AI Associate Data for AI — Timed 15 Questions

Question 1

A company wants to use Einstein Prediction Builder to predict customer churn. Which data preparation step is essential before building the model?

Accepted Answer

Define the prediction objective and the target date field.. Option B is correct because Einstein Prediction Builder requires you to define the prediction objective (e.g., 'Will this customer churn?') and specify the target date field that marks the event. This step is essential as it tells the model what to predict and over what time window, enabling the automated feature engineering and model training process.

Answer

Ensure the data is in a Salesforce connected data source like Data Cloud.

Answer

Create a formula field to calculate the churn probability.

Answer

Create a new custom object to store the prediction results.

Question 2

A data scientist needs to prepare data for Einstein Discovery. The dataset includes a field 'Customer_Status__c' with values 'Active', 'Inactive', and 'Churned'. How should this field be treated?

Accepted Answer

Keep as a text field and let Einstein Discovery handle it as a categorical predictor.. Option C is correct because Einstein Discovery natively supports text fields as categorical predictors, automatically encoding them for model training. The platform handles string values like 'Active', 'Inactive', and 'Churned' without requiring manual transformation, preserving the semantic meaning and cardinality of the data.

Answer

Create separate boolean fields for each value to improve model accuracy.

Answer

Remove the field because text fields cannot be used in Einstein Discovery.

Answer

Convert to numeric values 1, 2, 3 to preserve order.

Question 3

A company uses Salesforce Data Cloud to unify customer data from multiple sources. After connecting a data stream, they notice that records are missing from the unified profile. What is the most likely cause?

Accepted Answer

The reconciliation rule is not configured for the data source.. Option D is correct because reconciliation rules in Salesforce Data Cloud define how records from different data sources are matched and merged into a unified profile. If a reconciliation rule is not configured for a data source, records from that source may not be properly linked to existing profiles, leading to missing records in the unified view. This is a common configuration step that must be completed after connecting a data stream.

Answer

The data stream object is not a standard Salesforce object.

Answer

The data stream is not activated for identity resolution.

Answer

The data source is not from Salesforce, so it cannot be unified.

Question 4

A Salesforce admin is training an Einstein Bot to answer customer questions. Which data source should the bot use to provide accurate responses?

Accepted Answer

Knowledge articles with a published status.. Knowledge articles with a published status are the correct data source because they contain curated, approved, and structured information that Einstein Bot can reliably use to generate accurate responses. The bot leverages natural language processing to match customer questions against these articles, ensuring answers are based on verified content rather than unstructured or transient data.

Answer

Chatter posts from the product team.

Answer

Case records from the last 30 days.

Answer

Lead and contact reports.

Question 5

A company uses Einstein Discovery to identify factors that increase case resolution time. After training, the model shows that 'Case_Origin__c' has high importance. What action should the company take?

Accepted Answer

Investigate the categories within Case_Origin to understand their impact.. Option C is correct because the model identifies 'Case_Origin__c' as important; analyzing its categories can reveal which origins cause delays. Option A is wrong because removing the field loses information. Option B is wrong because the model already accounts for interactions. Option D is wrong because the origin is not necessarily a data quality issue.

Answer

Remove the field from the model to reduce complexity.

Answer

Create interaction terms between Case_Origin and other fields.

Answer

Increase the data quality threshold for Case_Origin records.

Question 6

A company has set up Einstein Next Best Action with a recommendation strategy. They want to ensure that recommendations are personalized based on the customer's recent behavior. What data should be used?

Accepted Answer

Streaming data from Data Cloud that includes recent website interactions.. Option B is correct because Einstein Next Best Action requires real-time or near-real-time data to personalize recommendations based on recent customer behavior. Streaming data from Data Cloud captures website interactions as they happen, enabling the recommendation engine to use the most current signals (e.g., page views, clicks) to adjust offers dynamically.

Answer

Event data from the website tracked via Google Analytics.

Answer

Static profile fields like customer age and location.

Answer

Historical data from a data warehouse updated daily.

Question 7

A company wants to use Einstein Article Recommendations to suggest knowledge articles to support agents. What is a prerequisite for this feature?

Accepted Answer

Knowledge articles must be created and published.. Einstein Article Recommendations requires that knowledge articles are created and published in the Salesforce Knowledge base. The feature uses natural language processing (NLP) to match the context of a case or conversation with published articles, so unpublished or draft articles cannot be recommended. Without published articles, the AI model has no content to analyze or suggest.

Answer

Articles must be of a specific type, such as FAQ.

Answer

The org must be enabled for Einstein features.

Answer

A case must be open for the recommendation to appear.

Question 8

Which TWO actions are required to prepare data for an Einstein Discovery model?

