company wants to use Einstein Bots to handle customer support queries. Which preparation is most important before deploying the bot?

Accepted Answer

Ensure the knowledge base articles are well-organized and cover common issues.. Option C is correct because Einstein Bots rely on a well-organized knowledge base to retrieve accurate answers for customer queries. Without properly structured articles covering common issues, the bot cannot effectively resolve tickets, leading to poor deflection rates and user frustration. This preparation directly impacts the bot's ability to understand and respond to natural language inputs using Salesforce's NLP and article matching.

Answer

Train all agents on how to monitor the bot.

Answer

Set up a new email channel for the bot.

Answer

Create a custom object to store bot conversations.

Question 8

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

Accepted Answer

Einstein Trust Layer with model explainability features.. Option A is correct because the Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

Answer

Data Mask to protect sensitive information.

Answer

Salesforce Shield with encryption and monitoring.

Answer

Field Audit Trail to track changes to data.

Question 9

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.

Answer

Override the score manually to reflect their judgment.

Answer

Disable Einstein Lead Scoring for that lead record.

Question 10

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 11

A company wants to deploy an Einstein AI model that uses sensitive customer data. Which practice should they follow to comply with data privacy regulations?

Accepted Answer

Use Einstein Trust Layer features to mask personally identifiable information (PII) in the model.. Option D is correct because the Einstein Trust Layer provides built-in capabilities to automatically mask or redact personally identifiable information (PII) before data is sent to the underlying AI model, ensuring compliance with data privacy regulations like GDPR and CCPA without requiring manual data handling. This feature operates at the platform level, intercepting data in transit and applying masking rules based on predefined patterns, so sensitive customer data is never exposed to the model or stored in its training logs.

Answer

Store all sensitive data in an external data lake and connect via APIs.

Answer

Obtain explicit consent from data subjects before using their data in AI models.

Answer

Limit the data used for training to only essential fields.

Question 12

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.. Option A is correct because 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 13

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Accepted Answer

Summarizing case conversations from service interactions.. Option B is correct because Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Answer

Generating report charts and dashboards.

Answer

Creating workflow rules based on user behavior.

Question 14

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 15

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.

AI Associate AI Fundamentals Practice Test 5 — 15 Questions

Exhibit

AI Associate AI Fundamentals Practice Test 5 — 15 Questions

Exhibit