Salesforce-AI-Associate 100% Guarantee Download Salesforce-AI-Associate Exam PDF Q&A [Mar 18, 2025]
Get Salesforce-AI-Associate Actual Free Exam Q&As to Prepare for Your Salesforce Certification
Salesforce Salesforce-AI-Associate Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
NEW QUESTION # 32
To avoid introducing unintended bias to an AI model, which type of data should be omitted?
- A. Transactional
- B. Engagement
- C. Demographic
Answer: C
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 # 33
A developer has a large amount of data, but it is scattered across different systems and is not standardized.
Which key data quality element should they focus on to ensure the effectiveness of the AI models?
- A. Performance
- B. Consistency
- C. Volume
Answer: B
Explanation:
When data is scattered and not standardized, the key data quality element a developer should focus on is consistency. Consistency refers to the uniformity and standardization of data across different systems, which is crucial for integrating and analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce provides tools and practices for ensuring data consistency, such as data integration and management solutions that help standardize and synchronize data across platforms. For more information on Salesforce data management, refer to the Salesforce data management tools at Salesforce Data Management.
NEW QUESTION # 34
What are the three commonly used examples of AI in CRM?
- A. Predictive scoring,reporting, Image classification
- B. Predictive scoring, forecasting, recommendations
- C. Einstein Bots, face recognition, recommendations
Answer: B
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 # 35
What is a Key consideration regarding data quality in AI implementation?
- A. Integration process of AI models with Salesforce workflows
- B. Techniques from customizing AI features in Salesforce
- C. Data's role in training and fine-tuning Salesforce AI models
Answer: C
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."
NEW QUESTION # 36
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. Survivorship
- B. Societal
- C. Confirmation
Answer: C
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'sexisting 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."
NEW QUESTION # 37
What is a key challenge of human AI collaboration in decision-making?
- A. Leads to move informed and balanced decision-making
- B. Creates a reliance on AI, potentially leading to less critical thinking and oversight
- C. Reduce the need for human involvement in decision-making processes
Answer: B
Explanation:
"A key challenge of human-AI collaboration in decision-making is that it creates a reliance on AI, potentially leading to less critical thinking and oversight. Human-AI collaboration is a process that involves humans and AI systems working together to achieve a common goal or task. Human-AI collaboration can have many benefits, such as leveraging the strengths and complementing the weaknesses of both humans and AI systems.
However, human-AI collaboration can also pose some challenges, such as creating a reliance on AI, potentially leading to less critical thinking and oversight. For example, human-AI collaboration can create a reliance on AI if humans blindly trust or follow the AI recommendations without questioning or verifying their validity or rationale."
NEW QUESTION # 38
What should be done to prevent bias from entering an AI system when training it?
- A. Include Proxy variables.
- B. Import diverse training data.
- C. Use alternative assumptions.
Answer: B
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 and patterns that are relevant for the AI task. Diversetraining 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."
NEW QUESTION # 39
What should organizations do to ensure data quality for their AI initiatives?
- A. Rely on AI algorithms to automatically handle data quality issues.
- B. Prioritize model fine-tuning over data quality improvements.
- C. Collect and curate high-quality data from reliable sources.
Answer: C
Explanation:
"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."
NEW QUESTION # 40
Which features of Einstein enhance sales efficiency and effectiveness?
- A. Opportunity List View, Lead List View, Account List view
- B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
- C. Opportunity Scoring, Lead Scoring, Account Insights
Answer: C
Explanation:
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."
NEW QUESTION # 41
Which features of Einstein enhance sales efficiency and effectiveness?
- A. Opportunity List View, Lead List View, Account List view
- B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
- C. Opportunity Scoring, Lead Scoring, Account Insights
Answer: C
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."
NEW QUESTION # 42
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 outcomes.
- B. Determine data availability.
- C. Remove biased data.
Answer: B
Explanation:
Before using AI to optimize business operations, the company should first assess the availability and quality of its data. Data is thefuel 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 # 43
A data quality expert at Cloud Kicks want to ensure that each new contact contains at least an email address ...
Which feature should they use to accomplish this?
- A. Duplicate matching rule
- B. Autofill
- C. Validation rule
Answer: C
Explanation:
Explanation
"A validation rule should be used to ensure that each new contact contains at least an email address or phone number. A validation rule is a feature that checks the data entered by users for errors before saving it to Salesforce. A validation rule can help ensure data quality by enforcing certain criteria or conditions for the data values."
NEW QUESTION # 44
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
Answer: C
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."
NEW QUESTION # 45
A data quality expert at Cloud Kicks want to ensure that each new contact contains at least an email address ...
