NVIDIA NCA-GENL 問題集

質問 1：
In Natural Language Processing, there are a group of steps in problem formulation collectively known as word representations (also word embeddings). Which of the following are Deep Learning models that can be used to produce these representations for NLP tasks? (Choose two.)
A. WordNet
B. BERT
C. TensorRT
D. Word2vec
E. Kubernetes
正解：B,D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
Which metric is commonly used to evaluate machine-translation models?
A. ROUGE score
B. BLEU score
C. F1 Score
D. Perplexity
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
Which of the following options describes best the NeMo Guardrails platform?
A. Ensuring the ethical use of artificial intelligence systems by monitoring and enforcing compliance with predefined rules and regulations.
B. Ensuring scalability and performance of large language models in pre-training and inference.
C. Developing and designing advanced machine learning models capable of interpreting and integrating various forms of data.
D. Building advanced data factories for generative AI services in the context of language models.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?
A. Training duration
B. Accuracy on a validation set
C. Number of layers
D. Model size
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?
A. BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.
B. BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.
C. BLEU scores evaluate the 'precision' of translations, while ROUGE scores focus on the 'recall' of summarized text.
D. BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Which of the following is a key characteristic of Rapid Application Development (RAD)?
A. Extensive upfront planning before any development.
B. Linear progression through predefined project phases.
C. Iterative prototyping with active user involvement.
D. Minimal user feedback during the development process.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 7：
You are in need of customizing your LLM via prompt engineering, prompt learning, or parameter-efficient fine-tuning. Which framework helps you with all of these?
A. NVIDIA TensorRT
B. NVIDIA DALI
C. NVIDIA Triton
D. NVIDIA NeMo
正解：D
解説: (Topexam メンバーにのみ表示されます)