QUESTION: 5

What are some of the ethical challenges associated with AI development?

Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
Implicit transparency of AI systems, which makes It easy for users to understand and trust their decisions
Inherent neutrality of AI systems, which eliminates any potential for human bias in decision- making

Answer(s): A

Explanation:

"Some of the ethical challenges associated with AI development are the potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes. Human bias can arise from the data used to train the models, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult to understand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness."

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QUESTION: 6

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

Usage
Accuracy
Consistency

Answer(s): C

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."

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QUESTION: 7

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 programming language
To understand and generate human language

Answer(s): C

Explanation:

"Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts."

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QUESTION: 8

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

Answer(s): A

Explanation:

"An example of Salesforce's Trusted AI Principle of Inclusivity in practice is testing models with diverse datasets. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Testing models with diverse datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain."

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Free Salesforce-AI-Associate Exam Braindumps (page: 2)

QUESTION: 5

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QUESTION: 6

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QUESTION: 7

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QUESTION: 8

Explanation: