매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
NVIDIA Generative AI LLMs 온라인 연습
최종 업데이트 시간: 2025년10월03일
당신은 온라인 연습 문제를 통해 NVIDIA NCA-GENL 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 NCA-GENL 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 51개의 시험 문제와 답을 포함하십시오.
정답:
Explanation:
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.
Reference: 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."
정답:
Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs. NVIDIA’s documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models.
Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility.
Option C describes a linear waterfall approach, not RAD.
Option D is false, as RAD relies heavily on user feedback.
Reference: NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html
정답:
Explanation:
Diffusion models, a class of generative AI models, operate in two phases: forward diffusion and reverse diffusion. According to NVIDIA’s documentation on generative AI (e.g., in the context of NVIDIA’s work on generative models), forward diffusion progressively injects noise into a data sample (e.g., an image or text embedding) over multiple steps, transforming it into a noise distribution. Reverse diffusion, conversely, starts with a noise vector and iteratively denoises it to generate a new sample that resembles the training data distribution. This process is central to models like DDPM (Denoising Diffusion Probabilistic Models).
Option A is incorrect, as forward diffusion adds noise, not generates samples.
Option B is false, as diffusion models typically use convolutional or transformer-based architectures, not recurrent networks.
Option C is misleading, as diffusion does not align with bottom-up/top-down processing paradigms.
Reference: NVIDIA Generative AI Documentation: https://www.nvidia.com/en-us/ai-data-science/generative-ai/ Ho, J., et al. (2020). "Denoising Diffusion Probabilistic Models."
정답:
Explanation:
A tokenizer in the context of large language models (LLMs) is a tool that splits text into smaller units called tokens (e.g., words, subwords, or characters) for processing by the model. NVIDIA’s NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with algorithms like WordPiece, Byte-Pair Encoding (BPE), or SentencePiece breaking text into manageable units to handle vocabulary constraints and out-of-vocabulary words. For example, the sentence “I love AI” might be tokenized into [“I”, “love”, “AI”] or subword units like [“I”, “lov”, “##e”, “AI”].
Option A is incorrect, as removing stop words is a separate preprocessing step.
Option B is wrong, as tokenization is not a predictive algorithm.
Option D is misleading, as converting text to numerical representations is the role of embeddings, not tokenization.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
정답:
Explanation:
When test data is unavailable, cross-validation is the most effective method to assess an AI model’s performance using only the training dataset. Cross-validation involves splitting the training data into multiple subsets (folds), training the model on some folds, and validating it on others, repeating this process to estimate generalization performance. NVIDIA’s documentation on machine learning workflows, particularly in the NeMo framework for model evaluation, highlights k-fold cross-validation as a standard technique for robust performance assessment when a separate test set is not available.
Option B (randomized controlled trial) is a clinical or experimental method, not typically used for model evaluation.
Option C (average entropy approximation) is not a standard evaluation method.
Option D (greedy decoding) is a generation strategy for LLMs, not an evaluation technique.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.
정답:
Explanation:
A/B testing is a controlled experimentation method used to compare two versions of a system (e.g., two model variants) to determine which performs better based on a predefined metric (e.g., user engagement, accuracy). NVIDIA’s documentation on model optimization and deployment, such as with Triton Inference Server, highlights A/B testing as a method to validate model improvements in real-world settings by comparing performance metrics statistically. For a recommendation system, A/B testing might compare click-through rates between two models.
Option B is incorrect, as A/B testing focuses on outcomes, not designer commentary.
Option C is misleading, as robustness is tested via other methods (e.g., stress testing).
Option D is partially true but narrow, as A/B testing evaluates broader performance metrics, not just latency.
Reference: NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
정답:
Explanation:
Docker is a containerization platform that ensures consistent environments for machine learning model training and inference by packaging dependencies, libraries, and configurations into portable containers. NVIDIA’s documentation on deploying models with Triton Inference Server and NGC (NVIDIA GPU Cloud) emphasizes Docker’s role in eliminating environment discrepancies between development and production, ensuring reproducibility.
Option A is incorrect, as Docker does not generate features.
