Salesforce-AI-Associate Exam Dumps, Reliable Salesforce-AI-Associate Exam Practice

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
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	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Topic 4	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 Trusted AI Principles of Salesforce to given scenarios.

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Salesforce Certified AI Associate Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?

A. Dashboard of Current Leads and Cases
B. Sales path and Automaton Case Escalations
C. Einstein Lead Scoring and Case Classification

Answer: C

Explanation:
"Einstein Lead Scoring and Case Classification are the capabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI to optimize sales and service processes by providing insights and recommendations based on data.
Einstein Lead Scoring can help prioritize leads based on their likelihood to convert, while Einstein Case Classification can help categorize and route cases based on their attributes."

NEW QUESTION # 30
A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?

A. Duplication
B. Usage
C. Completeness

Answer: C

Explanation:
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness.
Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights. Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.

NEW QUESTION # 31
Which type of bias imposes a system 's values on others?

A. Automation
B. Societal
C. Association

Answer: B

NEW QUESTION # 32
What is the most likely impact that high-quality data will have on customer relationships?

A. Increased brand loyalty
B. Improved customer trust and satisfaction
C. Higher customer acquisition costs

Answer: B

Explanation:
Explanation
"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."

NEW QUESTION # 33
Which action introduces bias in the training data used for AI algorithms?

A. Using a dataset that underrepresents perspectives and populations
B. Using a large dataset that is computationally expensive
C. Using a dataset that represents diverse perspectives and populations

Answer: A

Explanation:
Introducing bias in training data for AI algorithms occurs when the dataset used underrepresents certain perspectives and populations. This type of bias can skew AI predictions, making the system less fair and accurate. For example, if a dataset predominantly contains information from one demographic group, the AI's performance may not generalize well to other groups, leading to biased or unfair outcomes. Salesforce discusses the impact of biased training data and ways to mitigate this in their AI ethics guidelines, which can be explored further in the Salesforce AI documentation on Responsible Creation of AI.

NEW QUESTION # 34
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