Real Salesforce Salesforce-AI-Associate Exam Questions Study Guide [Q55-Q75]

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Real Salesforce Salesforce-AI-Associate Exam Questions Study Guide

Updated and Accurate Salesforce-AI-Associate Questions for passing the exam Quickly

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	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.
Topic 3	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Topic 4	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.

NO.55 Cloud Kicks wants to create a custom service analytics application to analyze cases in Salesforce. The application should rely on accurate data to ensure efficient case resolution.
Whichdata quality dimension Is essential for this custom application?

Consistency

Duplication

Age

NO.56 Why is it critical to consider privacy concerns when dealing with AI and CRM data?

Ensures compliance with laws and regulations

Confirms the data is accessible to all users

Increases the volume of data collected

NO.57 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?

Autofill

Duplicate matching rule

Validation rule

NO.58 A sales manager wants to use AI to help sales representatives log their calls quicker and more accurately.
Which functionality provides the best solution?

Call Summaries

Sales Dialer

Auto-Generated Sales Tasks

NO.59 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

NO.60 What is a key challenge of human AI collaboration in decision-making?

Leads to move informed and balanced decision-making

Creates a reliance on AI, potentially leading to less critical thinking and oversight

Reduce the need for human involvement in decision-making processes

NO.61 What is a potential outcome of using poor-quality data in AI application?

AI model training becomes slower and less efficient

AI models may produce biased or erroneous results.

AI models become more interpretable

NO.62 What is the significance of explainability of trusted AI systems?

Increases the complexity of AI models

Enhances the security and accuracy of AI models

Describes how Al models make decisions

NO.63 What is a key characteristic of machine learning in the context of AI capabilities?

Uses algorithms to learn from data and make decisions

Relies on preprogrammed rules to make decisions

Can perfectly mimic human intelligence anddecision-making

NO.64 What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

Chances of bIas and mitigated

Chances of bias are aggravated

Chances of bias are remove

NO.65 A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?

Duplication

Usage

Completeness

NO.66 A sales manager wants to use AI to help sales representatives log their calls quicker and more accurately.
Which functionality provides the best solution?

Sales Dialer

Call Summaries

Auto-Generated Sales Tasks

NO.67 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

NO.68 What should organizations do to ensure data quality for their AI initiatives?

Collect and curate high-quality data from reliable sources.

Rely on AI algorithms to automatically handle data quality issues.

Prioritizemodel fine-tuning over data quality improvements.

NO.69 Why is it critical to consider privacy concernswhen dealing with AI and CRM data?

Ensures compliance with laws and regulations

Confirms the data is accessible to all users

Increases the volume of data collected

NO.70 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?

Multi-Select Picklist

Text

Rich Text Area

NO.71 A customer using Einstein Prediction Builder is confused about why a certain prediction was made.
Following Salesforce’s Trusted AI Principle of Transparency, which customer information should be accessible on the Salesforce Platform?

An explanation of how Prediction Builder works and a link to Salesforce’s Trusted AI Principles

An explanation of the prediction’s rationale and a model card that describes how the model was created

A marketing article of the product that clearly outlines the oroduct’s capabilities and features

NO.72 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.

NO.73 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.

NO.74 What is a benefit of a diverse, balanced, and large dataset?

Training time

Data privacy

Model accuracy

NO.75 What is an example of ethical debt?

Violating a data privacy law and falling to pay fines

Launching an AI feature after discovering a harmful bias

Delaying an AI product launch to retrain an AI data model

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

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