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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 Mar. 24, 2025 (see below)
What are the key components of the data quality standard?
''Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.''
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company's recommendations?
Incomplete data quality negatively impacts the accuracy of product recommendations made by Cloud Kicks. If data is missing or incomplete, the AI models used for product recommendation may not have enough information to accurately predict customer preferences and behavior. This leads to recommendations that may not align well with customer needs, reducing customer satisfaction and potentially affecting sales. Ensuring complete and accurate data is crucial for effective recommendation systems. Salesforce discusses the impact of data quality on AI outcomes and strategies to enhance data integrity in their documentation on AI and data management, which can be referenced at Data Management for AI.
What is a potential outcome of using poor-quality data in AI application?
''A potential outcome of using poor-quality data in AI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor-quality data can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting.''
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