NCA-GENL시험대비공부하기 - NCA-GENL최고품질시험덤프자료
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NCA-GENL최고품질 시험덤프자료 - NCA-GENL자격증공부
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최신 NVIDIA-Certified Associate NCA-GENL 무료샘플문제 (Q61-Q66):
질문 # 61
In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?
정답：C
설명：
When preparing a multilingual dataset for fine-tuning an LLM, applying Unicode normalization (e.g., NFKC or NFC forms) is the most effective preprocessing technique to handle text from diverse scripts like Latin, Cyrillic, or Devanagari. Unicode normalization standardizes character encodings, ensuring that visually identical characters (e.g., precomposed vs. decomposed forms) are represented consistently, which improves model performance across languages. NVIDIA's NeMo documentation on multilingual NLP preprocessing recommends Unicode normalization to address encoding inconsistencies in diverse datasets. Option A (transliteration) may lose linguistic nuances. Option C (removing non-Latin characters) discards critical information. Option D (phonetic conversion) is impractical for text-based LLMs.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
질문 # 62
How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)
정답：D,E
설명：
A/B testing is a controlled experimentation technique used to compare two versions of a system to determine which performs better. In the context of deep learning, NVIDIA's documentation on model optimization and deployment (e.g., Triton Inference Server) highlights its use in evaluating model performance:
* Option A: A/B testing validates changes (e.g., model updates or new features) by statistically comparing outcomes (e.g., accuracy or user engagement), enabling data-driven optimization decisions.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html
질문 # 63
What is the main consequence of the scaling law in deep learning for real-world applications?
정답：B
설명：
The scaling law in deep learning, as covered in NVIDIA's Generative AI and LLMs course, describes the relationship between model performance, data size, model size, and computational resources. In the power- law region, increasing the amount of data, model parameters, or compute power leads to predictable improvements in performance, as errors decrease following a power-law trend. This has significant implications for real-world applications, as it suggests that scaling up data and resources can yield better results, particularly for large language models (LLMs). Option A is incorrect, as the irreducible error represents the inherent noise in the data, which cannot be exceeded regardless of data size. Option B is wrong, as small data regions typically yield suboptimal performance compared to scaled models. Option C is misleading, as small and medium data regimes do not typically match big data performance without scaling.
The course highlights: "In the power-law region of the scaling law, increasing data and compute resources leads to better model performance, driving advancements in real-world deep learning applications." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.
질문 # 64
Which metric is commonly used to evaluate machine-translation models?
정답：B
설명：
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."
질문 # 65
Which metric is commonly used to evaluate machine-translation models?
정답：C
설명：
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."
질문 # 66
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