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Salesforce AI Associate Exam Questions
- Salesforce AI Associate Certifications
- Salesforce Associate Certifications
- Topic 1: AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.
- Topic 2: AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
- Topic 3: Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply the Trusted AI Principles of Salesforce to given scenarios.
- Topic 4: Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Free Salesforce Salesforce AI Associate Exam Actual Questions
Note: Premium Questions for Salesforce AI Associate were last updated On Jul. 07, 2026 (see below)
How does an organization benefit from using AI to personalize the shopping experience of online customers?
''An organization benefits from using AI to personalize the shopping experience of online customers by increasing customer satisfaction. AI can help provide customized and relevant product recommendations, offers, or content based on the customers' preferences, behavior, or needs. AI can also help create a more engaging and interactive shopping experience by using natural language processing (NLP) or computer vision techniques. Personalized shopping experiences can improve customer satisfaction by meeting their expectations, needs, and interests.''
What is the main focus of the Accountability principle in Salesforce's Trusted AI Principles?
''The main focus of the Accountability principle in Salesforce's Trusted AI Principles is taking responsibility for one's actions toward customers, partners, and society. Accountability means that AI systems should be designed and developed with respect for the impact and consequences of their actions on others. Accountability also means that AI developers and users should be aware of and adhere to the ethical, legal, and regulatory standards and expectations of their industry and domain.''
What is the key difference between generative and predictive AI?
''The key difference between generative and predictive AI is that generative AI creates new content based on existing data and predictive AI analyzes existing data. Generative AI is a type of AI that can generate novel content such as images, text, music, or video based on existing data or inputs. Predictive AI is a type of AI that can analyze existing data or inputs and make predictions or recommendations based on patterns or trends.''
What is machine learning?
''A data model is a machine learning feature used in Salesforce. A data model is a representation or abstraction of a real-world phenomenon or process using data structures and algorithms. A data model can be used to describe, analyze, or predict various aspects of the phenomenon or process using machine learning techniques.''
Cloud Kicks uses Einstein to generate predictions but is not seeing accurate results. What is a potential reason for this?
AI models rely on high-quality data to produce accurate and reliable predictions. Poor data quality---such as missing values, inconsistent formatting, or biased data---can negatively impact AI performance.
Option A (Incorrect): If Cloud Kicks is using Einstein AI, it is unlikely that they are using the wrong product, as Einstein is designed for predictive analytics. The issue is more likely related to data quality or model training.
Option B (Correct): Poor data quality is one of the most common reasons for inaccurate AI predictions. If the input data contains errors, biases, or incomplete information, the AI model will generate flawed insights. Regular data cleaning and preprocessing are essential for improving prediction accuracy.
Option C (Incorrect): Having too much data does not necessarily result in inaccurate predictions. In fact, more data can improve model performance if properly structured and cleaned. However, if the data is noisy or unstructured, it may lead to inconsistencies.
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