AI Associate Practice Test 28 — 15 Questions

Question 1

A service agent is working on a case and needs to quickly find relevant knowledge articles without searching manually. Which Einstein feature can automatically suggest articles based on the case details?

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

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it automatically suggests relevant knowledge articles based on the case details, such as subject, description, and product. This eliminates the need for manual search by leveraging AI to match case context with article content.

Answer

Einstein Next Best Action

Answer

Einstein Search

Answer

Einstein Case Classification

Question 2

An admin wants to compare the AI-generated forecast with a rep's commit forecast to identify gaps. Which feature should they use?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it directly compares AI-generated forecasts with a rep's commit forecast to identify gaps. It uses historical data and predictive models to generate a baseline forecast, which can be overlaid with the rep's manual commit to highlight discrepancies for coaching and adjustment.

Answer

Einstein Prediction Builder

Answer

Einstein Discovery

Answer

Einstein Opportunity Scoring

Question 3

A company uses Einstein Bots for customer support. They want the bot to understand when a customer says 'I want to return a product' and trigger a return flow. What must be configured to recognize this intent?

Accepted Answer

Train the bot's NLP with sample utterances for the 'Return_Product' intent. Einstein Bots rely on Natural Language Processing (NLP) to interpret user intent from free-form text. To recognize the intent 'Return_Product', you must train the bot's NLP model by providing sample utterances (phrases) that represent that intent. This allows the bot to generalize and match variations like 'I need to send this back' or 'How do I return an item?' without requiring exact phrase matching.

Answer

Add a dialogue step that matches the phrase exactly

Answer

Create an entity for 'product' and map it to return flow

Answer

Use Einstein Article Recommendations to surface return policy

Question 4

A company wants to build a custom AI model that predicts whether a support case will be escalated based on fields like case origin, priority, and description. They want to use existing Salesforce data without coding. Which tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the no-code tool for creating custom predictive models on Salesforce data, letting admins select an object (e.g., Case), define a yes/no outcome (e.g., escalation), and choose predictor fields like origin, priority, and description. It trains a model automatically and surfaces scores on records without writing code. This matches the requirement exactly.

Answer

Einstein Case Classification

Answer

Einstein Article Recommendations

Answer

Einstein Discovery

Question 5

A company is using Einstein Discovery to analyze sales data and improve win rates. Which THREE outputs does Einstein Discovery provide?

Accepted Answer

Improvement suggestions to increase the target metric. Einstein Discovery is designed to surface actionable insights from data, and option B is correct because it generates improvement suggestions (prescriptive recommendations) that show how changing specific predictor values would increase the target metric, such as win rate. Option C is correct because Einstein Discovery produces stories that explain, in natural language, the key drivers and patterns behind the predicted outcome, making the model interpretable to business users. Option D is correct because Einstein Discovery visualizes predictions with waterfall charts that break down how each factor contributes positively or negatively to the outcome for a given record. Option A is not correct because automated A/B test design is a marketing/testing capability, not an Einstein Discovery output. Option E is not correct because Einstein Discovery focuses on predictive modeling and explanation rather than producing formal hypothesis testing results for statistical significance.

Answer

Automated A/B test designs

Answer

Hypothesis testing results for statistical significance

Question 6

An AI system used for recruitment has been found to be biased. Which THREE steps should be taken to address this? (Choose three.)

Accepted Answer

Audit the training data for bias. Option B is correct because auditing the training data for bias is the essential first diagnostic step: you must identify skewed distributions, under-representation, or historically discriminatory labels in the dataset before any remediation can be effective. Option C is correct because retraining the model with a balanced dataset directly addresses the root cause by correcting the class and demographic imbalances that produced the biased predictions. Option E is correct because monitoring outcomes for disparate impact provides ongoing post-deployment assurance, using metrics such as the 80% (four-fifths) rule to detect whether protected groups receive disproportionately unfavorable decisions over time. Option A does not belong because deploying the biased model unchanged perpetuates the harm and violates fairness and compliance obligations. Option D does not belong because simply removing demographic data (fairness through unawareness) does not eliminate bias, since correlated proxy features can still encode protected attributes and the model would then be unauditable for disparate impact.

Answer

Deploy the model without changes

Answer

Remove demographic data from the model

Question 7

A data analyst is preparing data for a machine learning model. They notice that many records have missing values for the 'industry' field. What is the best first step?

Accepted Answer

Replace missing values with a placeholder or impute them. When a dataset has many missing values in a feature like 'industry,' the best first step is to handle them explicitly — either by imputing values (e.g., mode, mean, or model-based imputation) or by using a placeholder category like 'Unknown.' This preserves the maximum amount of data and avoids introducing bias from dropping records. Imputation or placeholder encoding is a standard data-preparation practice before training.

Answer

Delete all records with missing values

Answer

Ignore the missing values and train the model as is

Answer

Use a model that automatically handles missing data

Question 8

A data architect notices that a Data Stream from an external ERP system is failing intermittently with schema mismatch errors. The ERP team says the schema changes occasionally. What is the most effective long-term solution?

Accepted Answer

Implement Data Stream schema validation and flexible mapping. Implementing schema validation and flexible mapping allows the Data Stream to accommodate unexpected schema changes from the ERP system gracefully, addressing the root cause. Option C is wrong because increasing retries does not fix schema mismatches. Option B is wrong because changing the primary key does not address schema changes. Option D is wrong because manual notifications are not scalable or reliable.

