HOT NEW SALESFORCE-AI-ASSOCIATE EXAM VCE 100% PASS | HIGH-QUALITY EXAM SALESFORCE CERTIFIED AI ASSOCIATE EXAM SYLLABUS PASS FOR SURE

HOT New Salesforce-AI-Associate Exam Vce 100% Pass | High-quality Exam Salesforce Certified AI Associate Exam Syllabus Pass for sure

HOT New Salesforce-AI-Associate Exam Vce 100% Pass | High-quality Exam Salesforce Certified AI Associate Exam Syllabus Pass for sure

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

TopicDetails
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 the 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
  • 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
  • AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.

Salesforce Certified AI Associate Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
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?

  • A. A marketing article of the product that clearly outlines the oroduct's capabilities and features
  • B. An explanation of how Prediction Builder works and a link to Salesforce's Trusted AI Principles
  • C. An explanation of the prediction's rationale and a model card that describes how the model was created

Answer: C

Explanation:
"An explanation of the prediction's rationale and a model card that describes how the model was created should be accessible on the Salesforce Platform following Salesforce's Trusted AI Principle of Transparency.
Transparency means that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with."


NEW QUESTION # 88
Cloud Kicks plans to use automated chat as its primary support channel.
Which Einstein feature should they use?

  • A. Discovery
  • B. Bots
  • C. Next Best Action

Answer: B

Explanation:
For Cloud Kicks, using automated chat as the primary support channel, the recommended Einstein feature is Bots. Einstein Bots are designed to automate customer interactions on common issues through chat and messaging platforms. They can handle routine requests, provide quick answers to frequently asked questions, and escalate more complex issues to human agents. Using Einstein Bots helps improve customer service efficiency and speed, leading to enhanced customer satisfaction. To learn more about setting up and optimizing Einstein Bots for a business, you can visit the Salesforce documentation on Einstein Bots at Salesforce Einstein Bots.


NEW QUESTION # 89
Cloud Kicks uses Einstein to generate predictions but is not seeing accurate results. What is a potential reason for this?

  • A. Too much data
  • B. Poor data quality
  • C. The wrong product

Answer: B

Explanation:
AI models rely on high-quality data to produce accurate and reliable predictions. Poor data quality-such as missing values, inconsistent formatting, or biased data-can negatively impact AI performance.
Option A (Incorrect): If Cloud Kicks is using Einstein AI, it is unlikely that they are using the wrong product, as Einstein is designed for predictive analytics. The issue is more likely related to data quality or model training.
Option B (Correct): Poor data quality is one of the most common reasons for inaccurate AI predictions. If the input data contains errors, biases, or incomplete information, the AI model will generate flawed insights.
Regular data cleaning and preprocessing are essential for improving prediction accuracy.
Option C (Incorrect): Having too much data does not necessarily result in inaccurate predictions. In fact, more data can improve model performance if properly structured and cleaned. However, if the data is noisy or unstructured, it may lead to inconsistencies.


NEW QUESTION # 90
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>

  • A. Inclusivity
  • B. Transparency
  • C. Accountability

Answer: A

Explanation:
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 # 91
What is an example of ethical debt?

  • A. Launching an AI feature after discovering a harmful bias
  • B. Delaying an AI product launch to retrain an AI data model
  • C. Violating a data privacy law and failing to pay fines

Answer: A

Explanation:
Ethical debt refers to the long-term negative consequences of prioritizing speed or convenience over responsible AI development practices. Ethical debt accumulates when AI systems are deployed despite known ethical concerns, such as bias, privacy violations, or transparency issues.
Option A (Correct): Launching an AI feature after discovering harmful bias is a clear example of ethical debt because it disregards the ethical obligation to ensure fairness and non-discrimination in AI outcomes. Ignoring bias can lead to systemic issues that are difficult and costly to correct later.
Option B (Incorrect): Violating a data privacy law and failing to pay fines is a legal issue rather than an example of ethical debt. While related, ethical debt pertains more to AI decision-making and development choices.
Option C (Incorrect): Delaying an AI product launch to retrain an AI model is a responsible action that helps avoid ethical debt, rather than an example of it. This demonstrates an effort to mitigate bias and improve AI fairness before deployment.


NEW QUESTION # 92
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