SALESFORCE-AI-ASSOCIATE PASS4SURE QUESTIONS & SALESFORCE-AI-ASSOCIATE ACTUAL TEST & SALESFORCE-AI-ASSOCIATE PRACTICE TRAINING

Salesforce-AI-Associate Pass4sure Questions & Salesforce-AI-Associate Actual Test & Salesforce-AI-Associate Practice Training

Salesforce-AI-Associate Pass4sure Questions & Salesforce-AI-Associate Actual Test & Salesforce-AI-Associate Practice Training

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

TopicDetails
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
  • 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 4
  • Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.

>> Latest Salesforce-AI-Associate Exam Topics <<

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Salesforce Certified AI Associate Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
Cloud Kicks wants to improve the quality of its AI model's predictions with the use of a large amount of data.
Which data quality element should the company focus on?

  • A. Accuracy
  • B. Volume
  • C. Location

Answer: A

Explanation:
To improve the quality of AI model predictions, Cloud Kicks should focus on the accuracy of the data.
Accurate data ensures that the insights and predictions generated by AI models are reliable and valid. Data accuracy involves correcting errors, filling missing values, and verifying data sources to enhance the quality of information fed into the AI systems. Focusing on data accuracy helps in minimizing prediction errors and enhances the decision-making process based on AI insights. For more details on the importance of data quality in AI models, Salesforce provides extensive guidance in their documentation, which can be found at Data Quality and AI.


NEW QUESTION # 62
What is the rile of data quality in achieving AI business Objectives?

  • A. Data quality is unnecessary because AI can work with all data types.
  • B. Data quality is required to create accurate AI data insights.
  • C. Data quality is important for maintain Ai data storage limits

Answer: B

Explanation:
Explanation
"Data quality is required to create accurate AI data insights. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Data quality can also affect the accuracy and validity of AI data insights, as they reflect the quality of the data used or generated by AI systems."


NEW QUESTION # 63
How does poor data quality affect predictive and generative AI models?

  • A. Creates inaccurate results
  • B. Increases raw data volume
  • C. Decreases storage efficiency

Answer: A

Explanation:
Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis.
Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.


NEW QUESTION # 64
What can bias in AI algorithms in CRM lead to?

  • A. Ethical challenges in CRM systems
  • B. Personalization and target marketing changes
  • C. Advertising cost increases

Answer: A

Explanation:
Explanation
"Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness and ethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers' identity or characteristics."


NEW QUESTION # 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?

  • A. Duplication
  • B. Completeness
  • C. Usage

Answer: B

Explanation:
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness.
Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights.
Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.


NEW QUESTION # 66
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