更新された2024年04月合格させるSalesforce-AI-Associate試験リアル練習テスト問題
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質問 # 36
Cloud Kicks wants to optimize its business operations by incorporating AI into its CRM.
What should the company do first to prepare its data for use with AI?
- A. Remove biased data.
- B. Determine data outcomes.
- C. Determine data availability.
正解：C
解説：
Explanation
Before using AI to optimize business operations, the company should first assess the availability and quality of its data. Data is the fuel for AI, and without sufficient and relevant data, AI cannot produce accurate and reliable results. Therefore, the company should identify what data it has, where it is stored, how it is accessed, and how it is maintained. This will help the company understand the feasibility and scope of its AI projects.
質問 # 37
What are the three commonly used examples of AI in CRM?
- A. Predictive scoring, forecasting, recommendations
- B. Einstein Bots, face recognition, recommendations
- C. Predictive scoring, reporting, Image classification
正解：A
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 # 38
What is an example of ethical debt?
- A. Delaying an AI product launch to retrain an AI data model
- B. Violating a data privacy law and falling to pay fines
- C. Launching an AI feature after discovering a harmful bias
正解：C
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 # 39
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 analyzes existing data and predictive AI creates new content based on existing data.
- C. Generative AI finds content similar to existing data and predictive AI analyzes 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 # 40
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 # 41
What is the rile of data quality in achieving AI business Objectives?
- A. Data quality is important for maintain Ai data storage limits
- B. Data quality is unnecessary because AI can work with all data types.
- C. Data quality is required to create accurate AI data insights.
正解：C
解説：
Explanation
"Data quality is required to create accurate AI data insights. 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 quality can also affect the accuracy and validity of AI data insights, as they reflect the quality of the data used or generated by AI systems."
質問 # 42
An administrator at Cloud Kicks wants to ensure that a field is set up on the customer record so their preferred name can be captured.
Which Salesforce field type should the administrator use to accomplish this?
- A. Multi-Select Picklist
- B. Text
- C. Rich Text Area
正解：B
解説：
Explanation
"A text field type should be used to capture the customer's preferred name. A text field type allows the user to enter any combination of letters, numbers, or symbols. A text field type can be used to store names, addresses, phone numbers, or other personal information."
質問 # 43
In the context of Salesforce's Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?
- A. Empower users to solve challenging technical problems using neural networks.
- B. Empower users to off all skill level to build AI application with clicks, not code.
- C. Empower users to contribute to the growing body of knowledge of leading AI research.
正解：B
解説：
Explanation
"The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery."
質問 # 44
Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results?
What to a potential mason for this?
- A. Poor data quality
- B. Too much data
- C. The wrong product
正解：A
解説：
Explanation
"Poor data quality is a potential reason for not seeing accurate results from an AI model. 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 models, as they may not have enough or correct information to learn from or make accurate predictions."
質問 # 45
Cloud Kicks wants to ensure that multiple records for the same customer are removed in Salesforce.
Which feature should be used to accomplish this?
- A. Standardized field names
- B. Trigger deletion of old records
- C. Duplicate management
正解：C
解説：
Explanation
"Duplicate management should be used to remove multiple records for the same customer in Salesforce.
Duplicate management is a feature that helps prevent and manage duplicate records in Salesforce. Duplicate management can help define matching rules, duplicate rules, and alert messages to detect and merge duplicate records."
質問 # 46
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 # 47
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. Age
- C. Consistency
正解：C
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 # 48
Which action should be taken to develop and implement trusted generated AI with Salesforce's safety guideline in mind?
- A. Create guardrails that mitigates toxicity and protect PII
- B. Develop right-sized models to reduce our carbon footprint.
- C. Be transparent when AI has created and automatically delivered content.
正解：A
解説：
Explanation
"Creating guardrails that mitigate toxicity and protect PII is an action that should be taken to develop and implement trusted generative AI with Salesforce's safety guideline in mind. Salesforce's safety guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the safety and well-being of humans and the environment. Creating guardrails means implementing measures or mechanisms that can prevent or limit the potential harm or risk caused by AI systems. For example, creating guardrails can help mitigate toxicity by filtering out inappropriate or offensive content generated by AI systems. Creating guardrails can also help protect PII by masking or anonymizing personal or sensitive information generated by AI systems."
質問 # 49
What should be done to prevent bias from entering an AI system when training it?
- A. Use alternative assumptions.
- B. Import diverse training data.
- C. Include Proxy variables.
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 # 50
A marketing manager wants to use AI to better engage their customers.
Which functionality provides the best solution?
- A. Einstein Engagement
- B. Journey Optimization
- C. Bring Your Own Model
正解：A
解説：
Explanation
"Einstein Engagement provides the best solution for a marketing manager who wants to use AI to better engage their customers. Einstein Engagement is a feature that uses AI to optimize email marketing campaigns by providing insights and recommendations on the best time, frequency, content, and subject lines to send emails to each customer. Einstein Engagement can help increase customer engagement, retention, and loyalty by delivering personalized and relevant messages."
質問 # 51
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 aggravated
- C. Chances of bias are remove
正解：A
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 # 52
How is natural language processing (NLP) used in the context of AI capabilities?
- A. To understand and generate human language
- B. To cleanse and prepare data for AI implementations
- C. To interpret and understand programming language
正解：A
解説：
Explanation
"Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts."
質問 # 53
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?
- A. Einstein Lead Scoring and Case Classification
- B. Dashboard of Current Leads and Cases
- C. Sales path and Automaton Case Escalations
正解：A
解説：
Explanation
"Einstein Lead Scoring and Case Classification are the capabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI to optimize sales and service processes by providing insights and recommendations based on data. Einstein Lead Scoring can help prioritize leads based on their likelihood to convert, while Einstein Case Classification can help categorize and route cases based on their attributes."
質問 # 54
A business analyst (BA) wants to improve business by enhancing their sales processes and customer..
Which AI application should the BA use to meet their needs?
- A. Sales data cleansing and customer support data governance
- B. Machine learning models and chatbot predictions
- C. Lead scoring, opportunity forecasting, and case classification
正解：C
解説：
Explanation
"Lead scoring, opportunity forecasting, and case classification are AI applications that can help a business analyst improve their sales processes and customer support. Lead scoring can help prioritize leads based on their likelihood to convert, opportunity forecasting can help predict future sales or revenue based on historical data and trends, and case classification can help categorize and route cases based on their attributes."
質問 # 55
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