AI Associate Practice Exam 60 — 20 Questions

Question 1

A data engineer is building a dataset for an Einstein Discovery model that predicts opportunity win probability. The dataset includes a 'Close_Date__c' field. They want to create a feature that captures the number of days between opportunity creation and close. Which transformation should they apply?

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

Accepted Answer

Create a formula field that subtracts 'CreatedDate' from 'Close_Date__c'.. To capture the sales cycle duration, the engineer should calculate the difference between the opportunity creation date and close date. This derived feature directly measures how long the opportunity was open, which can be a key predictor of win probability. Other transformations like bucketing or timestamp conversion do not provide this duration.

Answer

Replace 'Close_Date__c' with the quarter of the year.

Answer

Convert 'Close_Date__c' to a numeric Unix timestamp.

Answer

Bucket the 'Close_Date__c' field into monthly intervals.

Question 2

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.

Answer

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

Answer

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

Answer

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

Question 3

A marketing manager wants Einstein Engagement Scoring to identify which customers are most likely to engage with a new email campaign. The org uses Marketing Cloud and has connected its data sources. The manager notices that the Engagement Score is not available for a subset of contacts. Which factor most likely explains the missing scores?

Accepted Answer

The contacts have insufficient historical engagement data for the model to generate a score.. Einstein Engagement Scoring predicts the likelihood of engagement based on historical interactions. When contacts have insufficient engagement history, such as no prior sends or opens, the model cannot compute a reliable score. CRM lead scores, connector configuration, and journey membership do not directly determine whether an Engagement Score is generated for a contact.

Answer

The contacts have not been assigned a lead score in Sales Cloud.

Answer

The contacts are not included in an active journey.

Answer

The Marketing Cloud connector is not configured to sync Salesforce CRM data.

Question 4

A sales operations manager wants to use Einstein Lead Scoring to prioritize leads. They notice that the model has a low score for most leads, and the scores do not seem to reflect actual conversion likelihood. What is the most likely cause?

Accepted Answer

The lead data lacks sufficient historical conversion examples. Einstein Lead Scoring relies on historical lead conversion data to train its model. If there are too few converted leads, the model lacks sufficient examples to learn patterns, leading to poor scoring. Ensuring adequate historical data with a good mix of converted and unconverted leads is essential for accurate scores.

Answer

The leads are not assigned to the correct sales reps

Answer

The leads are missing phone numbers and email addresses

Answer

The org has exceeded its Einstein Lead Scoring API limits

Question 5

A sales operations team wants to use AI to improve pipeline management. They need to identify which opportunities are at risk and which accounts are most likely to buy. Which two Salesforce Einstein features should they use? (Choose two.)

Accepted Answer

Einstein Account Scoring. Einstein Opportunity Scoring and Einstein Account Scoring are the two features that directly predict win likelihood for opportunities and purchase/renewal likelihood for accounts. They provide actionable insights for pipeline management. The other options are either for leads, activity logging, or service cases, which do not match the sales operations need.

Answer

Einstein Case Classification

Answer

Einstein Activity Capture

Answer

Einstein Lead Scoring

Question 6

A sales operations analyst at Northern Trail Outfitters wants to give reps a ranked list of which open opportunities are most likely to close in the current quarter, based on historical opportunity patterns in Salesforce. The analyst does not want to build a custom model. Which Einstein feature should the analyst enable?

Accepted Answer

Einstein Opportunity Scoring to surface the likelihood that each open opportunity will be won.. Einstein Opportunity Scoring is the prebuilt Einstein feature that scores open opportunities by win likelihood using historical opportunity data. It requires no custom model building and surfaces scores on records and list views, which is exactly what the analyst wants for quarter-close prioritization. The other features target leads, cases, or require custom model creation, so they do not fit the stated requirement.

Answer

Einstein Lead Scoring to rank inbound leads by conversion likelihood.

Answer

Einstein Prediction Builder to create a custom score for each opportunity record.

Answer

Einstein Case Classification to predict opportunity win probability from support interactions.

Question 7

A Salesforce partner is building an AI-powered chatbot for a healthcare client. The chatbot will handle patient inquiries and provide general health information. The partner wants to ensure the solution adheres to ethical AI principles. Which two actions should the partner take? (Choose two.)

Accepted Answer

Implement clear disclosures that the user is interacting with an AI, not a human.. The two correct actions are implementing clear AI disclosures and ensuring diverse training data. These align with Salesforce's responsible AI principles of transparency and fairness. Disclosures build trust, while diverse data prevents bias. Other options either overstep ethical boundaries, violate privacy, or negate the benefits of AI.

