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Salesforce AI Associate Exam Questions - Navigate Your Path to Success

The Salesforce Certified AI Associate (Salesforce AI Associate) exam is a good choice for Salesforce AI Application Developers and if the candidate manages to pass Salesforce Certified AI Associate exam, he/she will earn Salesforce AI Associate, Salesforce Associate Certifications. Below are some essential facts for Salesforce Salesforce AI Associate exam candidates:

  • In actual Salesforce Certified AI Associate (Salesforce AI Associate) exam, a candidate can expect 40 Questions and the officially allowed time is expected to be around 70 Minutes.
  • TrendyCerts offers 105 Questions that are based on actual Salesforce AI Associate syllabus.
  • Our Salesforce AI Associate Exam Practice Questions were last updated on: Mar 11, 2025

Sample Questions for Salesforce AI Associate Exam Preparation

Question 1

What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?

Correct : C

Predictive analytics, machine learning, natural language processing (NLP), and computer vision are all types of artificial intelligence technologies that can be applied in Salesforce to enhance various aspects of business operations and customer interactions. Predictive analytics uses historical data to make predictions about future events. Machine learning involves algorithms that can learn from and make decisions based on data. NLP is concerned with the interactions between computers and humans using natural language, and computer vision interprets and processes visual information from the world to make sense of it in the way humans do. Salesforce harnesses these AI technologies, particularly through its Einstein platform, to provide powerful tools that help businesses automate tasks, make better decisions, and offer more personalized services. For more on how Salesforce utilizes these AI technologies, you can explore the Einstein AI services documentation at Salesforce Einstein.


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Question 2

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

Correct : A

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.


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