NCA-GENL Positive Feedback - NCA-GENL Reliable Test Questions
NCA-GENL Positive Feedback - NCA-GENL Reliable Test Questions
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NVIDIA Generative AI LLMs Sample Questions (Q14-Q19):
NEW QUESTION # 14
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. Accuracy on a validation set
- B. Training duration
- C. Number of layers
- D. Model size
Answer: A
Explanation:
When fine-tuning large language models (LLMs), the primary goal is to improve the model's performance on a specific task. The most common metric for assessing this performance is accuracy on a validation set, as it directly measures how well the model generalizes to unseen data. NVIDIA's NeMo framework documentation for fine-tuning LLMs emphasizes the use of validation metrics such as accuracy, F1 score, or task-specific metrics (e.g., BLEU for translation) to evaluate model performance during and after fine-tuning.
These metrics provide a quantitative measure of the model's effectiveness on the target task. Options A, C, and D (model size, training duration, and number of layers) are not performance metrics; they are either architectural characteristics or training parameters that do not directly reflect the model's effectiveness.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NEW QUESTION # 15
What is 'chunking' in Retrieval-Augmented Generation (RAG)?
- A. A technique used in RAG to split text into meaningful segments.
- B. A method used in RAG to generate random text.
- C. Rewrite blocks of text to fill a context window.
- D. A concept in RAG that refers to the training of large language models.
Answer: A
Explanation:
Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM.
According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."
NEW QUESTION # 16
In the context of data preprocessing for Large Language Models (LLMs), what does tokenization refer to?
- A. Splitting text into smaller units like words or subwords.
- B. Applying data augmentation techniques to generate more training data.
- C. Removing stop words from the text.
- D. Converting text into numerical representations.
Answer: A
Explanation:
Tokenization is the process of splitting text into smaller units, such as words, subwords, or characters, which serve as the basic units for processing by LLMs. NVIDIA's NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with popular tokenizers (e.g., WordPiece, BPE) breaking text into subword units to handle out-of-vocabulary words and improve model efficiency. For example, the sentence "I love AI" might be tokenized into ["I", "love", "AI"] or subword units like ["I",
"lov", "##e", "AI"]. Option B (numerical representations) refers to embedding, not tokenization. Option C (removing stop words) is a separate preprocessing step. Option D (data augmentation) is unrelated to tokenization.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
NEW QUESTION # 17
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?
- A. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
- B. ReLU is a linear function while sigmoid is non-linear.
- C. ReLU and sigmoid both have a range of 0 to 1.
- D. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
Answer: D
Explanation:
ReLU (Rectified Linear Unit) and sigmoid are activation functions used in neural networks. According to NVIDIA's deep learning documentation (e.g., cuDNN and TensorRT), ReLU, defined as f(x) = max(0, x), is computationally efficient because it involves simple thresholding, avoiding expensive exponential calculations required by sigmoid, f(x) = 1/(1 + e