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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Describe the ethical challenges of AI
  • Describe the elements
  • components of data quality
Topic 2
  • AI Capabilities in CRM
  • Ethical Considerations of AI
  • AI Fundamentals
Topic 3
  • Apply Salesforce's Trusted AI Principles to given scenarios
  • Describe the benefits of AI as they apply to CRM
Topic 4
  • Describe the importance of data quality
  • Explain the basic principles and applications of AI within Salesforce
Topic 5
  • Identify CRM AI capabilities
  • Differentiate between the types of AI and their capabilities

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Salesforce Certified AI Associate Exam (SU23) Sample Questions (Q51-Q56):

NEW QUESTION # 51
How does AI which CRM help sales representatives better understand previous customer interactions?

  • A. Provides call summaries
  • B. Creates, localizes, and translates product descriptions
  • C. Triggers personalized service replies

Answer: A

Explanation:
Explanation
"Providing call summaries is how AI with CRM helps sales representatives better understand previous customer interactions. Call summaries are a feature that uses natural language processing (NLP) to analyze voice conversations between sales representatives and customers and generate summaries or transcripts of the calls. Call summaries can help sales representatives better understand previous customer interactions by providing key information, insights, or action items from the calls."


NEW QUESTION # 52
Salesforce defines bias as using a person's Immutable traits to classify them or market to them.
Which potentially sensitive attribute is an example of an immutable trait?

  • A. Email address
  • B. Financial status
  • C. Nickname

Answer: B

Explanation:
Explanation
"Financial status is an example of an immutable trait. Immutable traits are characteristics that are inherent, fixed, or unchangeable. For example, financial status is an immutable trait because it is determined by factors beyond one's control, such as birth, inheritance, or economic conditions. Nickname and email address are not immutable traits because they can be changed by choice or preference."


NEW QUESTION # 53
What are some of the ethical challenges associated with AI development?

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

Answer: A

Explanation:
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 tounderstand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness."


NEW QUESTION # 54
What is a sensitive variable that car esc to bias?

  • A. Gender
  • B. Education level
  • C. Country

Answer: A

Explanation:
Explanation
"Gender is a sensitive variable that can lead to bias. A sensitive variable is a variable that can potentially cause discrimination or unfair treatment based on a person's identity or characteristics. For example, gender is a sensitive variable because it can affect how people are perceived, treated, or represented by AI systems."


NEW QUESTION # 55
What is a benefit of a diverse, balanced, and large dataset?

  • A. Model accuracy
  • B. Training time
  • C. Data privacy

Answer: A

Explanation:
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."


NEW QUESTION # 56
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