AI Associate Data for AI — Timed 50 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.

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

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Create a formula field to calculate the churn probability.

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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.

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

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

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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.

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The data stream object is not a standard Salesforce object.

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The data stream is not activated for identity resolution.

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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.

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Chatter posts from the product team.

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Case records from the last 30 days.

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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.

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Create interaction terms between Case_Origin and other fields.

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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.

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Event data from the website tracked via Google Analytics.

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Static profile fields like customer age and location.

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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.

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

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The org must be enabled for Einstein features.

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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.

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Remove all records with missing values in any field.

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Select exactly 10 predictor fields manually.

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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.

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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.

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Files uploaded to Salesforce Files.

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Chatter feed posts.

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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

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Create a labeled dataset using historical lead outcomes

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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.

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Apply Principal Component Analysis (PCA) for dimensionality reduction

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Create interaction features between existing variables

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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.

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To generate visualizations for business stakeholders

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To increase the storage capacity of the database

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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.

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Use a larger neural network model

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Encrypt the data before training

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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.

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Customer satisfaction survey responses

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Product inventory levels

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Employee work schedules

Question 16

During the data preparation phase for an AI model, a data engineer discovers that the 'AnnualRevenue' field contains some negative values. What is the best course of action?

Accepted Answer

Investigate the data source to correct the negative values. Option D is correct because negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.

Answer

Delete all records with negative revenue

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Replace negative values with the mean of positive values

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Keep negative values as they might represent returns or refunds

Question 17

Which TWO techniques are commonly used to handle missing values in a dataset for AI training?

Accepted Answer

Deletion of rows with missing values. Option B is correct because deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.

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L1 regularization

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One-hot encoding

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Min-max normalization

Question 18

Which THREE factors should be considered when selecting features for a predictive model in Salesforce?

Accepted Answer

Correlation between features to avoid multicollinearity. Option B is correct because multicollinearity occurs when two or more features are highly correlated, which can destabilize model coefficients and reduce interpretability. In Salesforce's predictive models, such as those built with Einstein Discovery, correlated features can inflate variance and lead to unreliable predictions. Avoiding multicollinearity ensures that the model's feature importance estimates are trustworthy and that the model generalizes well to new data.

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Volume of data available for each feature

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Compliance with data privacy regulations

Question 19

Which TWO are common data quality issues that can negatively impact AI model performance?

Accepted Answer

Missing values in critical fields. Missing values in critical fields (Option A) are a common data quality issue because many AI models, particularly those relying on statistical or gradient-based optimization, cannot handle null or NaN inputs without imputation or removal. If missing values are not addressed, the model may learn biased patterns or fail to converge, leading to degraded predictive performance.

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Low model accuracy during validation

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Insufficient storage space for data

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Inconsistent data governance policies

Question 20

A company wants to use its data from Salesforce to train an Einstein AI model. However, they need to exclude records where the customer has opted out of data use. Which field should they configure in the Data Manager?

Accepted Answer

A checkbox field named Data Use Opt-Out. Option A is correct because the Data Manager in Einstein AI uses a standard checkbox field named 'Data Use Opt-Out' to identify records that should be excluded from model training. When this checkbox is true, the system automatically filters out those records during data preparation, ensuring compliance with data privacy requirements.

Answer

The Record Type of the object

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A picklist field on the object

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A formula field that evaluates to true