Accepted Answer

Ensure the data is stored in a Salesforce object or a connected data source.. Options A and C are correct. A: Data must be in a supported object (standard or custom). C: The outcome field must be specified. Option B is not required; Einstein handles missing values. Option D is not required; data can be within Salesforce or connected. Option E is not required; features can be auto-selected.

Answer

Remove all records with missing values in any field.

Answer

Select exactly 10 predictor fields manually.

Answer

Create a separate dataset for training and validation.

Question 9

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.. Option B is correct because 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 10

Which TWO data sources can be used with Einstein Prediction Builder?

Accepted Answer

Data Cloud objects using the harmonized data model.. Einstein Prediction Builder requires structured data that can be mapped to a prediction objective. Data Cloud objects using the harmonized data model provide a unified, standardized schema that Prediction Builder can consume directly, enabling predictions across multiple Salesforce and external data sources. Standard Salesforce objects like Account and Opportunity are also supported because they contain the fields and relationships needed to train predictive models.

Answer

Files uploaded to Salesforce Files.

Answer

Chatter feed posts.

Answer

Dashboard and report snapshots.

Question 11

A company uses Salesforce Data Platform to store customer data. They want to use this data to train an AI model for lead scoring, but they are concerned about data quality. Which step should they take first to ensure the data is suitable for AI?

Accepted Answer

Profile the data to identify missing values, outliers, and inconsistencies. Profiling the data is the essential first step because it systematically identifies missing values, outliers, and inconsistencies that degrade model performance. Without this baseline assessment, any subsequent normalization or labeling would be applied to flawed data, leading to unreliable lead scoring predictions. Salesforce Data Platform supports profiling via tools like Einstein Analytics or Data Prep, which scan fields for nulls, range violations, and format errors.

Answer

Immediately normalize all numerical features

Answer

Create a labeled dataset using historical lead outcomes

Answer

Set up a data pipeline to stream data in real-time

Question 12

A data scientist is building a predictive model for customer churn using Salesforce data. The dataset has 20 features, and the target variable is highly imbalanced (5% churn, 95% non-churn). Which technique should be applied to handle the class imbalance before training?

Accepted Answer

Use Synthetic Minority Over-sampling Technique (SMOTE). SMOTE (Synthetic Minority Over-sampling Technique) is the correct choice because it generates synthetic samples for the minority class (churn) by interpolating between existing minority instances, effectively balancing the dataset without simply duplicating data. This prevents the model from being biased toward the majority class (non-churn) and improves recall for the churn class, which is critical in imbalanced classification problems.

Answer

Apply Principal Component Analysis (PCA) for dimensionality reduction

Answer

Create interaction features between existing variables

Answer

Use accuracy as the evaluation metric

Question 13

An administrator is configuring a Salesforce AI model that uses historical sales data. The data includes fields like 'Amount', 'Close_Date', and 'Lead_Source'. What is the primary purpose of data preprocessing in this context?

Accepted Answer

To clean and transform data into a format suitable for model training. Data preprocessing is essential for AI models because raw historical sales data often contains missing values, inconsistent formats, and noise. Cleaning (e.g., handling nulls in 'Amount') and transforming (e.g., encoding 'Lead_Source' into numerical features) ensure the model can learn patterns effectively, directly impacting training accuracy and convergence.

Answer

To generate visualizations for business stakeholders

Answer

To increase the storage capacity of the database

Answer

To enforce data access permissions for different user roles

Question 14

A company is deploying an AI model that recommends next best actions for sales reps. They notice that the model's recommendations are biased towards high-revenue opportunities. Which data-related action can help reduce this bias?

Accepted Answer

Oversample the underrepresented segments in the training data. Oversampling underrepresented segments in the training data directly addresses the class imbalance that causes the model to favor high-revenue opportunities. By increasing the frequency of low-revenue examples, the model learns to treat all segments more equally, reducing bias in its recommendations. This is a standard data-level technique for mitigating bias in AI models.

Answer

Use a larger neural network model

Answer

Encrypt the data before training

Answer

Remove all low-revenue opportunities from the training data

Question 15

A Salesforce admin wants to use Einstein Prediction Builder to predict case resolution time. What type of data is most critical for training this model?

Accepted Answer

Historical case records including resolution time. Einstein Prediction Builder requires historical data with known outcomes to train a supervised machine learning model. Historical case records containing actual resolution times provide the labeled examples needed for the model to learn patterns and predict future case resolution times. Without this ground truth data, the model cannot be trained to make accurate predictions.

Answer

Customer satisfaction survey responses

Answer

Product inventory levels

Answer

Employee work schedules