THREE DIFFERENT FORMATS OF REAL4DUMPS SALESFORCE SALESFORCE-AI-ASSOCIATE EXAM DUMPS

Three Different Formats of Real4dumps Salesforce Salesforce-AI-Associate Exam Dumps

Three Different Formats of Real4dumps Salesforce Salesforce-AI-Associate Exam Dumps

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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
  • 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 the Trusted AI Principles of Salesforce to given scenarios.

>> Detailed Salesforce-AI-Associate Study Plan <<

Salesforce-AI-Associate Reliable Test Prep | Salesforce-AI-Associate Latest Exam Pattern

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Salesforce Certified AI Associate Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 # 26
Which action introduces bias in the training data used for AI algorithms?

  • A. Using a dataset that represents diverse perspectives and populations
  • B. Using a dataset that underrepresents perspectives and populations
  • C. Using a large dataset that is computationally expensive

Answer: B

Explanation:
Introducing bias in training data for AI algorithms occurs when the dataset used underrepresents certain perspectives and populations. This type of bias can skew AI predictions, making the system less fair and accurate. For example, if a dataset predominantly contains information from one demographic group, the AI's performance may not generalize well to other groups, leading to biased or unfair outcomes. Salesforce discusses the impact of biased training data and ways to mitigate this in their AI ethics guidelines, which can be explored further in the Salesforce AI documentation on Responsible Creation of AI.


NEW QUESTION # 27
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?

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

Answer: C

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 # 28
How does data quality impact the trustworthiness of Al-driven decisions?

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

Answer: A


NEW QUESTION # 29
What are the key components of the data quality standard?

  • A. Accuracy, Completeness, Consistency
  • B. Reviewing, Updating, Archiving
  • C. Naming, formatting, Monitoring

Answer: A

Explanation:
Explanation
"Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources."


NEW QUESTION # 30
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