NCA-GENL 시험 - NVIDIA실제시험문제와 답 - 51문항

Question No : 1

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

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

정답:
Explanation:
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.
Option D: Scaling to multiple GPUs, allowing RAPIDS to handle large datasets efficiently by distributing workloads across GPU clusters.
Option A is incorrect, as RAPIDS focuses on GPU, not CPU, performance.
Option B (subsampling) is not a primary feature of RAPIDS, which aims for exact results.
Option E (more memory) is a hardware characteristic, not a RAPIDS feature.
Reference: NVIDIA RAPIDS Documentation: https://rapids.ai/

Question No : 2

Which calculation is most commonly used to measure the semantic closeness of two text passages?

A.Hamming distance
B.Jaccard similarity
C.Cosine similarity
D.Euclidean distance

정답:
Explanation:
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA’s documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT.
Option A (Hamming distance) is for binary data, not text embeddings.
Option B (Jaccard similarity) is for set-based comparisons, not semantic content.
Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Question No : 3

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.Converting text into numerical representations.
C.Removing stop words from the text.
D.Applying data augmentation techniques to generate more training data.

정답:
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.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Question No : 4

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

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

정답:
Explanation:
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.
Reference: 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

Question No : 5

Which Python library is specifically designed for working with large language models (LLMs)?

A.NumPy
B.Pandas
C.HuggingFace Transformers
D.Scikit-learn

정답:
Explanation:
The HuggingFace Transformers library is specifically designed for working with large language models (LLMs), providing tools for model training, fine-tuning, and inference with transformer-based architectures (e.g., BERT, GPT, T5). NVIDIA’s NeMo documentation often references HuggingFace Transformers for NLP tasks, as it supports integration with NVIDIA GPUs and frameworks like PyTorch for optimized performance.
Option A (NumPy) is for numerical computations, not LLMs.
Option B (Pandas) is for data manipulation, not model-specific tasks.
Option D (Scikit-learn) is for traditional machine learning, not transformer-based LLMs.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

Question No : 6

Which technology will allow you to deploy an LLM for production application?

A.Git
B.Pandas
C.Falcon
D.Triton

정답:
Explanation:
NVIDIA Triton Inference Server is a technology specifically designed for deploying machine learning models, including large language models (LLMs), in production environments. It supports high-performance inference, model management, and scalability across GPUs, making it ideal for real-time LLM applications. According to NVIDIA’s Triton Inference Server documentation, it supports frameworks like PyTorch and TensorFlow, enabling efficient deployment of LLMs with features like dynamic batching and model ensemble.
Option A (Git) is a version control system, not a deployment tool.
Option B (Pandas) is a data analysis library, irrelevant to model deployment.
Option C (Falcon) refers to a specific LLM, not a deployment platform.
Reference: NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

Question No : 7

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.Train the new model from scratch for each new category encountered.
D.Use a pre-trained language model with semantic embeddings.

정답:
Explanation:
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.
Reference: 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."

Question No : 8

What type of model would you use in emotion classification tasks?

A.Auto-encoder model
B.Siamese model
C.Encoder model
D.SVM model

정답:
Explanation:
Emotion classification tasks in natural language processing (NLP) typically involve analyzing text to predict sentiment or emotional categories (e.g., happy, sad). Encoder models, such as those based on transformer architectures (e.g., BERT), are well-suited for this task because they generate contextualized representations of input text, capturing semantic and syntactic information. NVIDIA’s NeMo framework documentation highlights the use of encoder-based models like BERT or RoBERTa for text classification tasks, including sentiment and emotion classification, due to their ability to encode input sequences into dense vectors for downstream classification.
Option A (auto-encoder) is used for unsupervised learning or reconstruction, not classification.
Option B (Siamese model) is typically used for similarity tasks, not direct classification.
Option D (SVM) is a traditional machine learning model, less effective than modern encoder-based LLMs for NLP tasks.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/text_classification.html

Question No : 9

What is the fundamental role of LangChain in an LLM workflow?

A.To act as a replacement for traditional programming languages.
B.To reduce the size of AI foundation models.
C.To orchestrate LLM components into complex workflows.
D.To directly manage the hardware resources used by LLMs.

