Salesforce Certified AI Associate Exam latest practice pdf & Salesforce-AI-Associate free study torrent

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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 Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
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 (Q98-Q103):

NEW QUESTION # 98
Cloud Kicks wants to ensure that multiple records for the same customer are removed in Salesforce.
Which feature should be used to accomplish this?

A. Duplicate management
B. Trigger deletion of old records
C. Standardized field names

Answer: A

Explanation:
"Duplicate management should be used to remove multiple records for the same customer in Salesforce.
Duplicate managementis a feature that helps prevent and manage duplicate records in Salesforce. Duplicate management can help define matching rules, duplicate rules, and alert messages to detect and merge duplicate records."

NEW QUESTION # 99
Which best describes the different between predictive AI and generative AI?

A. 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.
B. 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
C. Predictive new and original output for a given input.

Answer: C

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 # 100
How does poor data quality affect predictive and generative AI models?

A. Decreases storage efficiency
B. Increases raw data volume
C. Creates inaccurate results

Answer: C

Explanation:
Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis.
Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.

NEW QUESTION # 101
What are the potential consequences of an organization suffering from poor data quality?

A. Technical debt, monolithic system architecture, and slow ETL throughput
B. Low employee morale, stock devaluation, and inability to attract top talent
C. Revenue loss, poor customer service, and reputational damage

Answer: C

Explanation:
The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision-making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation. Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.

NEW QUESTION # 102
What are the key components of the data quality standard?

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

Answer: B

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 andvalid 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 # 103
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Salesforce Certified AI Associate Exam latest practice pdf & Salesforce-AI-Associate free study torrent

Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

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Salesforce Certified AI Associate Exam Sample Questions (Q98-Q103):

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