SALESFORCE SALESFORCE-AI-ASSOCIATE ADVANCED TESTING ENGINE | VALID SALESFORCE-AI-ASSOCIATE EXAM DISCOUNT

Salesforce Salesforce-AI-Associate Advanced Testing Engine | Valid Salesforce-AI-Associate Exam Discount

Salesforce Salesforce-AI-Associate Advanced Testing Engine | Valid Salesforce-AI-Associate Exam Discount

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Topic 2
  • Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
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 Trusted AI Principles of Salesforce to given scenarios.
Topic 4
  • 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.

Salesforce Certified AI Associate Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails.
Which data quality dimension should be assessed to reduce these communication Inefficiencies?

  • A. Duplication
  • B. Usage
  • C. Consent

Answer: A

Explanation:
"Duplication is the data quality dimension that should be assessed to reduce communication inefficiencies.
Duplication means that the data contains multiple copies or instances of the same record or value.
Duplication can cause confusion, errors, or waste in data analysis and processing. For example, duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose."


NEW QUESTION # 64
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company's recommendations?

  • A. The response time for the product
  • B. The diversity of the product
  • C. The accuracy of the product

Answer: C

Explanation:
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.


NEW QUESTION # 65
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

  • A. Chances of bIas and mitigated
  • B. Chances of bias are remove
  • C. Chances of bias are aggravated

Answer: A

Explanation:
Explanation
"Data quality and transparency can help mitigate the chances of bias in generative AI. Data quality means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can help mitigate bias by ensuring that the generative AI model learns from a balanced and representative sample of the target population or domain. Data transparency means that the data sources, methods, and processes are clear and open to inspection and verification. Data transparency can help mitigate bias by allowing users to understand and evaluate the data used or generated by the generative AI model."


NEW QUESTION # 66
Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails.
Which data quality dimension should be assessed to reduce these communication Inefficiencies?

  • A. Duplication
  • B. Usage
  • C. Consent

Answer: A

Explanation:
Explanation
"Duplication is the data quality dimension that should be assessed to reduce communication inefficiencies.
Duplication means that the data contains multiple copies or instances of the same record or value. Duplication can cause confusion, errors, or waste in data analysis and processing. For example, duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose."


NEW QUESTION # 67
What are the key components of the data quality standard?

  • A. Reviewing, Updating, Archiving
  • B. Accuracy, Completeness, Consistency
  • C. Naming, formatting, Monitoring

Answer: B

Explanation:
"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 andvalid 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."


NEW QUESTION # 68
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