2024 Realistic Verified Salesforce-AI-Associate exam dumps Q&As - Salesforce-AI-Associate Free Update
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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:
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NEW QUESTION # 35
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
Answer: B
Explanation:
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."
NEW QUESTION # 36
Which best describes the different between predictive AI and generative AI?
- A. 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
- 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 new and original output for a given input.
Answer: C
Explanation:
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."
NEW QUESTION # 37
Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic...
- A. Cryptographic
- B. Geographic
- C. Geographic
Answer: B
Explanation:
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."
NEW QUESTION # 38
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, with missing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company's operations?
- A. The response time for product recommendations is stalled.
- B. The accuracy of product recommendations is hindered.
- C. The diversity of product recommendations Is Improved.
Answer: B
Explanation:
Explanation
"The incomplete data quality will impact the company's operations by hindering the accuracy of product recommendations. Incomplete data means that the data is missing some values or attributes that are relevant for the AI task. Incomplete data can affect the performance and reliability of AI models, as they may not have enough information to learn from or make accurate predictions. For example, incomplete customer records can affect the quality of product recommendations, as the AI model may not be able to capture the customers' preferences, behavior, or needs."
NEW QUESTION # 39
Which type of bias results from data being labeled according to stereotypes?
- A. Interaction
- B. Societal
- C. Association
Answer: B
Explanation:
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."
NEW QUESTION # 40
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.
Answer: A
Explanation:
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."
NEW QUESTION # 41
A system admin recognizes the need to put a data management strategy in place.
What is a key component of data management strategy?
- A. Naming Convention
- B. Data Backup
- C. Color Coding
Answer: B
Explanation:
Explanation
Data Backup is a key component of a data management strategy. A data backup is a process of creating and storing copies of data in a separate location or device to prevent data loss or damagein case of a disaster, accident, or malicious attack. A data backup can help ensure data availability, reliability, and security by allowing data to be restored or recovered in the event of a data breach, corruption, or deletion. A data management strategy should include a data backup plan that defines the frequency, scope, method, and location of data backups, as well as the roles and responsibilities of the data backup team.
NEW QUESTION # 42
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. Use demographic data to identify minority groups.
- B. Integrate AI models that auto-correct biased data.
- C. Implement Salesforce's Trusted AI Principles.
Answer: C
Explanation:
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."
NEW QUESTION # 43
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 required to create accurate AI data insights.
- C. Data quality is unnecessary because AI can work with all data types.
Answer: B
Explanation:
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."
NEW QUESTION # 44
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. Determine data availability.
- B. Determine data outcomes.
- C. Remove biased data.
Answer: A
Explanation:
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.
NEW QUESTION # 45
In the context of Salesforce's Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?
- A. Empower users to contribute to the growing body of knowledge of leading AI research.
- B. Empower users to off all skill level to build AI application with clicks, not code.
- C. Empower users to solve challenging technical problems using neural networks.
Answer: B
Explanation:
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."
NEW QUESTION # 46
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. Ensured compatibility of AI algorithms with the system's Infrastructure
- B. Reduced need for healthcare expertise in interpreting AI outouts
- C. Enhanced accuracy and reliability of medical predictions and diagnoses
Answer: C
Explanation:
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."
NEW QUESTION # 47
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
Answer: B
Explanation:
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."
NEW QUESTION # 48
How does data quality impact the trustworthiness of Al-driven decisions?
- A. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
- B. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
- C. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
Answer: A
Explanation:
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."
NEW QUESTION # 49
To avoid introducing unintended bias to an AI model, which type of data should be omitted?
- A. Transactional
- B. Demographic
- C. Engagement
Answer: B
Explanation:
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."
NEW QUESTION # 50
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. Relies on preprogrammed rules to make decisions
- C. Can perfectly mimic human intelligence and decision-making
Answer: A
Explanation:
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."
NEW QUESTION # 51
How does a data quality assessment impact business outcome for companies using AI?
- A. Accelerates the delivery of new AI solutions
- B. Improves the speed of AI recommendations
- C. Provides a benchmark for AI predictions
Answer: C
Explanation:
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."
NEW QUESTION # 52
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