Free NVIDIA NCA-GENL Exam Questions
Absolute Free NCA-GENL Exam Practice for Comprehensive Preparation
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NVIDIA NCA-GENL Exam Questions
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Provided By: NVIDIA
- Exam: NCA - Generative AI LLMs
- Certification: NVIDIA Certified Associate
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Total Questions: 395
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Updated On: Sep 24, 2026
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Rated: 4.9 |
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Online Users: 790
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Question 1
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Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?
Answer: D
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Question 2
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Which technology will allow you to deploy an LLM for production application?
Answer: D
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Which technology will allow you to deploy an LLM for production application?
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Question 3
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In the transformer architecture, what is the purpose of positional encoding?
Answer: C
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In the transformer architecture, what is the purpose of positional encoding?
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Question 4
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You are working on a project that involves developing a named entity recognition (NER) system using spaCy. The system must accurately identify and categorize entities in a large corpus of unstructured text data. Additionally, the data processing pipeline must be optimized for performance and scalability. Which two actions would best enhance the performance and accuracy of the NER system in this scenario? (Select two)
Answer: A,B
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You are working on a project that involves developing a named entity recognition (NER) system using spaCy. The system must accurately identify and categorize entities in a large corpus of unstructured text data. Additionally, the data processing pipeline must be optimized for performance and scalability. Which two actions would best enhance the performance and accuracy of the NER system in this scenario? (Select two)
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Question 5
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You are tasked with deploying an LLM that generates real-time investment advice for clients. During testing, you notice that the model tends to provide overly optimistic predictions in volatile market conditions. The senior team member suggests incorporating a mechanism to ensure the model’s predictions account for market uncertainty. What is the most effective method to improve the model's handling of volatile market conditions?
Answer: D
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You are tasked with deploying an LLM that generates real-time investment advice for clients. During testing, you notice that the model tends to provide overly optimistic predictions in volatile market conditions. The senior team member suggests incorporating a mechanism to ensure the model’s predictions account for market uncertainty. What is the most effective method to improve the model's handling of volatile market conditions?