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NCA-GENL英語版 & NCA-GENLシュミレーション問題集

P.S.TopexamがGoogle Driveで共有している無料の2025 NVIDIA NCA-GENLダンプ：https://drive.google.com/open?id=1nPbXsLnJJL_PFwvySy51fz8nSKGGhzdy

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NVIDIA NCA-GENL 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• Prompt Engineering: This section of the exam measures the skills of Prompt Designers and covers how to craft effective prompts that guide LLMs to produce desired outputs. It focuses on prompt strategies, formatting, and iterative refinement techniques used in both development and real-world applications of LLMs.

トピック 2

• LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.

トピック 3

• Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.

トピック 4

• Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.

トピック 5

• Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

>> NCA-GENL英語版 <<

NVIDIA NCA-GENL英語版: 役に立つNCA-GENLシュミレーション問題集

学習の目的は何ですか？なぜ勉強する必要があるのですか？なぜNCA-GENL試験に長い間勉強したのですか？多くの人が考えるように、いつか三角形の面積の式を忘れても、私たちはまだ非常によく生きることができますが、NCA-GENL試験を学び知識を取得しようとする知識がなければ、どのようにできますか将来の生活に良い機会がありますか？したがって、試験は必要です。テストNCA-GENL認定を取得し、認定を取得し、私たちをより良く証明し、将来の人生への道を開くために。

NVIDIA Generative AI LLMs 認定 NCA-GENL 試験問題 (Q59-Q64):

質問 # 59
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)

• A. A/B testing allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
• B. A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.
• C. A/B testing guarantees immediate performance improvements in deep learning models without the need for further analysis or experimentation.
• D. A/B testing helps validate the impact of changes or updates to deep learning models by statistically analyzing the outcomes of different versions to make informed decisions for model optimization.
• E. A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.

正解：A、D

解説：
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

質問 # 60
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. Number of layers
• B. Accuracy on a validation set
• C. Model size
• D. Training duration

正解：B

解説：
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

質問 # 61
What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

• A. BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.
• B. BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.
• C. BLEU scores evaluate the 'precision' of translations, while ROUGE scores focus on the 'recall' of summarized text.
• D. BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.

正解：C

解説：
BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are metrics used to evaluate natural language processing (NLP) models, particularly for tasks like machine translation and text summarization. According to NVIDIA's NeMo documentation on NLP evaluation metrics, BLEU primarily measures the precision of n-gram overlaps between generated and reference translations, making it suitable for assessing translation quality. ROUGE, on the other hand, focuses on recall, measuring the overlap of n-grams, longest common subsequences, or skip-bigrams between generated and reference summaries, making it ideal for summarization tasks. Option A is incorrect, as BLEU and ROUGE do not measure fluency or uniqueness directly. Option B is wrong, as both metrics focus on n-gram overlap, not syntactic or semantic analysis. Option D is false, as neither metric evaluates efficiency or complexity.
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." Lin, C.-Y. (2004). "ROUGE: A Package for Automatic Evaluation of Summaries."

質問 # 62
You are using RAPIDS and Python for a data analysis project. Which pair of statements best explains how RAPIDS accelerates data science?

• A. RAPIDS is a Python library that provides functions to accelerate the PCIe bus throughput via word- doubling.
• B. RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU- GPU memory transfers.
• C. RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.

正解：B

解説：
RAPIDS is a suite of open-source libraries designed to accelerate data science workflows by leveraging GPU processing, as emphasized in NVIDIA's Generative AI and LLMs course. It enables on-GPU processing of computationally expensive calculations, such as data preprocessing and machine learning tasks, using libraries like cuDF and cuML. Additionally, RAPIDS minimizes CPU-GPU memory transfers by performing operations directly on the GPU, reducing latency and improving performance. Options A and B are identical and correct, reflecting RAPIDS' core functionality. Option C is incorrect, as RAPIDS does not focus on PCIe bus throughput or "word-doubling," which is not a relevant concept. Option D is wrong, as RAPIDS does not rely on lossless compression for acceleration but on GPU-parallel processing. The course notes: "RAPIDS accelerates data science by enabling GPU-based processing of computationally intensive tasks and minimizing CPU-GPU memory transfers, significantly speeding up workflows." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

質問 # 63
In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

• A. Use rule-based systems to manually define the characteristics of each category.
• B. Use a large, labeled dataset for each possible category.
• C. Use a pre-trained language model with semantic embeddings.
• D. Train the new model from scratch for each new category encountered.

正解：C

解説：
Zero-shot learning allows models to perform tasks or classify data into categories without prior training on those specific categories. In NLP, pre-trained language models (e.g., BERT, GPT) with semantic embeddings are highly effective for zero-shot learning because they encode general linguistic knowledge and can generalize to new tasks by leveraging semantic similarity. NVIDIA's NeMo documentation on NLP tasks explains that pre-trained LLMs can perform zero-shot classification by using prompts or embeddings to map input text to unseen categories, often via techniques like natural language inference or cosine similarity in embedding space. Option A (rule-based systems) lacks scalability and flexibility. Option B contradicts zero- shot learning, as it requires labeled data. Option C (training from scratch) is impractical and defeats the purpose of zero-shot learning.
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 # 64
......

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