AI Associate Practice Test 33 — 15 Questions

Question 1

A sales leader wants to compare each rep's forecast against an AI-generated prediction for the same period. Which Einstein feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it directly compares a sales rep's manual forecast against an AI-generated prediction for the same period, providing a side-by-side view of discrepancies. This allows sales leaders to assess forecast accuracy and adjust strategies based on AI insights.

Answer

Einstein Opportunity Scoring

Answer

Einstein Discovery

Answer

Einstein Lead Scoring

Question 2

A sales leader wants to see an AI-generated forecast that compares the predicted revenue to the reps' commit amounts. Which feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting uses AI to generate predictions that can be compared to rep commit amounts in the forecast dashboard.

Answer

Einstein Discovery

Answer

Einstein GPT

Answer

Einstein Opportunity Scoring

Question 3

A dataset contains a 'date' column. Which feature engineering technique would best capture both long-term trends and seasonal patterns?

Accepted Answer

Create cyclic features (sin/cos of month, day).. Cyclic features using sine and cosine transformations preserve the circular nature of temporal data (e.g., month 12 and month 1 are adjacent, not far apart). This allows a model to learn both long-term trends (via the year component) and seasonal patterns (via the cyclic encoding of month and day) without imposing a false linear ordering. In contrast, simple numeric extraction treats time as linear, which can misrepresent seasonal cycles.

Answer

Extract year, month, day as separate features.

Answer

Use only the day of week.

Answer

Drop the date column.

Question 4

An organization wants to use Einstein GPT to generate case summaries and draft knowledge articles for service agents. Which TWO Einstein GPT products should they enable? (Choose two)

Accepted Answer

Prompt Builder. Service GPT includes case summary and knowledge article draft capabilities.

Answer

Einstein Copilot

Answer

Sales GPT

Answer

Einstein Article Recommendations

Question 5

Which Einstein feature provides automated statistical analysis of Salesforce data, generates natural language stories, and suggests improvement actions?

Accepted Answer

Einstein Discovery. Einstein Discovery is the AI analytics tool that performs automated analysis, creates stories, and offers prescriptions.

Answer

Einstein Forecast

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

Question 6

A healthcare organization uses Einstein Next Best Action to recommend treatment plans to practitioners. A patient disputes a recommendation, claiming it was based on inaccurate historical data. Under GDPR, the patient has the right to obtain an explanation of the automated decision. Which Salesforce feature directly supports this right to explanation?

Accepted Answer

Einstein Trust Layer audit trail and model explainability. Einstein Trust Layer provides transparency and explainability features. The audit trail logs the factors that influenced the recommendation, and model explainability surfaces those factors. The combination of audit trail and explainability directly enables the right to explanation.

Answer

Einstein Activity Capture to gather customer interactions

Answer

Data Processing Addendum (DPA) signed with Salesforce

Answer

Data Cloud permission sets to control data access

Question 7

A company is developing a chatbot for customer service. They want to ensure the bot does not generate offensive responses. Which practice should they implement?

Accepted Answer

Deploy a content filter and human-in-the-loop moderation for sensitive interactions.. Deploying a content filter combined with human-in-the-loop moderation provides both automated detection of offensive language and human oversight for ambiguous or sensitive interactions. This layered approach ensures that the chatbot can block clearly toxic outputs in real time while allowing human reviewers to handle edge cases, reducing the risk of generating offensive responses.

Answer

Use unsupervised learning to allow the bot to learn naturally from conversations.

Answer

Remove all toxicity detection to avoid false positives.

Answer

Train the model only on customer service transcripts without review.

Question 8

A data engineer is troubleshooting a predictive model that stopped updating. The data flow from Data Cloud shows 'Data Transform Failed' with error: 'Field Amount cannot be null'. What is the most likely cause?

Accepted Answer

The target field in the model requires a non-null value but source data has nulls.. The error 'Field Amount cannot be null' indicates that the target field in the predictive model is configured to require a non-null value. When the data flow attempts to write records with null Amount values into that field, the transform fails. This is a common schema constraint mismatch where the source data contains nulls that violate the target field's nullability requirement.

Answer

The data transform includes a filter that removes records with null Amount.

