AI Associate Timed Practice Test 1 — 10 Questions

Question 1

A Salesforce admin implements Einstein Bots for customer service. To ensure the bot does not use biased language, what should the admin do?

Accepted Answer

Review training data for representativeness. Reviewing training data for representativeness helps identify and mitigate bias sources.

Answer

Use only predefined responses

Answer

Disable the bot for sensitive topics

Answer

Monitor conversations regularly

Question 2

A data architect is designing a data model for Einstein Discovery. The data includes categorical variables with high cardinality (e.g., postal codes). What is the best practice to handle such features?

Accepted Answer

Group them into higher-level categories (e.g., region).. Grouping high-cardinality categories into broader categories reduces overfitting and improves model stability.

Answer

Encode them as one-hot vectors.

Answer

Exclude them from the model.

Answer

Use the raw values without transformation.

Question 3

A data analyst is evaluating data quality for an Einstein model. Which TWO dimensions are most critical for model accuracy?

Accepted Answer

Accuracy. Completeness (no missing values) and accuracy (correct values) are fundamental to model performance.

Answer

Uniqueness

Answer

Consistency

Answer

Timeliness

Question 4

Which TWO actions are required to prepare data for an Einstein Discovery model?

Accepted Answer

Ensure the data is stored in a Salesforce object or a connected data source.. Options A and C are correct. A: Data must be in a supported object (standard or custom). C: The outcome field must be specified. Option B is not required; Einstein handles missing values. Option D is not required; data can be within Salesforce or connected. Option E is not required; features can be auto-selected.

Answer

Remove all records with missing values in any field.

Answer

Select exactly 10 predictor fields manually.

Answer

Create a separate dataset for training and validation.

Question 5

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring uses historical data to predict lead conversion probability and assign scores, enabling prioritization.

Answer

Einstein Activity Capture

Answer

Einstein Prediction Builder

Answer

Einstein Bot

Question 6

A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?

Accepted Answer

Lead interaction history with emails and web activity.. Einstein Engagement Scoring analyzes lead interactions (email opens, clicks, web visits) to calculate engagement scores. Option A is correct. Option B is wrong because demographic data is not the primary input. Option C is wrong because historical conversion data is used for predictive scoring, not engagement. Option D is wrong because social media data is not a direct input.

Answer

Historical conversion data from closed opportunities.

Answer

Lead demographic information like industry and company size.

Answer

Social media posts and mentions of the company.

Question 7

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

Accepted Answer

The user's email must be hosted on a supported platform (Gmail, Outlook).. Einstein Activity Capture requires that user emails be hosted on a supported platform (Gmail or Outlook/Exchange) because the feature uses server-side synchronization via APIs (Google Workspace APIs or Microsoft Graph) to automatically log emails and events into Salesforce. Without a supported email host, the service cannot connect to the mail server to capture activity data.

Answer

The org must be on Enterprise Edition or higher.

Answer

The user must have an Einstein AI license.

Answer

The user must manually enable email logging in personal settings.

Question 8

A nonprofit uses Einstein Vision to classify images of disaster areas. What is the primary benefit of using AI for this task?

Accepted Answer

It reduces manual effort and speeds up damage assessment.. Einstein Vision automates the classification of disaster images, significantly reducing the manual effort required for damage assessment. By processing large volumes of images rapidly, it accelerates the time to insight, enabling faster response and resource allocation. This aligns with the core benefit of AI: augmenting human effort with speed and scale.

Answer

It requires less training data than manual methods.

Answer

It eliminates all classification errors.

Answer

It can only classify images of specific disaster types.

Question 9

A company deploys Einstein Recommendation Builder on its e-commerce site. The recommendations are not personalized. What is the most likely cause?

Accepted Answer

The model has not been trained with enough user behavior data.. Einstein Recommendation Builder relies on user interaction data to personalize. If insufficient data exists, recommendations become generic. Option A is correct. Option B is wrong because real-time sync is not required. Option C is wrong because the builder can work without a data scientist. Option D is wrong because the model can recommend products beyond categories.

Answer

The company did not hire a data scientist to tune the model.

Answer

The recommendation engine is not syncing in real-time with the website.

Answer

The product catalog is too large for the model to process.

Question 10

A sales team is using Einstein Lead Scoring, but the scores for new leads seem inconsistent and not reflecting recent conversion patterns. The admin checks the model and finds it was trained three months ago. Which action should the admin take to improve model accuracy?

Accepted Answer

Retrain the Einstein Lead Scoring model with the latest lead data.. Option C is correct because retraining the model with recent data will capture current conversion patterns. Option A is wrong because increasing field history retention does not retrain the model. Option B is wrong because field-level security does not affect scoring. Option D is wrong because adjusting scoring ranges manually defeats the purpose of machine learning.

Answer

Manually override the lead scores for a sample of leads.

Answer

Increase the field history retention period for lead fields.

Answer

Adjust field-level security to allow the model to access more fields.