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2025 Valid Salesforce-AI-Associate Real Exam Questions (Updated) 100% Dumps & Practice Exam [Q51-Q69]

Published date January 26, 2025

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2025 Valid Salesforce-AI-Associate Real Exam Questions (Updated) 100% Dumps & Practice Exam

[UPDATED 2025] Salesforce Salesforce-AI-Associate Questions Prepare with Free Demo of PDF

Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply Trusted AI Principles of Salesforce to given scenarios.
Topic 2	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Topic 3	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Topic 4	AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.

NEW QUESTION 51
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?

Performance

Consistency

Volume

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 52
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.”

NEW QUESTION 53
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?

Dashboard of Current Leads and Cases

Sales path and Automaton Case Escalations

Einstein Lead Scoring and Case Classification

“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.”

NEW QUESTION 54
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>

Transparency

Inclusivity

Accountability

Explanation
“Conducting a series of Consequence Scanning workshops to support testing diverse datasets is an action that practices Salesforce’s Trusted AI Principle of Inclusivity. Inclusivity is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Conducting Consequence Scanning workshops means engaging with various stakeholders to identify and assess the potential impacts and implications of AI systems on different groups or domains. Conducting Consequence Scanning workshops can help practice Inclusivity by ensuring that diverse datasets are used to test and evaluate AI systems.”

NEW QUESTION 55
How does AI which CRM help sales representatives better understand previous customer interactions?

Creates, localizes, and translates product descriptions

Triggers personalized service replies

Provides call summaries

“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.”

NEW QUESTION 56
What should an organization do to enforce consistency across accounts for newly entered records?

Merge all duplicate accounts into a single record when duplicate entries are detected.

Input the data exactly as it appears from the source, such as the company’s website or social media,

Implement naming conventions or a predefined list of user-selectable values for organization-wide records.

To ensure consistency across accounts for newly entered records, organizations should implement naming conventions or a predefined list of user-selectable values. This approach standardizes data entry, reducing variations and errors. It also helps in maintaining clean data which is essential for accurate reporting and analytics. Using standardized naming conventions ensures that all users adhere to a consistent format, making it easier to manage and analyze data across the organization. For more information on best practices for data management in Salesforce, refer to Salesforce’s documentation on Data Management Best Practices.

NEW QUESTION 57
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.”

NEW QUESTION 58
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.”

NEW QUESTION 59
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

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 60
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company’s recommendations?

The response time for the product

The accuracy of the product

The diversity of the product

Incomplete data quality negatively impacts the accuracy of product recommendations made by Cloud Kicks.
If data is missing or incomplete, the AI models used for product recommendation may not have enough information to accurately predict customer preferences and behavior. This leads to recommendations that may not align well with customer needs, reducing customer satisfaction and potentially affecting sales.
Ensuring complete and accurate data is crucial for effective recommendation systems. Salesforce discusses the impact of data quality on AI outcomes and strategies to enhance data integrity in their documentation on AI and data management, which can be referenced at Data Management for AI.

NEW QUESTION 61
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?

Ethical AI Prediction Maturity Model

Ethical AI Process Maturity Model

Ethical AI practice Maturity Model

“The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use toguide 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 62
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?

Number of variables ipn the dataset

Size of the dataset

Age of the dataset

“The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect thefeasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data.”

NEW QUESTION 63
What role does data quality play in the ethical us of AI applications?

High-quality data is essential for ensuringunbased and for fair AI decisions, promoting ethical use, and preventing discrimi…

High-quality data ensures the process of demographic attributes requires for personalized campaigns.

Low-quality data reduces the risk of unintended bias as the datais not overfitted to demographic groups.

“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 64
Which action should be taken to develop and implement trusted generated AI with Salesforce’s safety guideline in mind?

Develop right-sized models to reduce our carbon footprint.

Create guardrails that mitigates toxicity and protect PII

Be transparent when AI has created and automatically delivered content.

“Creating guardrails that mitigate toxicity and protect PII is an action that should be taken to develop and implement trusted generativeAI with Salesforce’s safety guideline in mind. Salesforce’s safety guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the safety and well-being of humans and the environment. Creatingguardrails means implementing measures or mechanisms that can prevent or limit the potential harm or risk caused by AI systems. For example, creating guardrails can help mitigate toxicity by filtering out inappropriate or offensive content generated by AIsystems. Creating guardrails can also help protect PII by masking or anonymizing personal or sensitive information generated by AI systems.”

NEW QUESTION 65
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company’s recommendations?

The response time for the product

The accuracy of the product

The diversity of the product

Incomplete data quality negatively impacts the accuracy of product recommendations made by Cloud Kicks.
If data is missing or incomplete, the AI models used for product recommendation may not have enough information to accurately predict customer preferences and behavior. This leads to recommendations that may not align well with customer needs, reducing customer satisfaction and potentially affecting sales. Ensuring complete and accurate data is crucial for effective recommendation systems. Salesforce discusses the impact of data quality on AI outcomes and strategies to enhance data integrity in their documentation on AI and data management, which can be referenced at Data Management for AI.

NEW QUESTION 66
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.”

NEW QUESTION 67
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?

Enhanced accuracy and reliability of medical predictions and diagnoses

Ensured compatibility of AI algorithms with the system’s Infrastructure

Reduced need for healthcare expertise in interpreting AI outouts

“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 dataused for AI applications in healthcare is accurate, complete, consistent, and relevant.”

NEW QUESTION 68
A Salesforce administrator creates a new field to capture an order’s destination country.
Which field type should they use to ensure data quality?

Text

Picklist

Number

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.”

NEW QUESTION 69
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.”

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2025 Valid Salesforce-AI-Associate Real Exam Questions (Updated) 100% Dumps & Practice Exam [Q51-Q69]

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