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Glen King Glen King

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実用的なSalesforce-AI-Associate無料過去問試験-試験の準備方法-更新するSalesforce-AI-Associate合格体験記

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Salesforce-AI-Associate合格体験記 & Salesforce-AI-Associate資格試験

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Salesforce Salesforce-AI-Associate 認定試験の出題範囲：

トピック
出題範囲

トピック 1

AI のためのデータ: データ品質の重要性およびデータ品質のさまざまな要素やコンポーネントに関する質問は、このトピックに関連しています。

トピック 2

CRM における AI 機能: AI の利点と CRM の機能について理解します。

トピック 3

AI の倫理的考慮事項: 機械学習における人間の偏見、透明性の欠如など、AI の倫理的課題を詳しく掘り下げます。このトピックでは、Salesforce の信頼できる AI 原則を特定のシナリオに適用する方法についても説明します。

トピック 4

AI の基礎: このトピックでは、Salesforce における AI の主要な原則とアプリケーションについて説明します。また、さまざまな種類の AI とその機能にも焦点を当てています。

Salesforce Certified AI Associate Exam 認定 Salesforce-AI-Associate 試験問題 (Q72-Q77):

質問 # 72
What should be done to prevent bias from entering an AI system when training it?

A. Include Proxy variables.
B. Import diverse training data.
C. Use alternative assumptions.

正解：B

解説：
Explanation
"Using diverse training data is what should be done to prevent bias from entering an AI system when training it. Diverse training data means that the data covers a wide range of features andpatterns that are relevant for the AI task. Diverse training data can help prevent bias by ensuring that the AI system learns from a balanced and representative sample of the target population or domain. Diverse training data can also help improve the accuracy and generalization of the AI system by capturing more variations and scenarios in the data."

質問 # 73
Which features of Einstein enhance sales efficiency and effectiveness?

A. Opportunity Scoring, Lead Scoring, Account Insights
B. Opportunity List View, Lead List View, Account List view
C. Opportunity Scoring, Opportunity List View, Opportunity Dashboard

正解：A

解説：
"Opportunity Scoring, Lead Scoring, Account Insights are features of Einstein that enhance sales efficiency and effectiveness. Opportunity Scoring and Lead Scoring use predictive models to assign scores to opportunities and leads based on their likelihood to close or convert. Account Insights use natural language processing (NLP) to provide relevant news and insights about accounts based on their industry, location, or events."

質問 # 74
What is a possible outcome of poor data quality?

A. AI models maintain accuracy but have slower response times.
B. Biases in data can be inadvertently learned and amplified by AI systems.
C. AI predictions become more focused and less robust.

正解：B

解説：
"A possible outcome of poor data quality is that biases in data can be inadvertently learned and amplified by AI systems. 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 systems, as they may not have enough or correct information to learn from or make accurate predictions. Poor data quality can also introduce or exacerbate biases in data, such as human bias, societal bias, or confirmation bias, which can affect the fairness and ethics of AI systems."

質問 # 75
The Cloud technical team is assessing the effectiveness of their AI development processes?
Which established Salesforce Ethical Maturity Model should the team use to guide the development of trusted AI solution?

A. Ethical AI Process Maturity Model
B. Ethical AI Prediction Maturity Model
C. Ethical AI practice Maturity Model

正解：A

解説：
Explanation
"The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use to guide the development of trusted AI solutions. The Ethical AI Process Maturity Model is a framework that helps assess and improve the ethical and responsible practices and processes involved in developing and deploying AI systems. The Ethical AI Process Maturity Model consists of five levels of maturity: Ad Hoc, Aware, Defined, Managed, and Optimized. The Ethical AI Process Maturity Model can help guide the development of trusted AI solutions by providing a roadmap and best practices for achieving higher levels of ethical maturity."

質問 # 76
What are the potential consequences of an organization suffering from poor data quality?

A. Low employee morale, stock devaluation, and inability to attract top talent
B. Revenue loss, poor customer service, and reputational damage
C. Technical debt, monolithic system architecture, and slow ETL throughput

正解：B

解説：
The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision- making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation.
Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.

質問 # 77
......

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Glen King Glen King

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