THREE NVIDIA NCA-GENL EXAM QUESTIONS FORMATS - MAKE YOUR EXAM PREPARATION EASY

Three NVIDIA NCA-GENL Exam Questions Formats - Make Your Exam Preparation Easy

Three NVIDIA NCA-GENL Exam Questions Formats - Make Your Exam Preparation Easy

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NVIDIA Generative AI LLMs Sample Questions (Q52-Q57):

NEW QUESTION # 52
When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?

  • A. Reducing the input sequence length to minimize token processing.
  • B. Increasing the model's parameter count to improve response quality.
  • C. Switching to a CPU-based inference engine for better scalability.
  • D. Enabling dynamic batching to process multiple requests simultaneously.

Answer: D

Explanation:
NVIDIA Triton Inference Server is designed for high-performance model deployment, and dynamicbatching is a key optimization technique for reducing latency while maintaining high throughput in real-time applications like chatbots. Dynamic batching groups multiple inference requests into a single batch, leveraging GPU parallelism to process them simultaneously, thus reducing per-request latency. According to NVIDIA's Triton documentation, this is particularly effective for LLMs with variable input sizes, as it maximizes resource utilization. Option A is incorrect, as increasing parameters increases latency. Option C may reduce latency but sacrifices context and quality. Option D is false, as CPU-based inference is slower than GPU-based for LLMs.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html


NEW QUESTION # 53
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. Model size
  • C. Training duration
  • D. Accuracy on a validation set

Answer: D

Explanation:
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


NEW QUESTION # 54
What is a Tokenizer in Large Language Models (LLM)?

  • A. A machine learning algorithm that predicts the next word/token in a sequence of text.
  • B. A tool used to split text into smaller units called tokens for analysis and processing.
  • C. A method to remove stop words and punctuation marks from text data.
  • D. A technique used to convert text data into numerical representations called tokens for machine learning.

Answer: B

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


NEW QUESTION # 55
In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

  • A. To optimize the model's inference speed for production deployment.
  • B. To automate the collection of training data for fine-tuning.
  • C. To increase the model's parameter count for better performance.
  • D. To identify and mitigate potential biases, safety risks, and harmful outputs.

Answer: D

Explanation:
Red-teaming exercises involve systematically testing a large language model (LLM) by probing it with adversarial or challenging inputs to uncover vulnerabilities, such as biases, unsafe responses, or harmful outputs. NVIDIA's Trustworthy AI framework emphasizes red-teaming as a critical stepin the alignment process to ensure LLMs adhere to ethical standards and societal values. By simulating worst-case scenarios, red-teaming helps developers identify and mitigate risks, such as generating toxic content or reinforcing stereotypes, before deployment. Option A is incorrect, as red-teaming focuses on safety, not speed. Option C is false, as it does not involve model size. Option D is wrong, as red-teaming is about evaluation, not data collection.
References:
NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/


NEW QUESTION # 56
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.

Answer: C

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.
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."


NEW QUESTION # 57
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