[2023年10月最新リリース] 合格できるSalesforce-AI-Associate試験にはリアル問題とアンサー
合格できるSalesforce-AI-Associateレビューガイド、頼もしいSalesforce-AI-Associateテストエンジン
質問 # 25
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce's Trusted AI Principle of Transparency?
- A. Communicate how risk factors such as credit score can impact customer eligibility.
- B. Flag sensitive variables and their proxies to prevent discriminatory lending practices.
- C. Incorporate customer feedback into the model's continuous training.
正解：B
解説：
Explanation
"Flagging sensitive variables and their proxies to prevent discriminatory lending practices is how they should implement Salesforce's Trusted AI Principle of Transparency. Transparency is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with. Flagging sensitive variables and their proxies means identifying and marking variables that can potentially cause discrimination or unfair treatment based on a person's identity or characteristics, such as age, gender, race, income, or credit score. Flagging sensitive variables and their proxies can help implement Transparency by allowing users to understand and evaluate the data used or generated by AI systems."
質問 # 26
How does a data quality assessment impact business outcome for companies using AI?
- A. Provides a benchmark for AI predictions
- B. Improves the speed of AI recommendations
- C. Accelerates the delivery of new AI solutions
正解：A
解説：
Explanation
"A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AI predictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain."
質問 # 27
What is machine learning?
- A. AI that creates new content
- B. AI that can grow its intelligence
- C. A data model used in Salesforce
正解：C
解説：
Explanation
"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."
質問 # 28
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?
- A. Accuracy
- B. Usage
- C. Consistency
正解：C
解説：
Explanation
"Consistency is the data quality dimension that is affected by multiple variations of state and country values in contact records. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Inconsistent data can cause confusion, errors, or duplication in data analysis and processing."
質問 # 29
How does the "right of least privilege" reduce the risk of handling sensitive personal data?
- A. By limiting how many people have access to data
- B. By applying data retention policies
- C. By reducing how many attributes are collected
正解：A
解説：
Explanation
"The "right of least privilege" reduces the risk of handling sensitive personal data by limiting how many people have access to data. The "right of least privilege" is a security principle that states that each user or system should have the minimum level of access or privilege necessary to perform their tasks or functions.
The "right of least privilege" can help protect sensitive personal data from unauthorized access, misuse, or leakage."
質問 # 30
What role does data quality play in the ethical us of AI applications?
- A. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
- B. Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.
- C. High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi...
正解：C
解説：
Explanation
"High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data."
質問 # 31
Which statement exemplifies Salesforces honesty guideline when training AI models?
- A. Control bias, toxicity, and harmful content with embedded guardrails and guidance.
- B. Minimize the AI models carbon footprint and environment impact during training.
- C. Ensure appropriate consent and transparency when using AI-generated responses.
正解：C
解説：
Explanation
"Ensuring appropriate consent and transparency when using AI-generated responses is a statement that exemplifies Salesforce's honesty guideline when training AI models. Salesforce's honesty guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for honesty and integrity in how they work and what they produce. Ensuring appropriate consent and transparency means respecting and honoring the choices and preferences of users regarding how their data is used or generated by AI systems. Ensuring appropriate consent and transparency also means providing clear and accurate information and documentation about the AI systems and their outputs."
質問 # 32
Which best describes the different between predictive AI and generative AI?
- A. Predictive new and original output for a given input.
- B. Predictive AI and generative have the same capabilities differ in the type of input they receive:
predictive AI receives raw data whereas generation AI receives natural language. - C. Predictive AI uses machine learning to classes or predict output from its input data whereas generative AI does not use machine learning to generate its output
正解：A
解説：
Explanation
"The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques togenerate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos."
質問 # 33
What is the best method to safeguard customer data privacy?
- A. Track customer data consent preferences.
- B. Archive customer data on a recurring schedule.
- C. Automatically anonymize all customer data.
正解：A
解説：
Explanation
"Tracking customer data consent preferences is the best method to safeguard customer data privacy. Data privacy is the right of individuals to control how their personal data is collected, used, shared, or stored by others. Tracking customer data consent preferences means respecting and honoring the choices and preferences of customers regarding their personal data. Tracking customer data consent preferences can help ensure compliance with data privacy laws and regulations, as well as build trust and loyalty with customers."
質問 # 34
Which features of Einstein enhance sales efficiency and effectiveness?
- A. Opportunity Scoring, Lead Scoring, Account Insights
- B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
- C. Opportunity List View, Lead List View, Account List view
正解：A
解説：
Explanation
"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 # 35
How does data quality impact the trustworthiness of Al-driven decisions?
- A. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
- B. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
- C. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
正解：B
解説：
Explanation
"High-quality data improves the reliability and credibility of AI-driven decisions, fostering trust among users.
High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task.
High-quality data can improve the performance and reliability of AI systems, as they have enough and correct information to learn from and make accurate predictions. High-quality data can also improve the trustworthiness of AI-driven decisions, as users can have more confidence and satisfaction in using AI systems."
