AI Associate AI Fundamentals Practice Test 14 — 15 Questions

Question 1

A customer support director notices that cases are being routed to the wrong queues because the priority field is often left at the default value by agents. The director wants Salesforce to read the incoming case subject and description and automatically predict the correct priority so routing rules fire immediately. Which Einstein feature should be configured?

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

Accepted Answer

Einstein Case Classification. Predicting a case field such as priority from the subject and description is the defined purpose of Einstein Case Classification, which uses historical cases to learn patterns and then writes predicted values onto new cases so routing and escalation rules can act. Sentiment, recommendations, and article suggestions produce different outputs and cannot set the priority field.

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Einstein Article Recommendations

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Einstein Next Best Action

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Einstein Sentiment Analysis

Question 2

A customer support manager wants to reduce handling time by having an AI system automatically route incoming cases to the correct queue based on the text of the case description. The team has thousands of historically labeled cases mapping descriptions to queues. Which type of machine learning best fits this requirement?

Accepted Answer

Supervised classification. Because the team possesses thousands of cases already labeled with their correct queues, the task is a classic supervised classification problem. The model learns the mapping between case text and queue from those labels, then predicts the queue for new cases. This leverages existing data and produces a direct routing decision, unlike clustering or anomaly detection.

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Reinforcement learning

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Unsupervised clustering

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Anomaly detection

Question 3

A customer service team wants to use Salesforce Einstein to automatically classify incoming cases into categories such as 'Billing', 'Technical', or 'General'. They have a large set of historically labeled cases. Which Einstein capability should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct choice because it is purpose-built to automatically categorize cases based on their content. It uses historical case data to train a model that predicts the appropriate category for new cases. This reduces manual triage and improves routing accuracy. Other Einstein features like Sentiment Analysis, Prediction Builder, or Lead Scoring serve different purposes and cannot perform multi-class text classification for case management.

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Einstein Prediction Builder

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Einstein Sentiment Analysis

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Einstein Lead Scoring

Question 4

A service manager at a utility company wants to reduce handle time on case emails. They plan to use Einstein Case Classification to predict the Case Type and Priority on incoming cases. Which statement best describes how Einstein Case Classification produces these predictions?

Accepted Answer

It uses a language model to read the case subject and description and suggests field values learned from previously closed cases.. Einstein Case Classification applies natural language processing to case text and predicts values for configured picklist fields based on patterns learned from historical closed cases. It helps agents triage faster by suggesting Type and Priority. The other options describe manual keyword rules, customer self-selection, or agent routing, none of which reflect how the classification model generates its field predictions from case content.

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It uses a scoring model that ranks agents by their historical resolution speed and routes cases to the fastest agent.

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It requires the customer to select a case type and priority from a self-service portal before the case is created.

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It runs a deterministic keyword mapping that the admin maintains manually in Setup and assigns values when keywords match.

Question 5

A sales operations lead at Get Cloudy Consulting is reviewing an Einstein Lead Scoring model. The lead conversion rate is very low, and the model consistently assigns high scores to almost every lead. Which action best addresses the root cause?

Accepted Answer

Adjust the scoring model by ensuring there is a sufficient number of converted leads in the historical data used for training.. Einstein Lead Scoring depends on historical leads that converted and those that did not. When conversions are very rare, the model has little positive signal and tends to produce scores that do not separate leads well. Ensuring a sufficient number of converted leads in the training data restores the contrast the model needs. Adding fields, disabling scoring, or moving a threshold does not correct that fundamental data imbalance.

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Lower the lead score threshold so that fewer leads are classified as high scoring.

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Increase the number of fields Einstein Lead Scoring evaluates by adding custom fields to the lead object.

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Disable Einstein Lead Scoring and rely on manual lead prioritization until conversion rates improve.

Question 6

A retail company is evaluating whether to build its own machine learning model or adopt an out-of-the-box Salesforce predictive feature. The data science lead explains that the chosen approach must learn patterns from labeled historical examples, then apply those patterns to new, unseen records to output a prediction. Which term best describes this category of AI capability?

