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NVIDIA Generative AI LLMs 認定 NCA-GENL 試験問題 (Q41-Q46):

質問 # 41
When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

A. Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.
B. Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.
C. Subword tokenization reduces the model's computational complexity by eliminating embeddings.
D. Subword tokenization removes punctuation and special characters to simplify text input.

正解：B

解説：
Subword tokenization, such as Byte-Pair Encoding (BPE) or WordPiece, is critical for preprocessing text data in LLM fine-tuning because it breaks words into smaller units (subwords), enabling the model to handle rare or out-of-vocabulary (OOV) words effectively. NVIDIA's NeMo documentation on tokenization explains that subword tokenization creates a vocabulary of frequent subword units, allowing the model to represent unseen words by combining known subwords (e.g., "unseen" as "un" + "##seen"). This improves generalization compared to word-based tokenization, which struggles with OOV words. Option A is incorrect, as tokenization does not eliminate embeddings. Option B is false, as vocabulary size is not fixed but optimized.
Option D is wrong, as punctuation handling is a separate preprocessing step.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

質問 # 42
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

A. Enabling data processing to scale to multiple GPUs.
B. Providing more memory for data analysis.
C. Using the GPU for parallel processing of data.
D. Subsampling datasets to provide rapid but approximate answers.
E. Ensuring that CPUs are running at full clock speed.

正解：A、C

解説：
RAPIDS is an open-source suite of GPU-accelerated data science libraries developed by NVIDIA to speed up data processing and machine learning workflows. According to NVIDIA's RAPIDS documentation, its key advantages include:
* Option C: Using GPUs for parallel processing, which significantly accelerates computations for tasks like data manipulation and machine learning compared to CPU-based processing.
References:
NVIDIA RAPIDS Documentation:https://rapids.ai/

質問 # 43
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

A. Single generic model can do more than one task.
B. It is easier to explain the predictions.
C. Trained without the need for labeled data.
D. Cheaper computational costs during inference.
E. Smaller latency, higher throughput.

正解：A、D

解説：
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

質問 # 44
Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

A. Embeddings
B. Class tokens
C. Long sequences
D. Translations

正解：C

解説：
The transformer architecture, introduced in "Attention is All You Need" (Vaswani et al., 2017), is particularly effective for language modeling due to its ability to handle long sequences. Unlike RNNs, which struggle with long-term dependencies due to sequential processing, transformers use self-attention mechanisms to process all tokens in a sequence simultaneously, capturing relationships across long distances. NVIDIA's NeMo documentation emphasizes that transformers excel in tasks like language modeling because their attention mechanisms scale well with sequence length, especially with optimizations like sparse attention or efficient attention variants. Option B (embeddings) is a component, not a unique strength. Option C (class tokens) is specific to certain models like BERT, not a general transformer feature. Option D (translations) is an application, not a structural advantage.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

質問 # 45
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Random initialization
B. Early stopping
C. Dropout
D. Transfer learning

正解：D

解説：
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

質問 # 46
......

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誰もが成功する可能性があって、大切なのは選択することです、その結果、適切なNCA-GENL NVIDIA Generative AI LLMs試験問題集はプロセスを簡単にすることができます、それで、PDFバージョンはあなたの最善のオプションと思います。

プラットフォームのすべての試験資料には、PDF NCA-GENL、PCテストエンジン、およびAPPテストエンジンの3つのモードが含まれています。

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NVIDIA Generative AI LLMs 認定 NCA-GENL 試験問題 (Q41-Q46):

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