[Jun 20, 2024] Salesforce-AI-Associate Test Engine files, Salesforce-AI-Associate Dumps PDF
Latest Salesforce Salesforce-AI-Associate PDF and Dumps (2024) Free Exam Questions Answers
Salesforce Salesforce-AI-Associate Exam Syllabus Topics:
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NEW QUESTION # 18
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?
- A. Different types of automation tools used in Salesforce
- B. Different types of AI that can be applied in Salesforce
- C. Different types of data models used in Salesforce
Answer: B
Explanation:
Predictive analytics, machine learning, natural language processing (NLP), and computer vision are all types of artificial intelligence technologies that can be applied in Salesforce to enhance various aspects of business operations and customer interactions. Predictive analytics uses historical data to make predictions about future events. Machine learning involves algorithms that can learn from and make decisions based on data.
NLP is concerned with the interactions between computers and humans using natural language, and computer vision interprets and processes visual information from the world to make sense of it in the way humans do.
Salesforce harnesses these AI technologies, particularly through its Einstein platform, to provide powerful tools that help businesses automate tasks, make better decisions, and offer more personalized services. For more on how Salesforce utilizes these AI technologies, you can explore the Einstein AI services documentation at Salesforce Einstein.
NEW QUESTION # 19
What are the three commonly used examples of AI in CRM?
- A. Predictive scoring, forecasting, recommendations
- B. Predictive scoring, reporting, Image classification
- C. Einstein Bots, face recognition, recommendations
Answer: A
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 # 20
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. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
- C. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
Answer: C
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 # 21
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. Rich Text Area
- B. Text
- C. Multi-Select Picklist
Answer: B
Explanation:
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."
NEW QUESTION # 22
The Cloud technical team is assessing the effectiveness of their AI development processes?
Which established Salesforce Ethical Maturity Model should the team use to guide the development of trusted AI solution?
- A. Ethical AI Prediction Maturity Model
- B. Ethical AI practice Maturity Model
- C. Ethical AI Process Maturity Model
Answer: C
Explanation:
Explanation
"The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use to guide the development of trusted AI solutions. The Ethical AI Process Maturity Model is a framework that helps assess and improve the ethical and responsible practices and processes involved in developing and deploying AI systems. The Ethical AI Process Maturity Model consists of five levels of maturity: Ad Hoc, Aware, Defined, Managed, and Optimized. The Ethical AI Process Maturity Model can help guide the development of trusted AI solutions by providing a roadmap and best practices for achieving higher levels of ethical maturity."
NEW QUESTION # 23
Which Einstein capability uses emails to create content for Knowledge articles?
- A. Discover
- B. Predict
- C. Generate
Answer: C
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."
NEW QUESTION # 24
Cloud Kicks wants to use AI to enhance its sales processesand customer support.
Which capacity should they use?
- A. Sales path and Automaton Case Escalations
- B. Einstein Lead Scoring and Case Classification
- C. Dashboard of Current Leads and Cases
Answer: B
Explanation:
"Einstein Lead Scoring and Case Classification are thecapabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI tooptimize sales and service processes by providing insights and recommendations based ondata. 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."
NEW QUESTION # 25
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:
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."
NEW QUESTION # 26
What are the key components of the data quality standard?
- A. Naming, formatting, Monitoring
- B. Accuracy, Completeness, Consistency
- C. Reviewing, Updating, Archiving
Answer: B
Explanation:
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."
NEW QUESTION # 27
What are the potential consequences of an organization suffering from poor data quality?
- A. Technical debt, monolithic system architecture, and slow ETL throughput
- B. Low employee morale, stock devaluation, and inability to attract top talent
- C. Revenue loss, poor customer service, and reputational damage
Answer: C
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 # 28
How does poor data quality affect predictive and generative AI models?
- A. Creates inaccurate results
- B. Decreases storage efficiency
- C. Increases raw data volume
Answer: A
Explanation:
Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis.
Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.
NEW QUESTION # 29
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 new and original output for a given input.
- C. 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.
Answer: B
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 # 30
What role does data quality play in the ethical us of AI applications?
- A. Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.
- B. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
- C. High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi...
Answer: C
Explanation:
Explanation
"High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data."
NEW QUESTION # 31
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. Validation rule
- B. Autofill
- C. Duplicate matching rule
Answer: A
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 # 32
What is the best method to safeguard customer data privacy?
- A. Track customer data consent preferences.
- B. Automatically anonymize all customer data.
- C. Archive customer data on a recurring schedule.
Answer: A
Explanation:
"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."
NEW QUESTION # 33
What role does data quality play in the ethical us of AI applications?
- A. Low-quality data reduces the risk of unintended bias as the datais not overfitted to demographic groups.
- B. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
- C. High-quality data is essential for ensuringunbased and for fair AI decisions, promoting ethical use, and preventing discrimi...
Answer: C
Explanation:
"High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data."
NEW QUESTION # 34
What are the three commonly used examples of AI in CRM?
- A. Predictive scoring, forecasting, recommendations
- B. Predictive scoring, reporting, Image classification
- C. Einstein Bots, face recognition, recommendations
Answer: A
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
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