質問 # 36
Cloud kicks wants to decrease the workload for its customer care agents by implementing a chatbot on its website that partially deflects incoming cases by answering frequency asked questions Which field of AI is most suitable for this scenario?
- A. Natural language processing
- B. Predictive analytics
- C. Computer vision
正解：A
解説：
Explanation
"Natural language processing is the field of AI that is most suitable for this scenario. Natural language processing (NLP) is a branch of AI that enables computers to understand and generate natural language, such as speech or text. NLP can be used to create conversational interfaces that can interact with users using natural language, such as chatbots. Chatbots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the user's intent and context."
質問 # 37
What is a possible outcome of poor data quality?
- A. Biases in data can be inadvertently learned and amplified by AI systems.
- B. AI models maintain accuracy but have slower response times.
- C. AI predictions become more focused and less robust.
正解：A
解説：
Explanation
"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 # 38
A service leader wants use AI to help customer resolve their issues quicker in a guided self-serve application.
Which Einstein functionality provides the best solution?
- A. Bots
- B. Recommendation
- C. Case Classification
正解：A
解説：
Explanation
"Bots provide the best solution for a service leader who wants to use AI to help customers resolve their issues quicker in a guided self-serve application. Bots are a feature that uses natural language processing (NLP) and natural language understanding (NLU) to create conversational interfaces that can interact with customers using text or voice. Bots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the customer's intent and context."
質問 # 39
How does an organization benefit from using AI to personalize the shopping experience of online customers?
- A. Customers are more likely to visit competitor sites that personalize their experience.
- B. Customers are more likely to share personal information with a site that personalizes their experience.
- C. Customers are more likely to be satisfied with their shopping experience.
正解：C
解説：
Explanation
"An organization benefits from using AI to personalize the shopping experience of online customers by increasing customer satisfaction. AI can help provide customized and relevant product recommendations, offers, or content based on the customers' preferences, behavior, or needs. AI can also help create a more engaging and interactive shopping experience by using natural language processing (NLP) or computer vision techniques. Personalized shopping experiences can improve customer satisfaction by meeting their expectations, needs, and interests."
質問 # 40
Why is it critical to consider privacy concerns when dealing with AI and CRM data?
- A. Ensures compliance with laws and regulations
- B. Increases the volume of data collected
- C. Confirms the data is accessible to all users
正解：A
解説：
Explanation
"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."
質問 # 41
What is an example of ethical debt?
- A. Launching an AI feature after discovering a harmful bias
- B. Violating a data privacy law and falling to pay fines
- C. Delaying an AI product launch to retrain an AI data model
正解：A
解説：
Explanation
"Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is a term that describes the potential harm or risk caused by unethical or irresponsible decisions or actions related to AI systems. Ethical debt can accumulate over time and have negative consequences for users, customers, partners, or society. For example, launching an AI feature after discovering a harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect their trust, satisfaction, or well-being."
質問 # 42
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 Prediction Maturity Model
- B. Ethical AI practice Maturity Model
- C. Ethical AI Process Maturity Model
正解：C
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 # 43
What can bias in AI algorithms in CRM lead to?
- A. Ethical challenges in CRM systems
- B. Advertising cost increases
- C. Personalization and target marketing changes
正解：A
解説：
Explanation
"Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness and ethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers' identity or characteristics."
質問 # 44
Which type of bias results from data being labeled according to stereotypes?
- A. Interaction
- B. Societal
- C. Association
正解：B
解説：
Explanation
"Societal bias results from data being labeled according to stereotypes. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. For example, societal bias can occur when data is labeled based on gender, race, ethnicity, or religion stereotypes."
質問 # 45
What are some of the ethical challenges associated with AI development?
- A. Implicit transparency of AI systems, which makes It easy for users to understand and trust their decisions
- B. Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
- C. Inherent neutrality of AI systems, which eliminates any potential for human bias in decision-making
正解：B
解説：
Explanation
"Some of the ethical challenges associated with AI development are the potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes. Human bias can arise from the data used to train the models, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult tounderstand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness."
質問 # 46
What are the key components of the data quality standard?
- A. Accuracy, Completeness, Consistency
- B. Naming, formatting, Monitoring
- C. Reviewing, Updating, Archiving
正解：A
解説：
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 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."
質問 # 47
A customer using Einstein Prediction Builder is confused about why a certain prediction was made.
Following Salesforce's Trusted AI Principle of Transparency, which customer information should be accessible on the Salesforce Platform?
- A. A marketing article of the product that clearly outlines the oroduct's capabilities and features
- B. An explanation of how Prediction Builder works and a link to Salesforce's Trusted AI Principles
- C. An explanation of the prediction's rationale and a model card that describes how the model was created
正解：C
解説：
Explanation
"An explanation of the prediction's rationale and a model card that describes how the model was created should be accessible on the Salesforce Platform following Salesforce's Trusted AI Principle of Transparency.
Transparency means that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with."
質問 # 48
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