AI Associate Practice Test 47 — 15 Questions

Question 1

A data engineer needs to create a feature that represents the average purchase amount per customer over the last 30 days. The transactional data is timestamped. Which feature engineering technique is most appropriate?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Rolling average of purchase amounts over a 30-day window. A rolling average over a 30-day window directly computes the average purchase amount per customer for only the most recent 30 days of transactions, which matches the requirement of a time-sensitive feature. This technique uses a sliding window function (e.g., AVG() with a ROWS or RANGE frame in SQL, or rolling().mean() in pandas) that respects the timestamp order, ensuring only relevant data contributes to the feature.

Answer

Sum of all purchase amounts per customer

Answer

Count of purchases per customer

Answer

Minimum purchase amount per customer

Question 2

A company uses Einstein GPT for Sales to generate personalized email drafts. They want to ensure that the generated emails consistently include the recipient's company name and a specific discount offer from the opportunity. Which configuration is required?

Accepted Answer

Create a prompt template in Prompt Builder with merge fields for Company and Discount. Prompt Builder allows admins to create prompt templates that include merge fields for dynamic data, such as the recipient's company name and a discount offer from the opportunity. When Einstein GPT for Sales generates email drafts, it uses these merge fields to pull the specific values from the Salesforce record, ensuring consistency across all generated emails.

Answer

Configure Einstein Email Insights to highlight opportunities with discount information

Answer

Set up a Flow in Einstein Next Best Action to inject data into the email

Answer

Use Einstein Recommendation Builder to define dynamic email components

Question 3

You are a data scientist at a retail company. The company uses Einstein Discovery to analyze customer purchase patterns. The model is built on a dataset of 50,000 transactions. The model's R-squared is 0.85, but the predictions for new customers are consistently off by a large margin. The data includes features like 'Customer Age', 'Income', 'Previous Purchases', and 'Product Category'. The model was trained on data from the past two years. However, six months ago, the company launched a new loyalty program that significantly changed purchasing behavior. You suspect the model is not generalizing to new customers. What should you do to validate your hypothesis?

Accepted Answer

Create a holdout set of transactions from the last six months and compare model performance on it vs. older data. Creating a holdout set of transactions from the last six months directly tests whether the model's performance has degraded due to the loyalty program's impact on purchasing behavior. By comparing the R-squared or other metrics on this recent holdout set versus older data, you can quantify the drop in predictive accuracy and confirm that the model fails to generalize to the new data distribution. This approach is a standard method for detecting concept drift in machine learning models, especially when external changes (like a loyalty program) alter the underlying patterns.

Answer

Exclude new customers from the dataset entirely

Answer

Increase the training data size to include older transactions

Answer

Remove the 'Product Category' feature to simplify the model

Question 4

An admin is troubleshooting why Einstein Lead Scoring is not generating scores for some leads. They have enabled the feature and assigned permission sets. Which two factors could cause scores to be missing?

Accepted Answer

The lead's status is 'Converted' and scoring is configured to exclude converted leads. Einstein Lead Scoring can be configured to exclude leads with a 'Converted' status. If the scoring model is set to skip converted leads, those leads will not receive a score, even if the feature is enabled and permission sets are assigned. This is a common configuration setting that directly prevents scoring for converted records.

Answer

The lead has a custom field that is not included in the model

Answer

The lead does not have an email address

Answer

The lead's owner does not have a Salesforce license

Question 5

A company is implementing Einstein Activity Capture. They want to ensure that emails between the sales rep and the company's legal department are NOT logged. Which TWO actions should the admin take?

Accepted Answer

Add the legal department's email domain (e.g., @company-legal.com) to the excluded addresses list. Einstein Activity Capture provides an 'Excluded Addresses' list where you can specify email domains (e.g., @company-legal.com) to prevent any emails to or from that domain from being logged. Option C is correct because you can also add individual email addresses to the same excluded list, giving granular control over which specific contacts are excluded from activity logging.

Answer

Configure a validation rule to suppress logging when the recipient contains legal domain

Answer

Remove the sales rep's permission to log activities

Answer

Disable Einstein Activity Capture for the legal team's profiles

Question 6

A service agent receives an Einstein-generated case summary from Service GPT. The summary contains an error — it mentions a product the customer never purchased. What is the MOST likely cause?

Accepted Answer

The model experienced a hallucination — generating factually incorrect content. The Einstein-generated case summary incorrectly mentions a product the customer never purchased, which is a classic symptom of a hallucination in large language models. Hallucinations occur when the model generates plausible-sounding but factually incorrect content, often due to its probabilistic nature rather than relying on verified data. In this context, Service GPT may fabricate details if it lacks sufficient grounding in the actual Salesforce data, but the direct cause is the model's tendency to invent information.

