Salesforce-AI-Associate Practice Test Questions

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.

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.”

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.

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.

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.

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.

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.

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.

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.

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.