Salesforce-AI-AssociateのPDF問題集で2023年11月10日試験問題 有効なSalesforce-AI-Associate問題集
究極のSalesforce-AI-Associate準備ガイドで無料最新のSalesforce練習テスト問題集
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 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
| トピック 5 |
|
質問 # 15
Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce's Trusted AI Principle of Incluslvity?
- A. Test with diverse and representative datasets appropriate for how the model will be used.
- B. Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.
- C. Test only with data from a specific region or demographic to limit the risk of data leaks.
正解：A
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 # 16
What is a benefit of a diverse, balanced, and large dataset?
- A. Model accuracy
- B. Training time
- C. Data privacy
正解：A
解説：
Explanation
"Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting or underfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data."
質問 # 17
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?
- A. Chances of bias are remove
- B. Chances of bias are aggravated
- C. Chances of bIas and mitigated
正解：C
解説：
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."
質問 # 18
Which Einstein capability uses emails to create content for Knowledge articles?
- A. Predict
- B. Discover
- C. Generate
正解：C
解説：
Explanation
"Einstein Generate uses emails to create content for Knowledge articles. Einstein Generate is a natural language generation (NLG) feature that can automatically write summaries, descriptions, or recommendations based on data or text inputs. For example, Einstein Generate can analyze email conversations between agents and customers and generate draft articles for the Knowledge base."
質問 # 19
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 # 20
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>
- A. Inclusivity
- B. Transparency
- C. Accountability
正解：A
解説：
Explanation
"Conducting a series of Consequence Scanning workshops to support testing diverse datasets is an action that practices Salesforce's Trusted AI Principle of Inclusivity. Inclusivity is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Conducting Consequence Scanning workshops means engaging with various stakeholders to identify and assess the potential impacts and implications of AI systems on different groups or domains. Conducting Consequence Scanning workshops can help practice Inclusivity by ensuring that diverse datasets are used to test and evaluate AI systems."
質問 # 21
How does a data quality assessment impact business outcome for companies using AI?
- A. Accelerates the delivery of new AI solutions
- B. Provides a benchmark for AI predictions
- C. Improves the speed of AI recommendations
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 # 22
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：
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 # 23
What is an implication of user consent in regard to AI data privacy?
- A. AI operates Independently of user privacy and consent.
- B. AI ensures complete data privacy by automatically obtaining user consent.
- C. AI infringes on privacy when user consent is not obtained.
正解：C
解説：
Explanation
"AI infringes on privacy when user consent is not obtained. User consent is the permission or agreement given by a user to allow their personal data to be collected, used, shared, or stored by others. User consent is an important aspect of data privacy, which is the right of individuals to control how their personal data is handled by others. AI infringes on privacy when user consent is not obtained because it violates the user's rights and preferences regarding their personal data."
質問 # 24
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 # 25
What is the key difference between generative and predictive AI?
- A. Generative AI analyzes existing data and predictive AI creates new content based on existing data.
- B. Generative AI finds content similar to existing data and predictive AI analyzes existing data.
- C. Generative AI creates new content based on existing data and predictive AI analyzes existing data.
正解：C
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 # 26
What is a key benefit of effective interaction between humans and AI systems?
- A. Alerts humans to the presence of biased data
- B. Leads to more informed and balanced decision making
- C. Reduces the need for human involvement
正解：B
解説：
Explanation
"A key benefit of effective interaction between humans and AI systems is that it leads to more informed and balanced decision making. Effective interaction means that humans and AI systems can communicate and collaborate with each other in a clear, natural, and respectful way. Effective interaction can help leverage the strengths and complement the weaknesses of both humans and AI systems. Effective interaction can also help increase trust, confidence, and satisfaction in using AI systems."
質問 # 27
What is a possible outcome of poor data quality?
- A. Biases in data can be inadvertently learned and amplified by AI systems.
- B. AI predictions become more focused and less robust.
- C. AI models maintain accuracy but have slower response times.
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 # 28
What is a Key consideration regarding data quality in AI implementation?
- A. Data's role in training and fine-tuning Salesforce AI models
- B. Integration process of AI models with Salesforce workflows
- C. Techniques from customizing AI features in Salesforce
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 # 29
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 # 30
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. Usage
- B. Duplication
- C. Consent
正解：B
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 # 31
Which statement exemplifies Salesforces honesty guideline when training AI models?
- A. Ensure appropriate consent and transparency when using AI-generated responses.
- B. Minimize the AI models carbon footprint and environment impact during training.
- C. Control bias, toxicity, and harmful content with embedded guardrails and guidance.
正解：A
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
A Salesforce administrator creates a new field to capture an order's destination country.
Which field type should they use to ensure data quality?
- A. Number
- B. Text
- C. Picklist
正解：C
解説：
Explanation
"A picklist field type should be used to ensure data quality for capturing an order's destination country. A picklist field type allows the user to select one or more predefined values from a list. A picklist field type can ensure data quality by enforcing consistency, accuracy, and completeness of the data values."
質問 # 33
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 # 34
A healthcare company implements an algorithm to analyze patient data and assist in medical diagnosis.
Which primary role does data Quality play In this AI application?
- A. Reduced need for healthcare expertise in interpreting AI outouts
- B. Ensured compatibility of AI algorithms with the system's Infrastructure
- C. Enhanced accuracy and reliability of medical predictions and diagnoses
正解：C
解説：
Explanation
"Data quality plays a crucial role in enhancing the accuracy and reliability of medical predictions and diagnoses. Poor data quality can lead to inaccurate or misleading results, which can have serious consequences for patients' health and well-being. Therefore, it is important to ensure that the data used for AI applications in healthcare is accurate, complete, consistent, and relevant."
質問 # 35
......
合格率 取得する秘訣はSalesforce-AI-Associate認定試験エンジンPDF：https://jp.fast2test.com/Salesforce-AI-Associate-premium-file.html