Verified Salesforce-AI-Associate Exam Dumps PDF [2024] Access using BraindumpStudy [Q61-Q81]

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Verified Salesforce-AI-Associate Exam Dumps PDF [2024] Access using BraindumpStudy

Try Best Salesforce-AI-Associate Exam Questions from Training Expert BraindumpStudy

Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Topic 3	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Topic 4	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.

Q61. How is natural language processing (NLP) used in the context of AI capabilities?

To cleanse and prepare data for AI implementations

To interpret and understand programminglanguage

To understand and generate human language

Q62. Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results?
What to a potential mason for this?

Poor data quality

The wrongproduct

Too much data

Q63. 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

Q64. 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

Q65. 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

Q66. What is an example of Salesforce’s Trusted AI Principle of Inclusivity in practice?

Testing models with diverse datasets

Striving for model explain ability

Working with human rights experts

Q67. What are the key components of the data quality standard?

Reviewing, Updating, Archiving

Naming, formatting, Monitoring

Accuracy, Completeness, Consistency

Q68. 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.

Q69. Cloud kicks wants to develop a solution to predict customers’ interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimensionof data quality is affected in this scenario?

Accuracy

Consistency

Completeness

Q70. Cloud Kicks’ latest email campaign is struggling to attract new customers.
How can AI increase the company’s customer email engagement?

Create personalized emails

Resend emails to inactive recipients

Remove invalid email addresses

Q71. 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

Q72. Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.
Which type of records negatively impact data quality?

Structured

Complete

Duplicate

Q73. Which Einstein capability uses emails to create content for Knowledge articles?

Generate

Discover

Predict

Q74. 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

Q75. Cloud Kicks wants to develop a solution to predict customers product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.
Which data quality dimension is affected in this scenario?

Completeness

Accuracy

Consistency

Q76. Cloud Kicks wants to evaluate the quality of its sales data.
Which first step should they take for the data quality assessment?

Plan and align territories,

Run a new report or dashboard.

Identify business objectives.

Q77. Cloud kicks wants to decrease the workload for its customer care agents by implementing a chatbot on its website that partially deflects incoming cases by answering frequency asked questions Which field of AI is most suitable for this scenario?

Natural language processing

Computer vision

Predictive analytics

Q78. 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

Q79. What are the three commonly used examples of AI in CRM?

Predictive scoring, reporting, Image classification

Predictive scoring, forecasting, recommendations

Einstein Bots, face recognition, recommendations

Q80. 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

Q81. 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?

Remove biased data.

Determine data availability.

Determine data outcomes.