Accepted Answer

Supervised learning. Learning from historical records that contain known outcomes and then predicting those outcomes for new records is the definition of supervised learning. Unsupervised learning works without labels, reinforcement learning learns from reward feedback through actions, and generative AI produces new content, so none of them fits the described labeled-prediction workflow.

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Reinforcement learning

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Generative AI

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Unsupervised learning

Question 7

A customer service manager wants to use Einstein Bots to deflect common password reset and order status questions from live agents. Before building the bot, the manager asks which statement correctly describes how Einstein Bots handle user requests. Which statement is accurate?

Accepted Answer

Einstein Bots can be configured with dialogs and intents, and can invoke Salesforce actions like Flow or Apex to complete tasks such as resetting a password.. Einstein Bots combine natural language intent recognition with configurable dialogs and the ability to call Salesforce automation such as Flow or Apex. That means a bot can understand varied customer phrasing for password resets or order status, then execute the necessary backend action to resolve the request, achieving the deflection goal the manager has in mind.

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Einstein Bots rely solely on exact keyword matches, so any variation in user phrasing causes the bot to fail.

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Einstein Bots require a dedicated external chatbot platform and cannot run natively inside Salesforce channels.

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Einstein Bots can only answer questions using pre-written static responses and cannot perform any backend actions.

Question 8

A healthcare company is building an AI model to predict patient no-shows for appointments. The data science lead wants to ensure the model generalizes well to new patients rather than memorizing the training examples. Which practice should the team apply during model development?

Accepted Answer

Evaluate the model on a held-out validation set separate from the training data. Generalization is assessed by testing the model on data it has never seen. A held-out validation set provides that unbiased estimate and exposes overfitting, where training performance is high but new-data performance is poor. This practice is fundamental to building models that predict no-shows reliably for future patients rather than memorizing past records.

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Train the model only on the most recent month of appointment data

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Increase the number of features until training accuracy reaches 100 percent

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Remove all patients who missed appointments from the training data

Question 9

A Salesforce admin is exploring Einstein Studio to build a custom AI model. They want to bring their own data from an external data lake and train a model to predict customer lifetime value. Which statement best describes Einstein Studio?

Accepted Answer

It is a no-code interface for building, training, and deploying custom AI models using data from Data Cloud.. Einstein Studio is a no-code interface within Salesforce that enables users to build, train, and deploy custom AI models using data from Data Cloud. It supports predictive and generative models, allowing admins to leverage external data once ingested into Data Cloud. This makes it the ideal tool for predicting customer lifetime value from a data lake, as it abstracts away the need for coding and provides a guided workflow.

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It is a data visualization tool for creating dashboards that display AI model performance metrics.

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It is a prebuilt model that automatically predicts customer lifetime value without any configuration.

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It is a command-line tool for data scientists to write Python code and train models on Salesforce servers.

Question 10

A Salesforce admin is reviewing Einstein Opportunity Scoring with the sales operations team. The team asks which statement accurately reflects a limitation or behavior of this feature that they should plan around.

Accepted Answer

The model requires a minimum number of historical opportunities in the org before scores are generated, and new orgs may see no scores initially.. Einstein Opportunity Scoring depends on historical opportunity data to train its model, so orgs with limited closed opportunities may not receive scores until enough data accumulates. This is an important planning consideration for rollout timelines. The score supplements rather than replaces stage management, updates on a regular schedule rather than only on edits, and is governed by normal field-level access rather than an elevated permission.

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Scores are visible only to users with the Modify All Data permission, which prevents most reps from seeing them.

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The score is recalculated only when the opportunity record is edited, so untouched opportunities can display stale scores for long periods.

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The score replaces the opportunity stage, so sales reps no longer need to update the stage field manually.

Question 11

A customer service director at Cosmic Solutions wants an AI feature that recommends the best next action for an agent to take on a case, based on business rules and predictive scores already available in Salesforce. Which capability should the director implement?