Answer

Allow the chatbot to provide specific medical diagnoses to save time.

Answer

Configure the chatbot to escalate all queries to a human agent immediately.

Answer

Store all patient conversations indefinitely for future model training without consent.

Question 8

A Salesforce administrator is preparing a Data Cloud data stream to feed an Einstein Studio predictive model. The source object contains a 'ContactEmail' field where several records have values like 'not provided' or 'n/a'. The administrator wants the model to ignore these records during training. Which data quality dimension is primarily being addressed?

Accepted Answer

Validity. Validity is the data quality dimension that ensures values conform to expected formats and domains. In this scenario, the email field contains placeholder strings that are not valid email addresses. Filtering or transforming these values before feeding the data stream to Einstein Studio prevents the model from learning incorrect patterns, thereby improving prediction accuracy and data readiness for AI.

Answer

Completeness

Answer

Uniqueness

Answer

Timeliness

Question 9

A Salesforce customer service team uses Einstein Bots to handle customer inquiries. To ensure ethical AI, they want to provide transparency about the bot's capabilities. Which action should they take?

Accepted Answer

Clearly inform customers that they are interacting with an AI bot and provide an option to escalate to a human agent.. Ethical AI requires transparency so users understand they are interacting with an AI system. Informing customers and providing a human escalation option respects their autonomy and builds trust. The other options either deceive users, fail to disclose AI involvement, or violate privacy, none of which align with ethical AI principles.

Answer

Store all customer conversations for auditing purposes without notifying customers.

Answer

Configure the bot to mimic human responses so customers do not realize they are talking to a machine.

Answer

Limit the bot's responses to predefined FAQs to avoid any unexpected answers.

Question 10

A data engineer is preparing a dataset for an Einstein Discovery model to predict loan default risk. The dataset includes a field 'Income' with a skewed distribution and several outliers. The engineer wants to improve model performance by transforming this field. Which transformation is most appropriate?

Accepted Answer

Apply a logarithmic transformation to 'Income' to reduce skewness and the impact of outliers.. Logarithmic transformation is ideal for right-skewed data with outliers, as it reduces skewness and compresses extreme values. This allows the model to better capture the relationship between income and default risk without being dominated by a few high-income records. Other methods either do not address skewness or introduce bias.

Answer

Replace outliers with the median income value.

Answer

Bin 'Income' into deciles and use the decile rank as a feature.

Answer

Standardize 'Income' by subtracting the mean and dividing by the standard deviation.

Question 11

A Salesforce customer is deploying an Einstein AI model to predict customer churn. The company wants to ensure that its use of AI aligns with ethical principles of transparency and accountability. Which two actions should the company take? (Choose two.)

Accepted Answer

Establish a review board to evaluate the model's impact and address any ethical concerns.. Documenting the model in a model card and establishing a review board both promote transparency and accountability. A model card makes the model's purpose, data, and limitations clear, while a review board ensures ongoing oversight and responsibility. These actions align with ethical AI practices by making the system understandable and answerable to stakeholders. The other options either focus on privacy, reduce oversight, or are impractical for achieving transparency.

Answer

Use the model to automatically make final decisions on customer retention offers without human review.

Answer

Share the model's source code with all customers to demonstrate how it works.

Answer

Enable Einstein Trust Layer data masking for all fields used by the model.

Question 12

A Salesforce developer is building an AI-powered chatbot for a healthcare company. The chatbot will provide general health information and triage symptoms. The developer must ensure the AI adheres to ethical principles. Which two actions should the developer take to promote transparency and accountability? (Choose two.)

Accepted Answer

Ensure the chatbot clearly discloses that it is an AI and not a human.. Logging AI interactions and disclosing the chatbot's AI nature both enhance transparency and accountability. Logging provides a record for audits and improvement, while disclosure ensures users are informed. Together, they build trust and support responsible AI deployment in healthcare, where ethical standards are paramount.

Answer

Use a proprietary algorithm that cannot be explained to users to protect trade secrets.

Answer

Train the chatbot on a dataset that includes only positive patient outcomes to avoid negative bias.

Answer

Allow the chatbot to provide definitive medical diagnoses without human oversight.

Question 13

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.

Answer

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

Answer

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

Answer

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

Question 14

A Salesforce admin is using Data Cloud to create a calculated insight that computes the average order value per customer over the last 90 days. The admin needs to ensure the insight updates daily. Which Data Cloud feature should they use to schedule the calculation?

Accepted Answer

Calculated Insights. Calculated Insights is the Data Cloud feature designed to create and schedule metrics and aggregations. It allows admins to define calculations like average order value over a specified period and set a refresh schedule. This ensures the insight is up-to-date for use in AI models or analytics.

