Total 106 Questions
Last Updated On : 18-Jun-2025
Preparing with Salesforce-AI-Associate practice test is essential to ensure success on the exam. This Salesforce SP25 test allows you to familiarize yourself with the Salesforce-AI-Associate exam questions format and identify your strengths and weaknesses. By practicing thoroughly, you can maximize your chances of passing the Salesforce certification spring 2025 release exam on your first attempt. Surveys from different platforms and user-reported pass rates suggest Salesforce-AI-Associate practice exam users are ~30-40% more likely to pass.
A financial institution plans a campaign for preapproved credit cards? How should they implement Salesforce’s Trusted AI Principle of Transparency?
A. Communicate how risk factors such as credit score can impact customer eligibility.
B. Flag sensitive variables and their proxies to prevent discriminatory lending practices.
C. Incorporate customer feedback into the model’s continuous training.
Explanation:
“Flagging sensitive variables and their proxies to prevent discriminatory lending practices is how they should implement Salesforce’s Trusted AI Principle of Transparency. Transparency is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with. Flagging sensitive variables and their proxies means identifying and marking variables that can potentially cause discrimination or unfair treatment based on a person’s identity or characteristics, such as age, gender, race, income, or credit score. Flagging sensitive variables and their proxies can help implement Transparency by allowing users to understand and evaluate the data used or generated by AI systems.
What is an implication of user consent in regard to AI data privacy?
A. AI ensures complete data privacy by automatically obtaining user consent.
B. AI infringes on privacy when user consent is not obtained.
C. AI operates Independently of user privacy and consent.
Explanation:
“AI infringes on privacy when user consent is not obtained. User consent is the permission or agreement given by a user to allow their personal data to be collected, used, shared, or stored by others. User consent is an important aspect of data privacy, which is the right of individuals to control how their personal data is handled by others. AI infringes on privacy when user consent is not obtained because it violates the user’s rights and preferences regarding their personal data.”
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?
A. Different types of data models used in Salesforce
B. Different types of automation tools used in Salesforce
C. Different types of AI that can be applied in Salesforce
Explanation:
Predictive analytics, machine learning, natural language processing (NLP), and computer vision are all types of artificial intelligence technologies that can be applied in Salesforce to enhance various aspects of business operations and customer interactions. Predictive analytics uses historical data to make predictions about future events. Machine learning involves algorithms that can learn from and make decisions based on data. NLP is concerned with the interactions between computers and humans using natural language, and computer vision interprets and processes visual information from the world to make sense of it in the way humans do. Salesforce harnesses these AI technologies, particularly through its Einstein platform, to provide powerful tools that help businesses automate tasks, make better decisions, and offer more personalized services. For more on how Salesforce utilizes these AI technologies, you can explore the Einstein AI services documentation at Salesforce Einstein.
What is an example of ethical debt?
A. Violating a data privacy law and failing to pay fines
B. Delaying an AI product launch to retrain an AI data model
C. Launching an AI feature after discovering a harmful bias
Expanation:
Ethical debt refers to the future ethical consequences that arise when organizations fail to address ethical concerns during the design and development of AI systems.
By launching an AI feature after discovering a harmful bias, the company knowingly releases a flawed system, accumulating ethical debt that may require costly corrections later. This decision can lead to:
- Unfair or biased outcomes, negatively impacting users.
- Loss of trust, as customers may feel misrepresented or discriminated against.
- Regulatory and reputational risks, requiring future fixes and policy adjustments.
❌ Why not the other options?
A. Violating a data privacy law is a legal issue, not ethical debt. Failing to pay fines is a compliance problem.
B. Delaying a launch to retrain a model shows ethical responsibility, not debt — it’s a sign of good practice.
Which features of Einstein enhance sales efficiency and effectiveness?
A. Opportunity Scoring, Lead Scoring, Account Insights
B. Opportunity List View, Lead List View, Account List view
C. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
Explanation:
Salesforce Einstein is designed to enhance sales productivity by using AI to provide intelligent recommendations, insights, and predictions. Let's break down why each item in Option A contributes to sales efficiency:
1. Opportunity Scoring
Uses AI to analyze past deals and identify factors that lead to wins.
Provides a score for each opportunity so sales reps can focus on the most promising ones.
Helps prioritize work and increase close rates.
2. Lead Scoring
Predicts which leads are most likely to convert.
Enables reps to prioritize follow-ups and work smarter, not harder.
3. Account Insights
Surfaces relevant news and updates about accounts.
Keeps sales reps informed so they can engage with personalized and timely messages.
Why the other options are incorrect:
B. Opportunity List View, Lead List View, Account List View
These are standard Salesforce UI features, not Einstein AI-powered tools.
They improve organization but do not use AI to enhance sales effectiveness.
C. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
Only Opportunity Scoring is an Einstein AI feature.
The others are UI elements or dashboards, not intelligent features.
Cloud Kicks wants to optimize its business operations by incorporating AI into CRM. What should the company do first to prepare its data for use with AI?
A. Remove biased data.
B. Determine data availability
C. Determine data outcomes.
Explanation:
Before using AI in CRM (Customer Relationship Management), the first step is to ensure that relevant, sufficient, and high-quality data is actually available. Without available and accessible data, no AI model can be trained or generate insights.
Why "Determine data availability" is the first step:
AI models require large amounts of relevant data to learn patterns.
Understanding what data you have, where it’s stored, and whether it’s clean and accessible is critical before training any AI or using AI features in Salesforce like Einstein.
It's about laying the foundation for AI readiness.
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior. What Is a crucial factor that the developer should consider during selection?
A. Number of variables ipn the dataset
B. Size of the dataset
C. Age of the dataset
Explanation:
The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data.
Cloud Kicks wants to implement AI features on its Salesforce Platform but has concerns about potential ethical and privacy challenges. What should they consider doing to minimize potential AI bias?
A. Use demographic data to identify minority groups.
B. Integrate AI models that auto-correct biased data.
C. Implement Salesforce's Trusted AI Principles.
Explanation:
When implementing AI, especially in a CRM like Salesforce, it’s critical to proactively address ethical, fairness, and privacy issues. Salesforce provides a framework known as Trusted AI Principles to guide responsible AI usage.
Why “Implement Salesforce's Trusted AI Principles” is correct:
These principles include responsibility, accountability, transparency, fairness, and privacy.
They help organizations minimize bias, protect user data, and ensure fairness in AI decision-making.
Salesforce encourages customers to adopt these principles when using features like Einstein to ensure ethical use of AI.
Cloud Kicks is testing a new AI model. Which approach aligns with Salesforce's Trusted AI Principle of Inclusivity?
A. Test only with data from a specific region or demographic to limit the risk of data leaks.
B. Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.
C. Test with diverse and representative datasets appropriate for how the model will be used.
Explanation:
Testing with diverse and representative datasets appropriate for how the model will be used aligns with Salesforce’s Trusted AI Principle of Inclusivity. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Testing with diverse and representative datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain.
What is a sensitive variable that car esc to bias?
A. Education level
B. Country
C. Gender
Explanation:
Gender is a sensitive variable that can lead to bias. A sensitive variable is a variable that can potentially cause discrimination or unfair treatment based on a person’s identity or characteristics. For example, gender is a sensitive variable because it can affect how people are perceived, treated, or represented by AI systems.
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