Salesforce AI Associate

Practice Salesforce AI Associate Exam

Is it difficult for you to decide to purchase Salesforce Salesforce AI Associate exam dumps questions? CertQueen provides FREE online Salesforce Certified AI Associate Exam (SU23) Salesforce AI Associate exam questions below, and you can test your Salesforce AI Associate skills first, and then decide whether to buy the full version or not. We promise you get the following advantages after purchasing our Salesforce AI Associate exam dumps questions.
1.Free update in ONE year from the date of your purchase.
2.Full payment fee refund if you fail Salesforce AI Associate exam with the dumps

 

 Full Salesforce AI Associate Exam Dump Here

Latest Salesforce AI Associate Exam Dumps Questions

The dumps for Salesforce AI Associate exam was last updated on Apr 05,2025 .

Viewing page 1 out of 3 pages.

Viewing questions 1 out of 18 questions

Question#1

What is the significance of explainability of trusted AI systems?

A. Increases the complexity of AI models
B. Enhances the security and accuracy of AI models
C. Describes how Al models make decisions

Explanation:
The significance of the explainability of trusted AI systems is that it describes how AI models make decisions. Explainability is crucial for building trust and accountability in AI systems, ensuring that users and stakeholders understand the decision-making processes and outcomes generated by AI.
This is particularly important in scenarios where AI decisions impact personal or financial status, such as in credit scoring or healthcare diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI practices, aiming to make AI systems more transparent and understandable.
More details about Salesforce’s approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.

Question#2

What does the term "data completeness" refer to in the context of data quality?

A. The degree to which all required data points are present in the dataset
B. The process of aggregating multiple datasets from various databases
C. The ability to access data from multiple sources in real time

Explanation:
Data completeness is a measure of data quality that assesses whether all required data points are present in a dataset. It checks for missing values or gaps in data necessary for accurate analysis and decision-making. In the context of Salesforce, ensuring data completeness is crucial for the effectiveness of CRM operations, reporting, and AI-driven applications like Salesforce Einstein, which rely on complete data to function optimally. Salesforce provides various tools and features, such as data validation rules and batch data import processes, that help maintain data completeness across its platform. Detailed guidance on managing data quality in Salesforce can be found in the Salesforce Help documentation on data management at Salesforce Help Data Management.

Question#3

What is a key characteristic of machine learning in the context of AI capabilities?

A. Uses algorithms to learn from data and make decisions
B. Relies on preprogrammed rules to make decisions
C. Can perfectly mimic human intelligence and decision-making

Explanation:
“Machine learning is a key characteristic of AI capabilities that uses algorithms to learn from data and make decisions. Machine learning is a branch of AI that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can analyze data, identify patterns, and make predictions or recommendations based on the data.”

Question#4

A data quality expert at Cloud Kicks want to ensure that each new contact contains at least an email address …
Which feature should they use to accomplish this?

A. Autofill
B. Duplicate matching rule
C. Validation rule

Explanation:
“A validation rule should be used to ensure that each new contact contains at least an email address or phone number. A validation rule is a feature that checks the data entered by users for errors before saving it to Salesforce. A validation rule can help ensure data quality by enforcing certain criteria or conditions for the data values.”

Question#5

What role does data quality play in the ethical us of AI applications?

A. High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi…
B. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
C. Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.

Explanation:
“High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data.”

Exam Code: Salesforce AI Associate         Q & A: 88 Q&As         Updated:  Apr 05,2025

 

 Full Salesforce AI Associate Exam Dumps Here