Accepted Answer

Einstein Next Best Action. The director wants recommendations for the next action an agent should take, informed by business rules and predictive scores. Einstein Next Best Action is built for that purpose: it evaluates a strategy of rules and offers ranked recommendations in the user interface. Prediction Builder only produces scores, Bots handle conversations, and Activity Capture logs activity, so none of them deliver in-console action guidance.

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Einstein Activity Capture

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Einstein Bots

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Einstein Prediction Builder

Question 12

A Salesforce admin is preparing to enable Einstein Activity Capture for a sales team so that emails and events sync automatically to Salesforce. The admin must evaluate which considerations affect data visibility and governance before rollout. (Choose two.)

Accepted Answer

Admins can configure sharing settings that determine whether users see only their own captured activities or activities shared across the team.. Einstein Activity Capture stores synced activities in its own data store rather than as standard EmailMessage or Event records, and it provides sharing controls that govern whether users see only their own activities or team-wide activities. Both considerations directly affect visibility, reporting, and governance, so the admin must evaluate them before rolling out the feature to the sales team.

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Einstein Activity Capture requires every user to install a desktop plugin before any email or event can sync.

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Einstein Activity Capture permanently prevents admins from disabling it once enabled, so the decision is irreversible.

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Einstein Activity Capture automatically deletes all existing manually logged activities to avoid duplication with synced items.

Question 13

A Salesforce administrator is preparing to use Einstein Prediction Builder to predict which customers are likely to renew their service contracts. The admin must ensure the model has suitable data and a valid target. Which two conditions are required for this predictive model to be built successfully? (Choose two.)

Accepted Answer

A defined outcome field that records whether each historical customer renewed. To build a supervised prediction, Einstein Prediction Builder needs a clearly defined outcome field that captures the historical result, such as renewal or non-renewal, and enough historical records containing both outcomes to learn meaningful patterns. These two conditions supply the labels and the contrast required for training. Rules engines, streaming feeds, and dashboards are not prerequisites for model creation.

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A manually written rules engine that maps each customer to a renewal score

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A real-time external data stream that updates customer records every second

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A pre-built dashboard that visualizes renewal rates by region

Question 14

A marketing operations team is preparing to use a Salesforce predictive scoring feature on leads. The team wants to understand what the underlying model actually requires to produce meaningful scores. Which two statements correctly describe requirements or characteristics of supervised predictive scoring? (Choose two.)

Accepted Answer

Field values used as predictors should be populated consistently enough to be informative.. Supervised predictive scoring depends on historical records with known outcomes to learn from and on reasonably complete predictor fields to carry signal. It does not require live external data-lake connections, manual notebook retraining, or produce guaranteed accuracy, since scores are probabilistic estimates shaped by data quality and relevance.

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The model requires a live internet connection to an external data lake at scoring time.

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The model must be retrained manually by a data scientist using Python notebooks.

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Scores are guaranteed to be perfectly accurate as long as the feature is enabled.

Question 15

A Salesforce administrator is preparing to enable Einstein Activity Capture for a sales team that wants emails and events logged automatically. The team also wants to understand how the feature interacts with Salesforce data storage and sharing. (Choose two.)

Accepted Answer

Activity sharing settings determine which users can see captured emails and events, so admins must configure sharing before enabling capture.. Einstein Activity Capture stores captured emails and events in a separate activity data store and governs visibility through activity sharing settings. Admins planning a rollout must account for both the non-standard storage model and the sharing configuration. Historical migration, automatic record creation, and mandatory desktop plugins are not behaviors of this feature, so they should not factor into the implementation plan.

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Users must install a desktop plugin on every machine before any email or event can be captured by Einstein Activity Capture.

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Einstein Activity Capture automatically creates new lead and contact records whenever it detects an unknown email address in captured messages.

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Enabling Einstein Activity Capture immediately converts all historical emails in connected mailboxes into standard Salesforce EmailMessage records.