Answer

Use a different primary key for the data model

Answer

Increase the number of retries for the Data Stream

Answer

Ask the ERP team to manually notify of changes

Question 9

After deploying an Einstein Prediction Builder model, a user sees a new field on the record. What is this field called and what does it contain?

Accepted Answer

Prediction Score field, containing a percentage between 0 and 100. When an Einstein Prediction Builder model is deployed, it automatically creates a Prediction Score field on the record. This field contains a percentage value between 0 and 100 that represents the model's predicted likelihood for the target outcome. The score is the primary output of the model, not a category, confidence level, or text explanation.

Answer

Prediction Category field, containing the predicted category label

Answer

Prediction Confidence field, containing a confidence level of the model

Answer

Prediction Explanation field, containing a text explanation of the prediction

Question 10

A company wants to create a custom AI model that predicts whether a support case will be escalated (Yes/No) based on historical case data. They have fields like Case Origin, Priority, and Description. Which Einstein feature should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct feature because it allows users to create custom binary classification models (e.g., Yes/No predictions) using their own historical data fields such as Case Origin, Priority, and Description. Unlike pre-built models, Prediction Builder is designed for custom objectives like predicting case escalation without requiring data science expertise.

Answer

Einstein Case Classification

Answer

Einstein Next Best Action

Answer

Einstein Discovery

Question 11

A marketing team wants to recommend products and content to website visitors in Experience Cloud. Which Einstein feature should they use?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the Salesforce feature designed to deliver personalized product, content, and article recommendations to users on Experience Cloud sites and other channels. It uses machine learning to analyze user behavior and surface relevant items. This directly matches the marketing team's goal of recommending products and content to website visitors.

Answer

Einstein Article Recommendations

Answer

Einstein Next Best Action

Answer

Einstein Prediction Builder

Question 12

A support agent needs to quickly find a relevant knowledge article while handling a case. Which Einstein feature suggests articles automatically?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is a Salesforce Einstein feature that automatically suggests relevant knowledge articles to support agents based on the case subject, description, and other data. It uses natural language processing and machine learning to analyze the case and match it with the most pertinent articles, helping agents resolve issues faster. This directly addresses the need to quickly find a relevant knowledge article while handling a case.

Answer

Einstein Next Best Action

Answer

Einstein Recommendation Builder

Answer

Einstein Case Classification

Question 13

A sales manager is reviewing the AI predictions from Einstein Opportunity Scoring. Which THREE actions should the manager take to ensure ethical use of AI according to Salesforce Trusted AI principles?

Accepted Answer

Provide sales reps with explanations of the key factors influencing each score.. Option A is correct because Salesforce's Trusted AI principles emphasize transparency and explainability, so giving reps the key factors behind each Einstein Opportunity Scoring prediction lets them understand and appropriately act on the AI output. Option B is correct because responsible AI requires ongoing monitoring and validation of model accuracy against real outcomes, ensuring the predictions remain reliable and unbiased over time. Option E is correct because human oversight is a core Trusted AI tenet; encouraging judgment and escalating borderline cases keeps a human in the loop for consequential decisions. Option C is wrong because hiding the model's logic contradicts transparency and explainability rather than preventing harm. Option D is wrong because overriding scores without review undermines accountability and human oversight, replacing informed judgment with unchecked intuition.

Answer

Keep the model's logic secret to prevent gaming the system.

Answer

Override the scores without review if they conflict with intuition.

Question 14

An admin is using Einstein Prediction Builder to create a model predicting whether a support case will be escalated. Which THREE steps are required during the prediction creation process?

Accepted Answer

Select features (input fields) for the model. In Einstein Prediction Builder, the prediction creation wizard requires you to first choose the Salesforce object and the set of records used to train the model, which is why option E is correct. You must then select the prediction field — the outcome you want to predict — and for an escalation scenario this is a binary classification field (Yes/No), making option C correct. You also must select the features (input fields) that the model will use as predictors, which is why option B is correct. Option A is not required because Einstein Discovery is a separate analytics product and Prediction Builder builds and validates the model itself without a mandatory Discovery run. Option D is not required because Einstein Copilot is unrelated to creating a Prediction Builder model; predictions can be surfaced via Lightning components, flows, or the API without Copilot.

Answer

Run Einstein Discovery to validate the model

Answer

Configure Einstein Copilot to trigger the prediction

Question 15

A data analyst is using Einstein Discovery to analyze customer churn. They want to understand the key drivers of churn and get actionable recommendations. Which THREE outputs does Einstein Discovery provide to meet this need?

Accepted Answer

Waterfall charts showing contribution of each variable. Einstein Discovery's core value for understanding churn drivers is delivered through its story outputs: option A, waterfall charts showing the contribution of each variable, is correct because these visualizations decompose a prediction into the relative positive or negative contribution of each feature, revealing exactly which factors drive churn. Option B, a story narrative explaining key insights, is correct because Einstein Discovery auto-generates a plain-language narrative that summarizes the top drivers and patterns found in the data, making the analysis actionable for a data analyst. Option E, improvement suggestions with expected impact, is correct because Einstein Discovery provides prescriptive recommendations that quantify how changing a given variable would affect the predicted outcome, directly supporting actionable churn-reduction decisions. Option C is not one of the three outputs this scenario asks for, since record-level prediction scores come from scoring/deployment rather than the insight-explanation outputs, and option D is likewise a deployment artifact (a model for scoring new data) rather than an explanatory output about churn drivers.

Answer

Prediction scores for each record

Answer

A trained model for deployment