Answer

Identity Resolution

Answer

Data Streams

Answer

Data Model Objects

Question 15

Cloud Kicks recently enabled Einstein Case Classification and Einstein Case Routing for its support queue. After go-live, agents report that high-priority cases from a strategic account are still landing in the general queue instead of the escalation queue. The admin confirms the escalation queue exists, is active, and has routing rules configured. Which Einstein feature should the admin verify is configured to act on the escalation criteria?

Accepted Answer

Einstein Case Routing rules that evaluate the predicted field values and route the case to the escalation queue.. Queue assignment for classified cases is handled by Einstein Case Routing, which reads the predicted field values from Einstein Case Classification and applies routing rules. Classification alone only predicts field values; it does not decide queues. Verifying that the escalation routing rule references the predicted priority and the strategic account criteria is the correct step to resolve the misrouting.

Answer

Einstein Recommendation Builder strategies that prioritize strategic accounts in the support console.

Answer

Einstein Conversation Insights topic detection that tags the case with the strategic account name.

Answer

Einstein Case Classification prediction thresholds that control which queue receives the case.

Question 16

A Salesforce administrator is setting up a Data Cloud data stream to unify customer profiles from multiple sources for use in AI models. The sources include a CRM, a marketing automation platform, and a legacy system. The administrator needs to ensure that records from these sources are matched correctly to create a unified individual. Which Data Cloud feature should be used?

Accepted Answer

Identity Resolution ruleset. Identity Resolution is the Data Cloud feature that matches and unifies records from multiple sources into a single individual. It uses rulesets to define match criteria and reconciliation to merge attributes. This unified profile is essential for AI models that require a complete view of the customer to make accurate predictions.

Answer

Calculated Insights

Answer

Data Lake objects

Answer

Data Model objects

Question 17

A sales manager at a B2B company wants to know which existing accounts are most likely to renew their annual subscriptions, using data already stored in Salesforce. The admin has been asked to enable a feature that uses historical opportunity and activity data to produce a numeric likelihood score that appears directly on the account record. Which Salesforce AI capability should the admin enable?

Accepted Answer

Einstein Prediction Builder. The requirement is a custom numeric prediction on the Account object built from historical Salesforce data, which is exactly what Einstein Prediction Builder delivers through a guided setup that creates a score field on the record. The other choices address leads, activity logging, or case text classification, none of which produce a renewal-likelihood score on accounts.

Answer

Einstein Activity Capture

Answer

Einstein Lead Scoring

Answer

Einstein Case Classification

Question 18

A small recruiting team at Cloud Kicks has no data scientists and wants Salesforce to suggest which open job candidates are most likely to accept an offer, using historical hiring outcomes already stored in its Salesforce objects. Which AI capability should the team use?

Accepted Answer

Einstein Prediction Builder. The team needs a no-code way to score records for a business outcome using existing Salesforce data. Einstein Prediction Builder creates a prediction definition on an object, trains on historical records, and writes scores back to records, which matches the recruiting team's need to rank candidates by likelihood to accept an offer. The other capabilities handle chat, activity logging, or text tone, not outcome prediction.

Answer

Einstein Activity Capture

Answer

Einstein Sentiment Analysis

Answer

Einstein Bots

Question 19

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains a field named 'LastPurchaseDate' with values that include future dates (e.g., 2025-12-31) for some customers. What should the admin do to address this data quality issue?

Accepted Answer

Investigate the source of future dates and correct or exclude invalid values after validation.. Future dates in a 'LastPurchaseDate' field indicate potential data quality issues. The correct approach is to investigate and correct or exclude invalid values after validation. This ensures the model trains on accurate data, improving churn prediction. Simply removing, replacing, or ignoring the issue can introduce bias or errors.

Answer

Remove all records with future dates from the dataset to avoid skewing the model.

Answer

Leave the future dates as-is because Einstein Discovery automatically handles outliers.

Answer

Replace future dates with the current date to maintain consistency.

Question 20

A Salesforce administrator is configuring Einstein Discovery to analyze customer churn. The dataset includes sensitive attributes such as gender and ethnicity. The administrator wants to ensure the model does not use these attributes to make predictions. Which feature should they use?

Accepted Answer

Field Exclusion. Field Exclusion is the correct feature because it directly prevents specified fields from being used as predictors in Einstein Discovery. By excluding gender and ethnicity, the administrator ensures the model does not learn from these sensitive attributes, thus mitigating bias. Other options either focus on post-hoc detection or data privacy, not on proactive feature exclusion during model training.

Answer

Einstein Trust Layer

Answer

Data Masking

Answer

Bias Detection