Answer

The source object has a validation rule.

Answer

The data flow schedule is incorrect.

Question 9

A company has been using Einstein Lead Scoring for six months. Recently, the lead score confidence has dropped from 85% to 60%. The admin reviews the model and finds that many leads have missing data in custom fields used by the model. The admin also notices that field history tracking is not enabled on the Lead object. The lead volume is adequate with over 10,000 leads. What should the admin do to improve the model's confidence?

Accepted Answer

Enable field history tracking on the Lead object and retrain the model.. Field history tracking is required for Einstein Lead Scoring to capture changes over time, and retraining after enabling it will incorporate historical data. Option A is incorrect because manually updating all leads is impractical and doesn't address the root cause. Option C is incorrect because the lead volume is already adequate. Option D is incorrect because disabling and re-enabling will reset the model but not fix the missing field history.

Answer

Disable and re-enable Einstein Lead Scoring.

Answer

Increase the lead volume to at least 50,000 records.

Answer

Manually update all leads with missing data to have complete records.

Question 10

A Salesforce admin notices that Einstein Account Scoring is not generating scores for all accounts. Some accounts have no score even though they meet the data requirements. What is the most likely cause?

Accepted Answer

The org has fewer than 100 account records. Einstein Account Scoring requires a minimum of 100 account records to generate meaningful scores. If the org has fewer than 100 accounts, the model cannot establish a baseline for scoring, resulting in no scores for any account. This is a hard threshold enforced by the Einstein AI engine, not a configurable setting.

Answer

Einstein features are not enabled in the org

Answer

Users do not have the 'View Scores' permission

Answer

The accounts lack custom fields for scoring

Question 11

A marketing team wants to recommend relevant knowledge articles to service agents while they work on a case. Which Einstein feature provides these recommendations?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it specifically uses AI to analyze the context of a service case—such as case subject, description, and product—and then surfaces the most relevant knowledge articles directly within the Salesforce console for the agent. This is distinct from general recommendation engines because it is purpose-built for knowledge article suggestions in service workflows.

Answer

Einstein Next Best Action

Answer

Einstein Recommendation Builder

Answer

Einstein Case Classification

Question 12

Which THREE factors should be considered when evaluating the quality of a dataset for an AI model?

Accepted Answer

Presence of outliers that may skew the model.. Outliers can disproportionately influence model training, especially in algorithms like linear regression or k-means clustering, leading to biased predictions. Evaluating the presence and impact of outliers is critical for ensuring the model generalizes well to unseen data.

Answer

Total number of records available for training.

Answer

Number of distinct labels in the outcome field.

Question 13

A company is preparing customer data to train a custom AI model for sentiment analysis. Which two data preparation best practices should they follow? (Choose two.)

Accepted Answer

Ensure data is representative of all customer demographics.. Ensuring representative data and anonymizing PII are critical for model fairness and privacy. Removing all records with missing values can discard useful information; using only recent data may introduce bias; single-annotator labeling can cause subjective bias.

Answer

Use only data from the last month.

Answer

Remove all records with missing values.

Answer

Label data manually by a single annotator.

Question 14

A company wants to implement an autonomous AI agent in Salesforce that can handle customer service cases end-to-end. Which feature should they use?

Accepted Answer

Agentforce. Agentforce is the correct feature because it is specifically designed to create autonomous AI agents that can handle customer service cases end-to-end in Salesforce. Unlike simpler bots or copilots, Agentforce can independently execute multi-step workflows, make decisions, and take actions across Salesforce objects without requiring human intervention for every step.

Answer

Einstein Copilot

Answer

Einstein Next Best Action

Answer

Einstein Bots

Question 15

A CRM administrator is planning to implement predictive AI for lead scoring. Which TWO actions should be taken to ensure data quality?

Accepted Answer

Ensure lead source values are standardized (e.g., 'Web' not 'website' or 'web form'). Removing outdated or duplicate records and ensuring consistent formatting across fields are key data quality steps. Standardizing lead source values also ensures consistent labels.

Answer

Use as many fields as possible to increase model complexity

Answer

Manually label all historical leads again to remove bias

Answer

Use a smaller dataset to train faster