Salesforce-AI-Associate Exam Dumps - Salesforce AI Associate Questions and Answers

“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.”

“Poor data quality is a potential reason for not seeing accurate results from an AI model. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor data quality 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.”

 “Lead scoring, opportunity forecasting, and case classification are AI applications that can help a business analyst improve their sales processes and customer support. Lead scoring can help prioritize leads based on their likelihood to convert, opportunity forecasting can help predict future sales or revenue based on historical data and trends, and case classification can help categorize and route cases based on their attributes.”

For Cloud Kicks, using automated chat as the primary support channel, the recommended Einstein feature is Bots. Einstein Bots are designed to automate customer interactions on common issues through chat and messaging platforms. They can handle routine requests, provide quick answers to frequently asked questions, and escalate more complex issues to human agents. Using Einstein Bots helps improve customer service efficiency and speed, leading to enhanced customer satisfaction. To learn more about setting up and optimizing Einstein Bots for a business, you can visit the Salesforce documentation on Einstein Bots at Salesforce Einstein Bots.

“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.”

“It is critical to consider privacy concerns when dealing with AI and CRM data because it ensures compliance with laws and regulations. Data privacy is the right of individuals to control how their personal data is collected, used, shared, or stored by others. Data privacy laws and regulations are legal frameworks that define and enforce the rights and obligations of data subjects, data controllers, and data processors regarding personal data. Data privacy laws and regulations vary by country, region, or industry, and may impose different requirements or restrictions on how AI and CRM data can be handled.”

“The most likely impact that high-quality data will have on customer relationships is improved customer trust and satisfaction. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can improve customer relationships by enabling AI systems to provide personalized and relevant products, services, or solutions that meet the customers’ expectations, needs, and interests. High-quality data can also improve customer trust and satisfaction by reducing errors, delays, or waste in customer interactions.”

When data is scattered and not standardized, the key data quality element a developer should focus on is consistency. Consistency refers to the uniformity and standardization of data across different systems, which is crucial for integrating and analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce provides tools and practices for ensuring data consistency, such as data integration and management solutions that help standardize and synchronize data across platforms. For more information on Salesforce data management, refer to the Salesforce data management tools at Salesforce Data Management.

Neural networks are a key AI tool designed as a web of interconnected nodes, similar to the human brain's structure. Each connection, or synapse, in a neural network is guided by weights and biases that are adjusted during the learning process. These weights and biases determine the strength and influence of one node over another, facilitating complex pattern recognition and decision-making processes. Neural networks are extensively used in machine learning for tasks like image and speech recognition, among others. For more on neural networks in the context of Salesforce AI, see the Salesforce AI documentation on Neural Networks.

“A key benefit of effective interaction between humans and AI systems is that it leads to more informed and balanced decision making. Effective interaction means that humans and AI systems can communicate and collaborate with each other in a clear, natural, and respectful way. Effective interaction can help leverage the strengths and complement the weaknesses of both humans and AI systems. Effective interaction can also help increase trust, confidence, and satisfaction in using AI systems.”