Domain adaptation via prompting: Practice Questions — Prompt Engineering (NVIDIA-Certified Professional: Generative AI LLMs)

Practice Questions: Domain Adaptation via Prompting

These multiple-choice questions focus on domain adaptation via prompting, a key skill for the NVIDIA-Certified Professional: Generative AI LLMs exam. Domain adaptation via prompting involves tailoring prompts to guide large language models (LLMs) to perform well on tasks within specific domains without retraining the model.

1. Which of the following best describes domain adaptation via prompting?

   • A) Fine-tuning the entire model on domain-specific data
   • B) Modifying the prompt to include domain-specific context to influence model output
   • C) Changing the model architecture to suit a domain
   • D) Using a smaller model trained only on domain data

   Correct answer: B

   Explanation: Domain adaptation via prompting involves crafting prompts that provide domain-specific context or instructions to guide the model's responses without changing the model weights.

2. When adapting a prompt for a medical domain task, which strategy is most effective?

   • A) Using generic language and avoiding technical terms
   • B) Including detailed domain-specific terminology and examples in the prompt
   • C) Shortening the prompt to a single word
   • D) Using unrelated domain examples to test model flexibility

   Correct answer: B

   Explanation: Including domain-specific terminology and examples helps the model understand the context and produce more accurate, relevant outputs for that domain.

3. How does few-shot prompting assist in domain adaptation?

   • A) By retraining the model on a few domain examples
   • B) By providing a few examples in the prompt to demonstrate the desired task in the target domain
   • C) By limiting the model’s vocabulary to domain-specific words
   • D) By increasing the model size temporarily

   Correct answer: B

   Explanation: Few-shot prompting includes a small number of task examples within the prompt to guide the model’s understanding and output style in the target domain.

4. Which prompt modification can help control the style and tone when adapting to a legal domain?

   • A) Adding instructions to respond formally and cite legal precedents
   • B) Using casual language and slang
   • C) Removing all context from the prompt
   • D) Asking the model to generate poetry

   Correct answer: A

   Explanation: Explicit instructions within the prompt help the model generate domain-appropriate style and tone, such as formal language and citations in legal contexts.

5. What is a key limitation of domain adaptation via prompting compared to fine-tuning?

   • A) It requires access to model weights
   • B) It cannot incorporate domain knowledge
   • C) It may not achieve as high accuracy as fine-tuning on domain-specific tasks
   • D) It always requires large amounts of labeled data

   Correct answer: C

   Explanation: While prompting is efficient and flexible, it may not reach the same performance level as fine-tuning when extensive domain-specific accuracy is required.

6. In zero-shot domain adaptation, how is the prompt typically designed?

   • A) Including multiple examples from the domain
   • B) Providing detailed instructions without examples
   • C) Retraining the model on domain data
   • D) Using random unrelated prompts

   Correct answer: B

   Explanation: Zero-shot prompting relies on clear instructions within the prompt to guide the model without providing example demonstrations.

7. Which of the following is an effective way to evaluate domain adaptation via prompting?

   • A) Measuring model performance on general language tasks only
   • B) Testing model output quality on domain-specific benchmarks or datasets
   • C) Ignoring domain-specific errors
   • D) Only checking prompt length

   Correct answer: B

   Explanation: Evaluating on domain-specific benchmarks ensures the prompt adaptation effectively improves model performance in the target domain.

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