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Salesforce Salesforce-AI-Associate Exam Questions

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Salesforce Certified AI Associate Exam (SP25)

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Salesforce Salesforce-AI-Associate Sample Questions – Free Practice Test & Real Exam Prep

Question #1

A system admin recognizes the need to put a data management strategy in place.What is a key component of data management strategy?

  • A. Naming Convention
  • B. Data Backup
  • C. Color Coding
Answer: B
Explanation
Data Backup is a key component of a data management strategy. A data backup is a process of creating and storing copies of data in a separate location or device to prevent data loss or damagein case of a disaster, accident, or malicious attack. A data backup can help ensure data availability, reliability, and security by allowing data to be restored or recovered in the event of a data breach, corruption, or deletion. A data management strategy should include a data backup plan that defines the frequency, scope, method, and location of data backups, as well as the roles and responsibilities of the data backup team.
Question #2

Which best describes the different between predictive AI and generative AI?

  • A. Predictive new and original output for a given input.
  • B. 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.
  • C. 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
Answer: A
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 togenerate 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.”
Question #3

A service leader wants use AI to help customer resolve their issues quicker in a guided self-serve application. Which Einstein functionality provides the best solution?

  • A. Case Classification
  • B. Bots
  • C. Recommendation
Answer: B
Explanation
“Bots provide the best solution for a service leader who wants to use AI to help customers resolve their issues quicker in a guided self-serve application. Bots are a feature that uses natural language processing (NLP) and natural language understanding (NLU) to create conversational interfaces that can interact with customers using text or voice. Bots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the customer’s intent and context.”
Question #4

An administrator at Cloud Kicks wants to ensure that a field is set up on the customer record so their preferred name can be captured.Which Salesforce field type should the administrator use to accomplish this?

  • A. Multi-Select Picklist
  • B. Text
  • C. Rich Text Area
Answer: B
Explanation
“A text field type should be used to capture the customer’s preferred name. A text field type allows the user to enter any combination of letters, numbers, or symbols. A text field type can be used to store names, addresses, phone numbers, or other personal information.”
Question #5

What is an example of ethical debt?

  • A. Violating a data privacy law and falling to pay fines
  • B. Launching an AI feature after discovering a harmful bias
  • C. Delaying an AI product launch to retrain an AI data model
Answer: B
Explanation
“Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is a term that describes the potential harm or risk caused by unethical or irresponsible decisions or actions related to AI systems. Ethical debt can accumulate over time and have negative consequences for users, customers, partners, or society. For example, launching an AI feature after discovering a harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect their trust, satisfaction, or well-being.”
Question #6

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

  • A. Predictive scoring, reporting, Image classification
  • B. Predictive scoring, forecasting, recommendations
  • C. Einstein Bots, face recognition, recommendations
Answer: 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.”
Question #7

What is the role of Salesforce Trust AI principles in the context of CRM system?

  • A. Guiding ethical and responsible use of AI
  • B. Providing a framework for AI data model accuracy
  • C. Outlining the technical specifications for AI integration
Answer: A
Explanation
“The role of Salesforce Trust AI principles in the context of CRM systems is guiding ethical and responsible use of AI. Salesforce Trust AI principles are a set of guidelines and best practices for developing and using AI systems in a responsible and ethical way. The principles include Accountability, Fairness & Equality, Transparency & Explainability, Privacy & Security, Reliability & Safety, Inclusivity & Diversity, Empowerment & Education. The principles aim to ensure that AI systems are aligned with the values and interests of customers, partners, and society.”
Question #8

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
Answer: A
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 #9

What is the rile of data quality in achieving AI business Objectives?

  • A. Data quality is unnecessary because AI can work with all data types.
  • B. Data quality is required to create accurate AI data insights.
  • C. Data quality is important for maintain Ai data storage limits
Answer: B
Explanation
“Data quality is required to create accurate AI data insights. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Data quality can also affect the accuracy and validity of AI data insights, as they reflect the quality of the data used or generated by AI systems.”
Question #10

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?

  • A. Enhanced accuracy and reliability of medical predictions and diagnoses
  • B. Ensured compatibility of AI algorithms with the system's Infrastructure
  • C. Reduced need for healthcare expertise in interpreting AI outouts
Answer: 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.”
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