정답:
Explanation:
LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs) by orchestrating various components, such as LLMs, external data sources, memory, and tools, into cohesive workflows. According to NVIDIA’s documentation on generative AI workflows, particularly in the context of integrating LLMs with external systems, LangChain enables developers to build complex applications by chaining together prompts, retrieval systems (e.g., for RAG), and memory modules to maintain context across interactions. For example, LangChain can integrate an LLM with a vector database for retrieval-augmented generation or manage conversational history for chatbots.
Option A is incorrect, as LangChain complements, not replaces, programming languages.
Option B is wrong, as LangChain does not modify model size.
Option D is inaccurate, as hardware management is handled by platforms like NVIDIA Triton, not LangChain.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
LangChain Official Documentation: https://python.langchain.com/docs/get_started/introduction

Question No : 10

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

정답:
Explanation:
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.
Reference: 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/

Question No : 11

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

정답:
Explanation:
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.
Option B: It is used to compare different model configurations or hyperparameters (e.g., learning rates or architectures) to identify the best setup for a specific task.
Option C is incorrect because A/B testing focuses on model performance, not dataset selection.
Option D is false, as A/B testing does not guarantee immediate improvements; it requires analysis.
Option E is wrong, as A/B testing is widely used in deep learning for real-world applications.
Reference: NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

Question No : 12

What is 'chunking' in Retrieval-Augmented Generation (RAG)?

A.Rewrite blocks of text to fill a context window.
B.A method used in RAG to generate random text.
C.A concept in RAG that refers to the training of large language models.
D.A technique used in RAG to split text into meaningful segments.

정답:
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.
Reference: 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."

Question No : 13

What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

A.Increase the clock speed of the CP
B.Using techniques like memory pooling.
C.Upgrade the GPU to a higher-end model.
D.Increase the number of CPU cores.

정답:
Explanation:
Limited throughput between CPU and GPU often results from data transfer bottlenecks or inefficient resource utilization. NVIDIA’s documentation on optimizing deep learning workflows (e.g., using CUDA and cuDNN) suggests the following:
Option B: Memory pooling techniques, such as pinned memory or unified memory, reduce data transfer overhead by optimizing how data is staged between CPU and GPU.
Option C: Upgrading to a higher-end GPU (e.g., NVIDIA A100 or H100) increases computational capacity and memory bandwidth, improving throughput for data-intensive tasks.
Option A (increasing CPU clock speed) has limited impact on CPU-GPU data transfer bottlenecks, and Option D (increasing CPU cores) is less effective unless the workload is CPU-bound, which is uncommon in GPU-accelerated deep learning.
Reference: NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
NVIDIA GPU Product Documentation: https://www.nvidia.com/en-us/data-center/products/

Question No : 14

Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

A.Training the model with additional data.
B.Choosing another model architecture.
C.Increasing the model's parameter count.
D.Leveraging the system message.

정답:
Explanation:
Prompt engineering involves designing inputs to guide large language models (LLMs) to produce desired outputs without modifying the model itself. Leveraging the system message is a key technique, where a predefined instruction or context is provided to the LLM to set the tone, role, or constraints for its responses. NVIDIA’s NeMo framework documentation on conversational AI highlights the use of system messages to improve the contextual accuracy of LLMs, especially in dialogue systems or task-specific applications. For instance, a system message like “You are a helpful technical assistant” ensures responses align with the intended role. Options A, B, and C involve model training or architectural changes, which are not part of prompt engineering.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Question No : 15

What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

A.To simplify the model's architecture, making it easier to interpret the results.
B.To artificially expand the dataset's size and improve the model's ability to generalize.
C.To ensure perfect alignment and uniformity across all images in the dataset.
D.To reduce the computational resources required for training deep learning models.

정답:
Explanation:
Image transformation techniques such as flipping, rotation, and zooming are forms of data augmentation used to artificially increase the size and diversity of a dataset. NVIDIA’s Deep Learning AI documentation, particularly for computer vision tasks using frameworks like DALI (Data Loading Library), explains that data augmentation improves a model’s ability to generalize by exposing it to varied versions of the training data, thus reducing overfitting. For example, flipping an image horizontally creates a new training sample that helps the model learn invariance to certain transformations.
Option A is incorrect because transformations do not simplify the model architecture.
Option C is wrong, as augmentation introduces variability, not uniformity.
Option D is also incorrect, as augmentation typically increases computational requirements due to additional data processing.
Reference: NVIDIA DALI Documentation: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html

NVIDIA NCA-GENL 시험