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Salesforce Salesforce-AI-Associate 認定試験の出題範囲：
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質問 # 37
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：
"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 # 38
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：
"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 # 39
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 # 40
What is a potential outcome of using poor-quality data in AI application?
- A. AI models may produce biased or erroneous results.
- B. AI model training becomes slower and less efficient
- C. AI models become more interpretable
正解：A
解説：
Explanation
"A potential outcome of using poor-quality data in AI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete,inconsistent, irrelevant, or outdated for the AI task. Poor-quality data 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. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting."
質問 # 41
What should organizations do to ensure data quality for their AI initiatives?
- A. Collect and curate high-quality data from reliable sources.
- B. Rely on AI algorithms to automatically handle data quality issues.
- C. Prioritize model fine-tuning over data quality improvements.
正解：A
解説：
"Organizations should collect and curate high-quality data from reliable sources to ensure data quality for their AI initiatives. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Reliable sources mean that the data is trustworthy, credible, and authoritative.
Collecting and curating high-quality data from reliable sources can improve the performance and reliability of AI systems."
質問 # 42
Cloud Kicks prepares a dataset for an AI model and identifies some inconsistencies in the data.
What is the most appropriate action the company should take?
- A. Investigate the data inconsistencies and apply data quality techniques.
- B. Adjust the Al model to account for the data inconsistencies.
- C. Increase the quantity of data being used for training the model
正解：A
解説：
When inconsistencies in data are identified, the most appropriate action is to investigate these inconsistencies and apply data quality techniques. Adjusting the AI model to accommodate poor quality data or simply increasing the quantity of data without addressing the underlying issues does not solve the problem and can lead to less reliable AI outputs. Proper data cleaning, normalization, and validation are necessary steps to ensure that the data fed into an AI model is accurate and reliable, thus enhancing the model's performance.
Salesforce provides guidelines on how to manage and improve data quality, including practical steps for addressing data inconsistencies, detailed at Improving Data Quality in Salesforce.
質問 # 43
What are some of the ethical challenges associated with AI development?
- A. Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
- B. Implicit transparency of AI systems, which makes It easy for users to understand and trust their decisions
- C. Inherent neutrality of AI systems, which eliminates any potential for human bias in decision-making
正解：A
解説：
"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 themodels, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult to understand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness."
質問 # 44
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce's Trusted AI Principle of Transparency?
- A. Incorporate customer feedback into the model's continuous training.
- B. Communicate how risk factors such as credit score can impact customer eligibility.
- C. Flagsensitive variables and their proxies to prevent discriminatory lending practices.
正解：C
解説：
"Flagging sensitive variables and their proxies to prevent discriminatory lending practicesis 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 variablesthat 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 # 45
How does a data quality assessment impact business outcome for companies using AI?
- A. Improves the speed of AI recommendations
- B. Accelerates the delivery of new AI solutions
- C. Provides a benchmark for AI predictions
正解：C
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 # 46
How does AI assist in lead qualification?
- A. Scores leads based on customer data
- B. Automatically interacts with prospects
- C. Creates personalized SMS campaigns
正解：A
解説：
AI assists in lead qualification primarily by scoring leads based on customer data. This process, known as lead scoring, uses machine learning algorithms to evaluate leads against a set of predefined criteria that reflect potential interest and sales readiness. The scores assigned help sales teams prioritize their efforts toward leads most likely to convert, thus improving efficiency and success rates in sales activities. Salesforce AI enhances this process through features like Einstein Lead Scoring, which automatically calculates scores based on both historical conversion data and behavioral data from prospects. For further insights, Salesforce provides detailed documentation on lead scoring with AI at Salesforce Einstein Lead Scoring.
質問 # 47
Cloud kicks wants to develop a solution to predict customers' interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimensionof data quality is affected in this scenario?
- A. Accuracy
- B. Completeness
- C. Consistency
正解：C
解説：
"Consistency is the dimension of data quality 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 # 48
Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.
Which type of records negatively impact data quality?
- A. Complete
- B. Structured
- C. Duplicate
正解：C
解説：
Duplicate records negatively impact data quality by creating inconsistencies and confusion in database management, leading to potential errors in customer relationship management (CRM) systems like Salesforce. Duplicates can skew analytics results, lead to inefficiencies in customer service, and result in redundant marketing efforts. Salesforce offers various tools to identify and merge duplicate records, thereby maintaining high data integrity. More about managing duplicate records in Salesforce and ensuring data quality can be found in Salesforce's documentation on duplicate management at Salesforce Duplicate Management.
質問 # 49
What is the best method to safeguard customer data privacy?
- A. Archive customer data on a recurring schedule.
- B. Track customer data consent preferences.
- C. Automatically anonymize all customer data.
正解：B
解説：
"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 preferencesof 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 # 50
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. Picklist
- C. Text
正解：B
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 # 51
What is a key characteristic of machine learning in the context of AI capabilities?
- A. Uses algorithms to learn from data and make decisions
- B. Can perfectly mimic human intelligence and decision-making
- C. Relies on preprogrammed rules to make decisions
正解：A
解説：
Explanation
"Machine learning is a key characteristic of AI capabilities that uses algorithms to learn from data and make decisions. Machine learning is a branch of AI that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can analyze data, identify patterns, and make predictions or recommendations based on the data."
質問 # 52
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?
- A. Dashboard of Current Leads and Cases
- B. Sales path and Automaton Case Escalations
- C. Einstein Lead Scoring and Case Classification
正解：C
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 # 53
How does data quality impact the trustworthiness of Al-driven decisions?
- A. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
- B. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
- C. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
正解：B
解説：
"High-quality dataimproves 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 AIsystems."
質問 # 54
Cloud Kicks wants to implement AI features on its 5aiesforce Platform but has concerns about potential ethical and privacy challenges.
What should they consider doing to minimize potential AI bias?
- A. Implement Salesforce's Trusted AI Principles.
- B. Use demographic data to identify minority groups.
- C. Integrate AI models that auto-correct biased data.
正解：A
解説：
Explanation
"Implementing Salesforce's Trusted AI Principles is what Cloud Kicks should consider doing to minimize potential AI bias. Salesforce's Trusted 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."
質問 # 55
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：
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 # 56
Which type of bias results from data being labeled according to stereotypes?
- A. Interaction
- B. Societal
- C. Association
正解：B
解説：
"Societal bias results from data being labeled according to stereotypes. Societal bias is a type ofbias 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 # 57
A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?
- A. Usage
- B. Duplication
- C. Completeness
正解：C
解説：
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness.
Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights. Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.
質問 # 58
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