[Q57-Q74] Latest Salesforce Salesforce-AI-Associate First Attempt, Exam real Dumps Updated [Sep-2024]

September 29, 2024 admin 0

Categories : Salesforce-AI-Associate , Salesforce

Rate this post

Latest Salesforce Salesforce-AI-Associate First Attempt, Exam real Dumps Updated [Sep-2024]

Get the superior quality Salesforce-AI-Associate Dumps Questions from VCEPrep. Nobody can stop you from getting to your dreams now. Your bright future is just a click away!

QUESTION 57
A system admin recognizes the need to put a data management strategy in place.
What is a key component of data management strategy?

Naming Convention

Data Backup

Color Coding

Data Backup is a key component of a datamanagement 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 damage in 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.

QUESTION 58
Which features of Einstein enhance sales efficiency and effectiveness?

Opportunity List View, Lead List View, Account List view

Opportunity Scoring, Opportunity List View, Opportunity Dashboard

Opportunity Scoring, Lead Scoring, Account Insights

“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.”

QUESTION 59
What are the key components of the data quality standard?

Reviewing, Updating, Archiving

Naming, formatting, Monitoring

Accuracy, Completeness, Consistency

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.”

QUESTION 60
Which statement exemplifies Salesforces honesty guideline when training AI models?

Minimize the AI models carbon footprint and environment impact during training.

Ensure appropriate consent and transparency when using AI-generated responses.

Control bias, toxicity, and harmful content withembedded guardrails and guidance.

“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 andpreferences 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.”

QUESTION 61
Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce’s Trusted AI Principle of Incluslvity?

Test only with data from a specific region or demographic to limit the risk of data leaks.

Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.

Test with diverse and representative datasets appropriate for how the model will be used.

“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.”

QUESTION 62
What are some key benefits of AI in improving customer experiences in CRM?

Improves CRM security protocols, safeguarding sensitive customer data from potential breaches and threats

Streamlines case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions

Fully automates the customer service experience, ensuring seamless automated interactions with customers

Explanation
“Streamlining case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions are some key benefits of AI in improving customer experiences in CRM. AI can help automate and optimize various aspects of customer service, such as routing cases to the right agents, providing relevant information or suggestions, and generating reports or insights. AI can also help enhance customer satisfaction and loyalty by reducing wait times, improving response quality, and providing personalized solutions.”

QUESTION 63
What is the most likely impact that high-quality data will have on customer relationships?

Increased brand loyalty

Higher customer acquisition costs

Improved customer trust and satisfaction

Explanation
“The most likely impact that high-quality data will have on customer relationships is improved customer trust and satisfaction. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can improve customer relationships by enabling AI systems to provide personalized and relevant products, services, or solutions that meet the customers’ expectations, needs, and interests. High-quality data can also improve customer trust and satisfaction by reducing errors, delays, or waste in customer interactions.”

QUESTION 64
What is a potential source of bias in training data for AI models?

The data is collected in area time from sources systems.

The data is skewed toward is particular demographic or source.

The data is collected from a diverse range of sources and demographics.

“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.”

QUESTION 65
What is an implication of user consent in regard to AI data privacy?

AI ensures complete data privacy by automatically obtaining user consent.

AI infringes on privacy when user consent is not obtained.

AI operates Independently of user privacy and consent.

“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.”

QUESTION 66
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, withmissing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company’s operations?

The response time for product recommendations is stalled.

The accuracy of product recommendations is hindered.

The diversity of product recommendations Is Improved.

“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.”

QUESTION 67
A marketing manager wants to use AI to better engage their customers.
Which functionality provides the best solution?

Journey Optimization

Bring Your Own Model

Einstein Engagement

“EinsteinEngagement 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.”

QUESTION 68
How does the “right of least privilege” reduce the risk of handling sensitive personal data?

By limiting how many people have access to data

By reducing how many attributes are collected

By applying data retention policies

“The “right of least privilege” reduces the risk of handling sensitive personal data by limiting how many people have access to data. The “right of least privilege” is a security principle that states that each user or system should have the minimum level of access or privilege necessary to perform their tasks or functions.
The “right of least privilege” can help protect sensitive personal data from unauthorized access, misuse, or leakage.”

QUESTION 69
Which best describes the different between predictive AI and generative AI?

Predictive new and original output for a given input.

Predictive AI and generative have the same capabilities differ in the type of input theyreceive: predictive AI receives raw data whereas generation AI receives natural language.

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

“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 to generate 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.”

QUESTION 70
What is a possible outcome of poor data quality?

AI models maintain accuracy but have slower response times.

Biases in data can be inadvertently learned and amplified by AI systems.

AI predictions become more focused and less robust.

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.”

QUESTION 71
What is the key difference between generative and predictive AI?

Generative AI creates new content based on existing data and predictive AI analyzes existing data.

Generative AI finds content similar to existing data and predictive AI analyzes existing data.

Generative AI analyzes existing data and predictive AI creates new content based on existing data.

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.”

QUESTION 72
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?

Duplication

Usage

Consent

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.”

QUESTION 73
What is a societal implication of excluding ethics in AI development?

Faster and cheaper development

More innovation and creativity

Harm to marginalized communities

Excluding ethics in AI development can lead to societal implications such as harm to marginalized communities. When ethical considerations are not integrated into AI development, the resulting technologies may perpetuate or amplify biases, leading to unfair treatment or discrimination against certain groups. This can reinforce existing social inequalities and prevent these communities from benefiting equally from the advancements in AI technology. Salesforce is committed to responsible AI development and emphasizes the importance of ethical considerations in their development practices to prevent such outcomes. Details on Salesforce’s approach to ethical AI and its importance can be found at Salesforce Ethical AI.

QUESTION 74
Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic…

Geographic

Cryptographic

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.”

Loading …