AI Associate Practice Test 19 — 15 Questions

Question 1

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 2

A sales rep wants to use Einstein Activity Capture to automatically log emails and meetings. Which prerequisite must be met?

Accepted Answer

Users must grant access to their email and calendar via OAuth. Einstein Activity Capture requires users to explicitly grant access to their email and calendar via OAuth (Option D). This OAuth-based authentication allows Salesforce to securely sync emails and meetings from the user's email provider (e.g., Gmail, Outlook) into Salesforce records. Without this explicit consent, the feature cannot access or log the user's activity data.

Answer

Chatter must be disabled for the organization

Answer

Sales Cloud Einstein licenses for all users

Answer

The feature is automatically enabled once email integration is configured

Question 3

A company has international customers and wants Einstein Prediction Builder to forecast deal closure probability. The data includes fields like 'region', 'product line', and 'deal amount'. What is a best practice to ensure the model works for all regions?

Accepted Answer

Group regions into broader categories like 'Americas', 'EMEA', 'APAC'.. Grouping regions into broader categories like 'Americas', 'EMEA', and 'APAC' reduces high cardinality and sparsity in categorical features, which improves model stability and prevents overfitting in Einstein Prediction Builder. This approach ensures each region group has sufficient training data to learn meaningful patterns, enabling the model to generalize better across all regions without introducing bias from rare categories.

Answer

One-hot encode the region field using 50+ dummy variables.

Answer

Remove the region field to avoid bias.

Answer

Use region as a numeric rank based on past conversion rates.

Question 4

A sales manager wants to automatically prioritize leads with the highest likelihood of converting. Which Einstein feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it uses AI to analyze historical lead data and assign a score based on the likelihood of conversion, enabling the sales manager to prioritize leads with the highest probability of becoming customers. This feature is specifically designed for lead prioritization, leveraging predictive models trained on past lead conversion patterns.

Answer

Einstein Opportunity Scoring

Answer

Einstein Activity Capture

Answer

Einstein Prediction Builder

Question 5

An organization uses Einstein Discovery to analyze survey data. The model reveals a correlation between age and satisfaction. What is the responsible use of this insight?

Accepted Answer

Investigate causality before action. A correlation between age and satisfaction does not imply that age causes satisfaction. The responsible use is to investigate causality — through further analysis, controlled studies, or domain expertise — before taking action. Acting on a spurious correlation could lead to ineffective or harmful decisions.

Answer

Act on the correlation immediately

Answer

Discard the result

Answer

Publish the result as-is

Question 6

A company uses Einstein Lead Scoring and notices that leads with a score above 90 are not converting as expected. They suspect the model is overfit to historical patterns. What should they do to improve model performance?

Accepted Answer

Retrain the model by including more recent leads and removing outdated ones. Overfitting occurs when a model learns historical patterns that are no longer relevant. By retraining the model with more recent leads and removing outdated ones, the model can adapt to current conversion behaviors and reduce overfitting, improving predictive performance.

Answer

Increase the score range from 1-99 to 1-100

Answer

Manually adjust lead scores for high-scoring leads

Answer

Add more features to the model to capture more signals

Question 7

Refer to the exhibit. A Salesforce admin sees this error when trying to enable Einstein Lead Scoring. What should the admin do to resolve the issue?

Accepted Answer

Add more lead records with associated activities until reaching at least 100. Einstein Lead Scoring requires a minimum of 100 lead records with associated activities (e.g., emails, events, tasks) to generate a predictive model. The error indicates insufficient data, so adding more leads with activities meets the threshold for model training.

Answer

Enable Einstein features in the org

Answer

Map lead fields to Einstein fields

Answer

Grant the admin the 'Manage Einstein' permission

Question 8

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 9

A company wants to use Einstein Prediction Builder to predict which customers are likely to churn. They have a dataset that includes customers' names, email addresses, and detailed purchase history. According to the data minimisation principle, which fields should be included in the model?

Accepted Answer

Only the purchase history. Data minimisation requires using only the data necessary for the stated purpose. For predicting churn, purchase history is the relevant behavioral signal; names and email addresses are personally identifiable information (PII) that add little predictive value and increase privacy risk, so they should be excluded.

Answer

Only the email addresses, since they are unique identifiers

Answer

All three fields because more data improves accuracy

Answer

Names and purchase history, but not email addresses

Question 10

A company is concerned about the data minimization principle when using AI to predict customer lifetime value. Which approach aligns with this principle?

