Salesforce Salesforce-AI-Associate問題集で100%カバー率リアル試験問題（更新された78問あります)
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質問 # 45
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. An explanation of the prediction's rationale and a model card that describes how the model was created
- B. A marketing article of the product that clearly outlines the oroduct's capabilities and features
- C. An explanation of how Prediction Builder works and a link to Salesforce's Trusted AI Principles
正解：A
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 # 46
Cloud Kicks wants to develop a solution to predict customers product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.
Which data quality dimension is affected in this scenario?
- A. Completeness
- B. Consistency
- C. Accuracy
正解：B
解説：
Explanation
"Consistency is the data quality dimension that is affected in this scenario. 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. For example, using different field types for the same attribute can affect the consistency of the data."
質問 # 47
What are the key components of the data quality standard?
- A. Naming, formatting, Monitoring
- B. Reviewing, Updating, Archiving
- C. Accuracy, Completeness, Consistency
正解：C
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 # 48
To avoid introducing unintended bias to an AI model, which type of data should be omitted?
- A. Transactional
- B. Engagement
- C. Demographic
正解：C
解説：
Explanation
"Demographic data should be omitted to avoid introducing unintended bias to an AI model. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairness and ethics of AI systems."
質問 # 49
How does a data quality assessment impact business outcome for companies using AI?
- A. Improves the speed of AI recommendations
- B. Provides a benchmark for AI predictions
- C. Accelerates the delivery of new AI solutions
正解：B
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 # 50
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. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
- C. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
正解：C
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 # 51
How does AI which CRM help sales representatives better understand previous customer interactions?
- A. Triggers personalized service replies
- B. Provides call summaries
- C. Creates, localizes, and translates product descriptions
正解：B
解説：
Explanation
"Providing call summaries is how AI with CRM helps sales representatives better understand previous customer interactions. Call summaries are a feature that uses natural language processing (NLP) to analyze voice conversations between sales representatives and customers and generate summaries or transcripts of the calls. Call summaries can help sales representatives better understand previous customer interactions by providing key information, insights, or action items from the calls."
質問 # 52
What are the three commonly used examples of AI in CRM?
- A. Einstein Bots, face recognition, recommendations
- B. Predictive scoring, forecasting, recommendations
- C. Predictive scoring, reporting, Image classification
正解：B
解説：
Explanation
"Predictive scoring, forecasting, and recommendations are three commonly used examples of AI in CRM.
Predictive scoring can help prioritize leads, opportunities, and customers based on their likelihood to convert, churn, or buy. Forecasting can help predict future sales, revenue, or demand based on historical data and trends. Recommendations can help suggest the best products, services, or actions for each customer based on their preferences, behavior, and needs."
質問 # 53
Salesforce defines bias as using a person's Immutable traits to classify them or market to them.
Which potentially sensitive attribute is an example of an immutable trait?
- A. Email address
- B. Financial status
- C. Nickname
正解：B
解説：
Explanation
"Financial status is an example of an immutable trait. Immutable traits are characteristics that are inherent, fixed, or unchangeable. For example, financial status is an immutable trait because it is determined by factors beyond one's control, such as birth, inheritance, or economic conditions. Nickname and email address are not immutable traits because they can be changed by choice or preference."
質問 # 54
Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.
Which type of bias is most likely to be encountered in this scenario?
- A. Societal
- B. Survivorship
- C. Confirmation
正解：C
解説：
Explanation
"Confirmation bias is most likely to be encountered in this scenario. Confirmation bias is a type of bias that occurs when data or information confirms or supports one's existing beliefs or expectations. For example, confirmation bias can occur when a product recommendation feature only recommends shoes of a given color based on the customer's purchase history, without considering other factors or preferences that may influence their choice."
質問 # 55
Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic...
- A. Geographic
- B. Geographic
- C. Cryptographic
正解：A
解説：
Explanation
"Demographic data is the data that Salesforce automatically excludes from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairness and ethics of AI systems. Salesforce excludes demographic data from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns by ensuring that the models are based on behavioral data rather than personal data."
