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Salesforce Salesforce-AI-Associate Exam (page: 1)
Salesforce Certified AI Associate
Updated on: 25-Dec-2025

Viewing Page 1 of 22
Salesforce-AI-Associate PDF 106 Questions

What is a benefit of a diverse, balanced, and large dataset?

  1. Training time
  2. Data privacy
  3. Model accuracy

Answer(s): C

Explanation:

"Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting or underfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data."



What are the three commonly used examples of AI in CRM?

  1. Predictive scoring, reporting, Image classification
  2. Predictive scoring, forecasting, recommendations
  3. Einstein Bots, face recognition, recommendations

Answer(s): B

Explanation:

"Predictive scoring, forecasting, and recommendations are three commonly used examples of AI in CRM. Predictive scoring can help prioritize leads, opportunities, and customers based on their likelihood to convert, churn, or buy. Forecasting can help predict future sales, revenue, or demand based on historical data and trends. Recommendations can help suggest the best products, services, or actions for each customer based on their preferences, behavior, and needs."



Cloud Kicks wants to optimize its business operations by incorporating AI into its CRM.
What should the company do first to prepare its data for use with AI?

  1. Remove biased data.
  2. Determine data availability.
  3. Determine data outcomes.

Answer(s): B

Explanation:

Before using AI to optimize business operations, the company should first assess the availability and quality of its data. Data is the fuel for AI, and without sufficient and relevant data, AI cannot produce accurate and reliable results. Therefore, the company should identify what data it has, where it is stored, how it is accessed, and how it is maintained. This will help the company understand the feasibility and scope of its AI projects.



A healthcare company implements an algorithm to analyze patient data and assist in medical diagnosis.
Which primary role does data Quality play In this AI application?

  1. Enhanced accuracy and reliability of medical predictions and diagnoses
  2. Ensured compatibility of AI algorithms with the system's Infrastructure
  3. Reduced need for healthcare expertise in interpreting AI outouts

Answer(s): A

Explanation:

"Data quality plays a crucial role in enhancing the accuracy and reliability of medical predictions and diagnoses. Poor data quality can lead to inaccurate or misleading results, which can have serious consequences for patients' health and well-being. Therefore, it is important to ensure that the data used for AI applications in healthcare is accurate, complete, consistent, and relevant."



What are some of the ethical challenges associated with AI development?

  1. Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
  2. Implicit transparency of AI systems, which makes It easy for users to understand and trust their decisions
  3. Inherent neutrality of AI systems, which eliminates any potential for human bias in decision- making

Answer(s): A

Explanation:

"Some of the ethical challenges associated with AI development are the potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes. Human bias can arise from the data used to train the models, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult to understand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness."



Viewing Page 1 of 22



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