VALID SALESFORCE-AI-ASSOCIATE VALID TEST DISCOUNT HELP YOU CLEAR YOUR SALESFORCE-AI-ASSOCIATE: SALESFORCE CERTIFIED AI ASSOCIATE EXAM EXAM SURELY

Valid Salesforce-AI-Associate Valid Test Discount Help You Clear Your Salesforce-AI-Associate: Salesforce Certified AI Associate Exam Exam Surely

Valid Salesforce-AI-Associate Valid Test Discount Help You Clear Your Salesforce-AI-Associate: Salesforce Certified AI Associate Exam Exam Surely

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This format is for candidates who do not have the time or energy to use a computer or laptop for preparation. Salesforce Salesforce-AI-Associate PDF file includes real Salesforce Salesforce-AI-Associate questions, and they can be easily printed and studied at any time. TestInsides regularly updates its PDF file to ensure that its readers have access to the updated questions.

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 the 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.

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Salesforce-AI-Associate Valid Exam Objectives - Salesforce-AI-Associate Exam Preview

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Salesforce Certified AI Associate Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which best describes the different between predictive AI and generative AI?

  • A. Predictive new and original output for a given input.
  • B. Predictive AI and generative have the same capabilities differ in the type of input they receive:
    predictive AI receives raw data whereas generation AI receives natural language.
  • C. 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

Answer: A

Explanation:
"The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques to generate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos."


NEW QUESTION # 27
How does data quality impact the trustworthiness of Al-driven decisions?

  • A. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
  • B. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
  • C. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.

Answer: B

Explanation:
Explanation
"High-quality data improves the reliability and credibility of AI-driven decisions, fostering trust among users.
High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task.
High-quality data can improve the performance and reliability of AI systems, as they have enough and correct information to learn from and make accurate predictions. High-quality data can also improve the trustworthiness of AI-driven decisions, as users can have more confidence and satisfaction in using AI systems."


NEW QUESTION # 28
What is a benefit of a diverse, balanced, and large dataset?

  • A. Training time
  • B. Data privacy
  • C. Model accuracy

Answer: C

Explanation:
"Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting orunderfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data."


NEW QUESTION # 29
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

  • A. Chances of bIas and mitigated
  • B. Chances of bias are remove
  • C. Chances of bias are aggravated

Answer: A

Explanation:
Explanation
"Data quality and transparency can help mitigate the chances of bias in generative AI. Data quality means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can help mitigate bias by ensuring that the generative AI model learns from a balanced and representative sample of the target population or domain. Data transparency means that the data sources, methods, and processes are clear and open to inspection and verification. Data transparency can help mitigate bias by allowing users to understand and evaluate the data used or generated by the generative AI model."


NEW QUESTION # 30
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?

  • A. Consistency
  • B. Usage
  • C. Accuracy

Answer: A

Explanation:
Explanation
"Consistency is the data quality dimension that is affected by multiple variations of state and country values in contact records. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Inconsistent data can cause confusion, errors, or duplication in data analysis and processing."


NEW QUESTION # 31
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