AI Associate Practice Test 27 — 15 Questions

Question 1

Refer to the exhibit. A Salesforce admin is reviewing an AI model's fairness report. Which action should the admin take?

Accepted Answer

Retrain the model because the equal opportunity score is below threshold.. The equal opportunity score (0.72) is below the bias threshold (0.8), indicating potential unfairness in true positive rates across groups. Option A is wrong because demographic parity is above threshold but equal opportunity is not, so not all metrics exceed threshold. Option C is wrong because removing features may not address the root cause. Option D is wrong because increasing the threshold would mask the problem.

Answer

Remove the email_engagement feature to improve fairness.

Answer

Increase the bias threshold to 0.9.

Answer

Deploy the model because all metrics exceed the threshold.

Question 2

Which three practices help maintain data quality for AI models in Salesforce? (Choose three.)

Accepted Answer

Monitor data freshness with Data Check. Data Check in Salesforce monitors data freshness by tracking when records were last updated, ensuring that AI models use current data. Stale data can degrade model accuracy, so this practice directly supports data quality for AI.

Answer

Disable duplicate matching rules for faster load

Answer

Use Excel for manual data updates

Question 3

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Question 4

A company wants to implement an autonomous AI agent using Agentforce. Which TWO components are essential for building the agent in Agent Builder?

Accepted Answer

Topics. In Agent Builder, Topics are essential because they define the specific intents or categories of user requests that the agent should handle, such as 'Order Status' or 'Return Policy'. Without Topics, the agent has no way to classify incoming queries and route them to the appropriate conversation flow. Actions are equally essential because they represent the tasks or API calls the agent can execute to fulfill a user's request, such as querying Salesforce records or invoking an external service.

Answer

Users

Answer

Profiles

Answer

Apex triggers

Question 5

Refer to the exhibit. A data analyst runs a profile on a dataset and sees these statistics. Based on best practices, which action should be taken first?

Accepted Answer

Remove the 200 duplicate records. Duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.

Answer

Impute the 500 missing values with the mean

Answer

Remove the 50 outliers in the Amount field

Answer

Skip all preprocessing and train the model directly

Question 6

A company uses Einstein Prediction Builder to predict customer churn. The data includes account creation date, number of support cases, and average payment delay. After training, the model shows low confidence scores. What is the most likely cause?

Accepted Answer

The data contains many missing values or outliers for the selected fields.. Low confidence scores in Einstein Prediction Builder often stem from data quality issues such as missing values or outliers. These anomalies distort the model's ability to learn meaningful patterns, leading to uncertain predictions. Clean, complete data is essential for the model to produce high-confidence scores.

Answer

The training dataset includes fewer than 500 records.

Answer

The prediction field is set to a numeric type instead of a picklist.

Answer

The model was trained on data refreshed daily instead of weekly.

Question 7

Which Einstein feature provides AI-enhanced forecast predictions that go beyond manager rollups and can compare AI forecast to rep commitments?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct answer because it uses AI to generate predictive forecasts that go beyond traditional manager rollups, and it allows users to compare the AI-generated forecast against individual rep commitments. This feature leverages historical data and patterns to provide more accurate predictions, enabling sales leaders to identify discrepancies between AI insights and human inputs.

Answer

Einstein Opportunity Scoring

Answer

Einstein Discovery

Answer

Einstein Lead Scoring

Question 8

A company wants to analyze recorded sales calls to identify keywords, talk-time patterns, and automatically capture next steps. Which feature should they use?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it is specifically designed to analyze recorded sales calls, identifying keywords, talk-time patterns, and automatically capturing next steps using natural language processing (NLP) and speech analytics. It transcribes conversations, detects sentiment, and extracts actionable insights from audio recordings, directly matching the company's requirements.

Answer

Einstein Activity Capture

Answer

Einstein Email Insights

Answer

Einstein Discovery

Question 9

A company wants to use AI to automatically analyze and classify images uploaded to Salesforce records, such as identifying product defects. Which Einstein feature should they use?

Accepted Answer

Einstein Vision. Einstein Vision is the correct feature because it is specifically designed for image recognition and classification tasks, such as analyzing uploaded images to identify product defects. It uses deep learning models to detect objects, classify images, and extract text from images, making it ideal for visual inspection use cases within Salesforce.

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

Answer

Einstein Language

Question 10

An admin creates a predictive model in Einstein Prediction Builder to forecast customer churn. The model shows high accuracy on test data but poor performance in production. What is the most likely cause?

Accepted Answer

Data drift between training and production environments. The model's high accuracy on test data but poor performance in production is a classic symptom of data drift. In Einstein Prediction Builder, the model was trained on historical data that may not reflect current customer behavior patterns, leading to a mismatch between training and production distributions. This is not a model training issue but a data environment shift.

Answer

Improper feature scaling in the training pipeline

Answer

The model is overfitted to the training data

Answer

Target leakage in the training dataset

Question 11

Which TWO of the following are common causes of model drift in Einstein Discovery?

Accepted Answer

Seasonal patterns that affect the target variable. Seasonal patterns (Option B) cause model drift because the relationship between input features and the target variable changes predictably over time, such as higher sales during holidays. Einstein Discovery models trained on historical data may fail to generalize if the seasonal cycle is not captured or if the model is not retrained to account for these recurring shifts, leading to degraded prediction accuracy.

Answer

Improved data quality after cleaning

Answer

Increased model complexity

Answer

Reduced size of the training dataset

Question 12

A sales rep noticed that the Einstein Lead Scoring prediction bar shows 'No score available' for many leads. The admin confirmed that Einstein Lead Scoring is enabled and the permission set is assigned. What is the most likely cause?

Accepted Answer

The org does not have enough historical data to train the scoring model.. Einstein Lead Scoring requires a minimum amount of historical lead data (typically at least 2,000 converted and 2,000 unconverted leads) to train its predictive model. If the org lacks sufficient historical data, the model cannot generate scores, resulting in 'No score available' for leads. This is the most likely cause because the admin confirmed the feature and permissions are correctly enabled.

Answer

The leads have not yet been assigned to a user.

Answer

The leads were created less than 30 days ago.

Answer

The lead scoring model is still training.

Question 13

A company wants to use Einstein Activity Capture to log emails and events automatically. Which two considerations should the admin evaluate before enabling this feature?

Accepted Answer

Users must link their email client (Gmail or Outlook).. Einstein Activity Capture requires users to link their email client (Gmail or Outlook) to Salesforce via OAuth 2.0 authentication. This linkage allows the feature to automatically log emails and events from the connected email and calendar systems without manual user intervention.

Answer

The feature is only available for Sales Cloud.

Answer

All lead and contact fields must be visible to users.

Answer

Users must install a browser plugin.

Question 14

An admin has built an Einstein Bot that handles order status inquiries. However, when customers type 'Where is my order?', the bot often does not understand and escalates incorrectly. What is the most likely cause?

Accepted Answer

The intent for order status lacks sufficient training phrases. The most likely cause is that the intent for order status lacks sufficient training phrases. In Einstein Bot, intents are matched using natural language processing (NLP) based on the training phrases provided. If the bot hasn't been trained on enough variations of 'Where is my order?', it will fail to recognize the user's intent and escalate incorrectly.

Answer

The bot is not connected to the order management system

Answer

Entities are not defined for order numbers

Answer

The bot's dialogue flow is not configured correctly

Question 15

A company wants to automatically log all sent emails and calendar events from Gmail into Salesforce without manual user action. Which feature should be configured?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture automatically syncs emails and events from email systems to Salesforce.

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Answer

Salesforce Inbox