Which feature should they use to accomplish this?
- A. Duplicate matching rule
- B. Autofill
- C. Validation rule
Answer: C
Explanation:
"A validation rule should be used to ensure that each new contact contains at least an email address or phone number. A validation rule is a feature that checks the data entered by users for errors before saving it to Salesforce. A validation rule can help ensure data quality by enforcing certain criteria or conditions for the data values."
NEW QUESTION # 46
What is a benefit of a diverse, balanced, and large dataset?
- A. Data privacy
- B. Training time
- C. Model accuracy
Answer: C
Explanation:
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."
NEW QUESTION # 47
What is a potential source of bias in training data for AI models?
- A. The data is skewed toward is particular demographic or source.
- B. The data is collected from a diverse range of sources and demographics.
- C. The data is collected in area time from sources systems.
Answer: A
Explanation:
"A potential source of bias in training data for AI models is that the datais skewed toward a particular demographic or source. Skewed data means that the data is not balanced or representative of the target population or domain. Skewed data can introduce or exacerbate bias in AI models, as they may overfit or underfit the modelto a specific subset of data. For example, skewed data can lead to bias if the data is collected from a limited or biased demographic or source, such as a certain age group, gender, race, location, or platform."
NEW QUESTION # 48
What is the role of Salesforce Trust AI principles in the context of CRM system?
- A. Outlining the technical specifications for AI integration
- B. Providing a framework for AI data model accuracy
- C. Guiding ethical and responsible use of AI
Answer: C
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 practicesfor 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."
NEW QUESTION # 49
What are the potential consequences of an organization suffering from poor data quality?
- A. Revenue loss, poor customer service, and reputational damage
- B. Low employee morale, stock devaluation, and inability to attract top talent
- C. Technical debt, monolithic system architecture, and slow ETL throughput
Answer: A
Explanation:
The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision-making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation. Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
NEW QUESTION # 50
What is an implication of user consent in regard to AI data privacy?
- A. AI ensures complete data privacy by automatically obtaining user consent.
- B. AI infringes on privacy when user consent is not obtained.
- C. AI operates Independently of user privacy and consent.
Answer: B
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 byothers. 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."
NEW QUESTION # 51
A sales manager is looking to enhance the quality of lead data in their CRM system.
Which process will most likely help the team accomplish this goal?
- A. Prioritize active leads quarterly.
- B. Review and update missing lead information.
- C. Redesign the lead conversion process,
Answer: B
Explanation:
To enhance the quality of lead data in their CRM system, the most effective process is to review and update missing lead information. This process involves identifying incomplete records and filling in missing details, which can significantly improve the accuracy and usefulness of lead data. Accurate and complete lead information is crucial for effective lead scoring, prioritization, and follow-up, enhancing overall sales performance. Salesforce CRM offers data quality tools and features that assist in regularly reviewing and maintaining the accuracy of lead data. Information on managing lead data quality in Salesforce can be found at Salesforce Lead Management.
NEW QUESTION # 52
What is a key benefit of effective interaction between humans and AI systems?
- A. Leads to more informed and balanced decision making
- B. Reduces the need for human involvement
- C. Alerts humans to the presence of biased data
Answer: A
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 # 53
Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce's Trusted AI Principle of Incluslvity?
- A. Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.
- B. Test with diverse and representative datasets appropriate for how the model will be used.
- C. Test only with data from a specific region or demographic to limit the risk of data leaks.
Answer: B
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."
NEW QUESTION # 54
A sales manager wants to use AI to help sales representatives log their calls quicker and more accurately.
Which functionality provides the best solution?
- A. Sales Dialer
- B. Call Summaries
- C. Auto-Generated Sales Tasks
Answer: B
Explanation:
The best functionality to help sales representatives log their calls quicker and more accurately is the use of AI- generated Call Summaries. This feature leverages AI to analyze voice data from sales calls and automatically generate concise summaries and actionable insights, which are then logged into the CRM system. This not only speeds up the process of recording call details but also enhances the accuracy of the data captured, reducing the likelihood of human error and ensuring that important details are not missed. Salesforce provides AI tools that integrate with telephony solutions to enable these capabilities, enhancing the efficiency of sales operations. For more information on Salesforce AI features like Einstein Call Coaching that support this functionality, visit Salesforce Einstein Call Coaching.
NEW QUESTION # 55
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: C
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 # 56
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:
"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 AIpredictions, 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 # 57
......
Salesforce-AI-Associate Questions Truly Valid For Your Salesforce Exam: https://getfreedumps.passreview.com/Salesforce-AI-Associate-exam-questions.html