How should I use these Data for AI practice questions?

Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

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AI Associate · topic practice

Data for AI practice questions

Practise Salesforce AI Associate AI Associate Data for AI practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Data for AI

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What the exam tests

What to know about Data for AI

Data for AI questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Data for AI exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Data for AI questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Data for AI practice questions

What to know about Data for AI

Common Data for AI exam traps

Data for AI questions

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

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?

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?

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

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?

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?

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

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

Which THREE factors should be considered when evaluating the quality of a dataset for an AI model?

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

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?

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?

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?

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?

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?

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?

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

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

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

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?

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