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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

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

Topic 2

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 3

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 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 Certified AI Associate Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?

A. Size of the dataset
B. Number of variables ipn the dataset
C. Age of the dataset

Answer: A

Explanation:
Explanation
"The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data."

NEW QUESTION # 58
Cloud Kicks prepares a dataset for an AI model and identifies some inconsistencies in the data.
What is the most appropriate action the company should take?

A. Increase the quantity of data being used for training the model
B. Investigate the data inconsistencies and apply data quality techniques.
C. Adjust the Al model to account for the data inconsistencies.

Answer: B

Explanation:
When inconsistencies in data are identified, the most appropriate action is to investigate these inconsistencies and apply data quality techniques. Adjusting the AI model to accommodate poor quality data or simply increasing the quantity of data without addressing the underlying issues does not solve the problem and can lead to less reliable AI outputs. Proper data cleaning, normalization, and validation are necessary steps to ensure that the data fed into an AI model is accurate and reliable, thus enhancing the model's performance.
Salesforce provides guidelines on how to manage and improve data quality, including practical steps for addressing data inconsistencies, detailed at Improving Data Quality in Salesforce.

NEW QUESTION # 59
Cloud kicks wants to decrease the workload for its customer care agents by implementing a chatbot on its website that partially deflects incoming cases by answering frequency asked questions Which field of AI is most suitable for this scenario?

A. Predictive analytics
B. Computer vision
C. Natural language processing

Answer: C

Explanation:
"Natural language processing is the field of AI that is most suitable for this scenario. Natural language processing (NLP) is a branch of AI that enables computers to understand and generate natural language, such as speech or text. NLP can be used to create conversational interfaces that can interact with users using natural language, such as chatbots. Chatbots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the user's intent and context."

NEW QUESTION # 60
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce's Trusted AI Principle of Transparency?

A. Communicate how risk factors such as credit score can impact customer eligibility.
B. Incorporate customer feedback into the model's continuous training.
C. Flag sensitive variables and their proxies to prevent discriminatory lending practices.

Answer: C

Explanation:
Explanation
"Flagging sensitive variables and their proxies to prevent discriminatory lending practices is how they should implement Salesforce's Trusted AI Principle of Transparency. Transparency is one of the Trusted AI Principles that states 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. Flagging sensitive variables and their proxies means identifying and marking variables that can potentially cause discrimination or unfair treatment based on a person's identity or characteristics, such as age, gender, race, income, or credit score. Flagging sensitive variables and their proxies can help implement Transparency by allowing users to understand and evaluate the data used or generated by AI systems."

NEW QUESTION # 61
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 # 62
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