Option C is false, as Docker does not reduce computational requirements.
Option D is wrong, as Docker does not affect model accuracy.
Reference: NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html
정답:
Explanation:
NVIDIA Triton Inference Server is a high-performance framework designed for deploying machine learning models, including NLP models, in production environments. It supports optimized inference on GPUs, dynamic batching, and integration with frameworks like PyTorch and TensorFlow. According to NVIDIA’s Triton documentation, it is ideal for deploying LLMs for real-time applications with low latency.
Option A (DeepStream) is for video analytics, not NLP.
Option B (HuggingFace) is a library for
model development, not deployment.
Option C (NeMo) is for training and fine-tuning, not
production deployment.
Reference: NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
정답:
Explanation:
Regular expression (regex) filters are widely used in data preprocessing to select text data containing
specific keywords or patterns. NVIDIA’s documentation on data preprocessing for NLP tasks, such as
in NeMo, highlights regex as a standard tool for filtering datasets based on textual criteria, enabling
efficient data curation. For example, a regex pattern like .*keyword.* can select all texts containing
“keyword.” Option A (ActionScript) is a programming language for multimedia, not data filtering.
Option B (Tableau) is for visualization, not text filtering.
Option C (JSON parser) is for structured data,
not keyword-based text selection.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
정답:
Explanation:
Attention mechanisms in transformer models, as introduced in "Attention is All You Need" (Vaswani et al., 2017), allow the model to focus on relevant parts of the input sequence by assigning higher weights to important tokens during processing. NVIDIA’s NeMo documentation explains that self-attention enables transformers to capture long-range dependencies and contextual relationships, making them effective for tasks like language modeling and translation.
Option B is incorrect, as attention does not compress sequences but processes them fully.
Option C is false, as attention is not about generating noise.
Option D refers to embeddings, not attention.
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
정답:
Explanation:
Ensuring explicable decision-making processes, often referred to as explainability or interpretability, is critical for aligning AI systems with societal values and norms. NVIDIA’s Trustworthy AI framework emphasizes that explainable AI allows stakeholders to understand how decisions are made, fostering trust and ensuring compliance with ethical standards. This is particularly important for addressing biases and ensuring fairness.
Option A (prediction accuracy) is important but does not guarantee ethical alignment.
Option B (complex algorithms) may improve performance but not societal alignment.
Option C (autonomy) can conflict with ethical oversight, making it less desirable.
Reference: NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/
정답:
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^(-x)). Sigmoid outputs values in the range [0, 1], making it suitable for predicting probabilities in binary classification tasks. ReLU, with an unbounded positive range, is less suited for direct probability prediction but accelerates training by mitigating vanishing gradient issues.
Option A is incorrect, as ReLU is non-linear (piecewise linear).
Option B is false, as ReLU is more efficient and not inherently more accurate.
Option C is wrong, as
ReLU’s range is [0, ∞), not [0, 1].
Reference: NVIDIA cuDNN Documentation: https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html
Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.
정답:
Explanation:
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.
Option E: A single generic LLM can perform multiple tasks (e.g., text generation, classification, translation) due to its broad pre-training, unlike smaller models that are typically task-specific.
Option A is incorrect, as LLMs require large amounts of data, often labeled or curated, for pre-training.
Option B is false, as LLMs typically have higher latency and lower throughput due to their size.
Option C is misleading, as LLMs are often less interpretable than smaller models.
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."
정답:
Explanation:
Accountability is a core principle of Trustworthy AI that addresses the ethical implications of AI’s societal impact, including potential misuse and unintended consequences. NVIDIA’s guidelines on Trustworthy AI, as outlined in their AI ethics framework, emphasize accountability as ensuring that AI systems are transparent, responsible, and answerable for their outcomes. This includes mitigating risks of bias, ensuring fairness, and addressing unintended societal impacts.
Option A (Certification) refers to compliance processes, not ethical implications.
Option B (Data Privacy) focuses on protecting user data, not broader societal impact.
Option D (Legal Responsibility) is related but narrower, focusing on liability rather than ethical considerations.
Reference: NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/
정답:
Explanation:
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.
Reference: 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."