Accepted Answer

Use only fields directly relevant to purchase history and engagement, excluding PII like social security numbers. Aligns with the data minimization principle by restricting data collection to only fields directly relevant to the prediction task (purchase history and engagement) and explicitly excluding personally identifiable information (PII) like social security numbers. This reduces privacy risk and complies with regulations such as GDPR and CCPA, which require that data collected be adequate, relevant, and limited to what is necessary for the processing purpose.

Answer

Include all available fields to maximize model accuracy

Answer

Obtain explicit consent for every field used

Answer

Anonymize all data before training, including purchase amounts

Question 11

A company wants to use Einstein to automatically log emails and events from their email system into Salesforce without manual user action. Which feature should be enabled?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture automatically syncs emails, events, and contacts between a connected email/calendar system (like Gmail or Outlook) and Salesforce, logging them to the appropriate records without manual user action. It is specifically designed for automatic activity logging and is the correct feature for this requirement. The other Einstein features focus on insights, analytics, or conversation analysis rather than automatic activity capture.

Answer

Einstein Email Insights

Answer

Einstein Conversation Insights

Answer

Einstein Discovery

Question 12

A company is using Einstein Activity Capture to sync emails and events from Gmail to Salesforce. However, certain internal emails from the IT department are being logged accidentally. Which configuration step should the admin take to prevent these emails from being captured?

Accepted Answer

Add the IT department's email domain to the Excluded Addresses list in sync settings. Einstein Activity Capture uses an 'Excluded Addresses' list in its sync settings to prevent specific email addresses or domains from being logged into Salesforce. By adding the IT department's email domain to this list, the admin ensures that any emails sent from or to that domain are automatically excluded from capture, stopping internal IT emails from appearing in Salesforce records without affecting other users.

Answer

Create an email-to-case rule to delete those emails

Answer

Disable Einstein Activity Capture for the IT department users

Answer

Use Einstein Email Insights to mark them as low priority

Question 13

An administrator is configuring Einstein Forecasting. They notice the AI forecast differs significantly from the manager's commit. The manager wants to understand why the AI forecast is lower. What should the administrator do?

Accepted Answer

Check the AI forecast explanation, which shows key drivers like historical win rate and pipeline changes. Einstein Forecasting provides an AI forecast explanation that details the key drivers influencing the prediction, such as historical win rates and pipeline changes. This explanation allows the administrator to transparently show the manager why the AI forecast is lower, addressing the discrepancy without disabling or manually overriding the AI model.

Answer

Run an Einstein Discovery story on the forecast data

Answer

Disable Einstein Forecasting and rely solely on manager commit

Answer

Adjust the AI forecast manually to match the commit

Question 14

A company is deploying Einstein Article Recommendations on its customer portal. They want to ensure customers know that recommendations are AI-generated. Which action aligns with the Salesforce Trusted AI Principle of Honesty?

Accepted Answer

Include a note stating 'Recommended by Einstein AI' on each recommendation. The Salesforce Trusted AI Principle of Honesty requires transparency about AI-generated content. By explicitly stating 'Recommended by Einstein AI' on each recommendation, the company clearly discloses that the recommendations are AI-generated, building trust with customers. This aligns with the principle's focus on avoiding deception and ensuring users understand the nature of the content they are seeing.

Answer

Provide a chatbot that can explain the recommendations

Answer

Use a generic label like 'Recommended for you'

Answer

Do not mention AI; just show the recommendations

Question 15

A company is developing a sentiment analysis model for customer reviews. The team wants to ensure the model is fair and does not exhibit bias. Which TWO actions are MOST effective? (Choose two.)

Accepted Answer

Audit the training data for imbalanced representation across demographic groups. Option B is correct because auditing the training data for imbalanced representation across demographic groups directly detects and quantifies bias before it is learned by the model, allowing the team to apply mitigation techniques such as resampling, reweighting, or collecting additional data. Option D is correct because ensuring the training data includes reviews from a diverse range of customers gives the model representative examples across demographics, which reduces the risk of the model systematically underperforming or producing skewed sentiment predictions for underrepresented groups. Options A, C, and E are not appropriate: a more complex model (A) can amplify bias rather than fix it, using historical data as-is (C) preserves existing imbalances and is the opposite of a fairness action, and removing all negative reviews (E) introduces severe label and selection bias by distorting the true sentiment distribution.

Answer

Use a more complex model to capture nuanced patterns

Answer

Use only historical data as-is without modification

Answer

Remove all negative reviews from the training data to avoid negative bias