Answer

The training data for Service GPT was not representative

Answer

The admin did not enable grounding in Salesforce data

Answer

The case description field was empty

Question 7

A service agent needs quick access to relevant knowledge articles while handling a case. Which Einstein feature can suggest the most relevant articles automatically?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations (C) is the correct feature because it uses AI to automatically surface the most relevant knowledge articles based on the context of the case, such as subject, description, and product. This directly addresses the need for quick access to relevant articles without manual search, leveraging Salesforce's predictive AI to match case data with article content.

Answer

Einstein Next Best Action

Answer

Einstein Case Classification

Answer

Einstein Service GPT

Question 8

Refer to the exhibit. A data scientist tries to query the dataset but receives an error. Which of the following is the most likely cause?

Accepted Answer

The requested fields are not included in the policy.. 'data_scientist' is in allowedUsers, so they are allowed. Option B is not in policy, C the policy filters amounts >0 and <10000, so 5000 is included, D the fields are in the policy, so they should be accessible.

Answer

The condition filters out records with amount=5000.

Answer

The data scientist is not listed in the allowedUsers array.

Answer

The policy format is invalid JSON.

Question 9

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.. 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 10

A company is preparing data for Einstein Article Recommendation. Which data source is most appropriate for training the model?

Accepted Answer

Historical article view and click data.. Einstein Article Recommendation uses supervised machine learning to predict which articles users are likely to find relevant. The model must be trained on historical user engagement signals—specifically article view and click data—to learn patterns of relevance. Without this behavioral data, the model cannot establish a correlation between user actions and article content.

Answer

Org metadata.

Answer

System debug logs.

Answer

User profile data only.

Question 11

A company uses Einstein Lead Scoring but notices that many leads with high scores are not converting. The admin wants to understand which factors are influencing the score. Where can the admin find this information?

Accepted Answer

On the lead record, in the Einstein Lead Score component, by clicking 'Score Factors'. The Einstein Lead Score component on the lead record includes a 'Score Factors' link that, when clicked, displays the top positive and negative factors influencing the lead score. This is the designated UI element for admins to gain transparency into which attributes (e.g., source, industry, behavior) are driving the score, without needing to access setup menus or external support.

Answer

In the Einstein Setup menu under Lead Scoring Models

Answer

By contacting Salesforce support for a model explanation

Answer

By running a report on the Lead object with the Einstein Score field

Question 12

A data analyst wants to understand why a particular opportunity win rate dropped last quarter. They need automated statistical analysis with natural language explanations and improvement suggestions. Which tool should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery provides automated statistical analysis, stories, and improvement suggestions.

Answer

Einstein Prediction Builder

Answer

Einstein Conversation Insights

Answer

Einstein Next Best Action

Question 13

A data scientist is building a churn prediction model using Einstein Discovery. They want to ensure the model does not rely on sensitive attributes like race or gender, even if those are correlated with other features. Which technique is MOST aligned with Salesforce's data minimisation principle?

Accepted Answer

Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them. Data minimisation means using only the data necessary for the task. Excluding sensitive attributes from the feature set is the most direct way to prevent them from being used, even if they are correlated with other features. Correlation does not imply causation, and if those features are not essential, they should be removed.

Answer

Include all features and rely on the AI to ignore biased ones

Answer

Apply a fairness constraint after training to adjust predictions

Answer

Use differential privacy to add noise to the training data

Question 14

A service analytics team wants to understand why customer case resolution times have increased. They need automated statistical analysis that generates natural language explanations and suggests improvements. Which product should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct product because it is specifically designed for automated statistical analysis that generates natural language explanations of data patterns and suggests actionable improvements. It uses machine learning to analyze historical data, identify key drivers of changes like increased resolution times, and outputs plain-English insights and recommendations, directly matching the team's need for automated analysis with explanatory and prescriptive output.

Answer

Einstein Prediction Builder

Answer

Einstein GPT

Answer

Einstein Bots

Question 15

A company plans to use Einstein Discovery to analyze sales data. Which data preparation step is essential for time-series forecasting?

Accepted Answer

Ensure date fields are properly formatted and contain sufficient historical range. For time-series forecasting in Einstein Discovery, the date field must be properly formatted (e.g., as a date or datetime data type) and contain a sufficient historical range to identify patterns like seasonality and trends. Without adequate historical data, the model cannot learn temporal dependencies, making this step essential.

Answer

Remove all outliers in sales amounts

Answer

Remove duplicate records

Answer

Scale all numeric fields to a 0-1 range