質問 # 56
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 # 57
Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce's Trusted AI Principle of Incluslvity?
- A. Test only with data from a specific region or demographic to limit the risk of data leaks.
- B. Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.
- C. Test with diverse and representative datasets appropriate for how the model will be used.
正解：C
解説：
Explanation
"Testing with diverse and representative datasets appropriate for how the model will be used aligns with Salesforce's Trusted AI Principle of Inclusivity. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences.
Testing with diverse and representative datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain."
質問 # 58
Which type of bias imposes a system 's values on others?
- A. Association
- B. Societal
- C. Automation
正解：B
解説：
Explanation
"Societal bias is the type of bias that imposes a system's values on others. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. Societal bias can affect the fairness and ethics of AI systems, as they may affect how different groups or domains are perceived, treated, or represented by AI systems. For example, societal bias can occur when AI systems impose a system's values on others, such as using Western standards of beauty or success to judge or rank people from other cultures."
質問 # 59
What is the role of Salesforce Trust AI principles in the context of CRM system?
- A. Providing a framework for AI data model accuracy
- B. Guiding ethical and responsible use of AI
- C. Outlining the technical specifications for AI integration
正解：B
解説：
Explanation
"The role of Salesforce Trust AI principles in the context of CRM systems is guiding ethical and responsible use of AI. Salesforce Trust AI principles are a set of guidelines and best practices for developing and using AI systems in a responsible and ethical way. The principles include Accountability, Fairness & Equality, Transparency & Explainability, Privacy & Security, Reliability & Safety, Inclusivity & Diversity, Empowerment & Education. The principles aim to ensure that AI systems are aligned with the values and interests of customers, partners, and society."
質問 # 60
Cloud Kicks wants to create a custom service analytics application to analyze cases in Salesforce. The application should rely on accurate data to ensure efficient case resolution.
Which data quality dimension Is essential for this custom application?
- A. Duplication
- B. Consistency
- C. Age
正解：B
解説：
Explanation
"Consistency is the data quality dimension that is essential for creating a custom service analytics application to analyze cases in Salesforce. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Consistent data can ensure that the custom application can accurately and efficiently analyze cases and provide meaningful insights."
質問 # 61
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. Consent
- B. Usage
- C. Duplication
正解：C
解説：
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."
質問 # 62
What is a Key consideration regarding data quality in AI implementation?
- A. Data's role in training and fine-tuning Salesforce AI models
- B. Techniques from customizing AI features in Salesforce
- C. Integration process of AI models with Salesforce workflows
正解：A
解説：
Explanation
"Data's role in training and fine-tuning Salesforce AI models is a key consideration regarding data quality in AI implementation. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Data's role in training and fine-tuning Salesforce AI models means understanding how data is used to build, train, test, and improve AI models in Salesforce, such as Einstein Prediction Builder or Einstein Discovery."
質問 # 63
What can bias in AI algorithms in CRM lead to?
- A. Advertising cost increases
- B. Ethical challenges in CRM systems
- C. Personalization and target marketing changes
正解：B
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 # 64
What is the key difference between generative and predictive AI?
- A. Generative AI creates new content based on existing data and predictive AI analyzes existing data.
- B. Generative AI finds content similar to existing data and predictive AI analyzes existing data.
- C. Generative AI analyzes existing data and predictive AI creates new content based on existing data.
正解：A
解説：
Explanation
"The key difference between generative and predictive AI is that generative AI creates new content based on existing data and predictive AI analyzes existing data. Generative AI is a type of AI that can generate novel content such as images, text, music, or video based on existing data or inputs. Predictive AI is a type of AI that can analyze existing data or inputs and make predictions or recommendations based on patterns or trends."
質問 # 65
How does the "right of least privilege" reduce the risk of handling sensitive personal data?
- A. By reducing how many attributes are collected
- B. By applying data retention policies
- C. By limiting how many people have access to data
正解：C
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 # 66
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