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Salesforce-AI-Associate Test Simulates: Salesforce Certified AI Associate Exam & Salesforce-AI-Associate Study Guide
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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 2
- 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 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
- AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
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Updated Salesforce Salesforce-AI-Associate Practice Questions in PDF Format
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Salesforce Certified AI Associate Exam Sample Questions (Q86-Q91):
NEW QUESTION # 86
A business analyst (BA) wants to improve business by enhancing their sales processes and customer..
Which AI application should the BA use to meet their needs?
- A. Sales data cleansing and customer support data governance
- B. Lead scoring, opportunity forecasting, and case classification
- C. Machine learning models and chatbot predictions
Answer: B
Explanation:
"Lead scoring, opportunity forecasting, and case classification are AI applications that can help abusiness analyst improve their sales processes and customer support. Lead scoring can help prioritize leads based on their likelihood to convert, opportunity forecasting can help predict future sales or revenue based on historical data and trends, and caseclassification can help categorize and route cases based on their attributes."
NEW QUESTION # 87
Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results?
What to a potential mason for this?
- A. Too much data
- B. The wrongproduct
- C. Poor data quality
Answer: C
Explanation:
"Poor data quality is a potential reason for not seeing accurate results from an AI model. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor dataquality can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions."
NEW QUESTION # 88
What are some key benefits of AI in improving customer experiences in CRM?
- A. Improves CRM security protocols, safeguarding sensitive customer data from potential breaches and threats
- B. Fully automates the customer service experience, ensuring seamless automated interactions with customers
- C. Streamlines case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions
Answer: B
Explanation:
"Streamlining case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions are some key benefits of AI in improving customer experiences in CRM. AI can help automate and optimize various aspects of customer service, such as routing cases to the right agents, providing relevant information or suggestions, and generating reports or insights. AI can also help enhance customer satisfaction and loyalty by reducing wait times, improving response quality, and providing personalized solutions."
NEW QUESTION # 89
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. Adjust the Al model to account for the data inconsistencies.
- B. Increase the quantity of data being used for training the model
- C. Investigate the data inconsistencies and apply data quality techniques.
Answer: C
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 # 90
Cloud kicks wants to develop a solution to predict customers' interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimensionof data quality is affected in this scenario?
- A. Completeness
- B. Consistency
- C. Accuracy
Answer: B
Explanation:
"Consistency is the dimension of data quality that is affected in this scenario. 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. For example, using different field types for the same attribute can affect the consistency of the data."
NEW QUESTION # 91
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