GET ACCURATE ANSWERS AND REALISTIC PRACTICE WITH NVIDIA'S NCA-GENL EXAM QUESTIONS

Get Accurate Answers and Realistic Practice with NVIDIA's NCA-GENL Exam Questions

Get Accurate Answers and Realistic Practice with NVIDIA's NCA-GENL Exam Questions

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Tags: Simulation NCA-GENL Questions, NCA-GENL Valid Test Fee, NCA-GENL Exam Pass Guide, NCA-GENL Latest Test Materials, NCA-GENL Examinations Actual Questions

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NVIDIA NCA-GENL Valid Test Fee | NCA-GENL Exam Pass Guide

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NVIDIA Generative AI LLMs Sample Questions (Q11-Q16):

NEW QUESTION # 11
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. Early stopping
  • B. Transfer learning
  • C. Random initialization
  • D. Dropout

Answer: B

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.
References:
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/


NEW QUESTION # 12
In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

  • A. Bootstrapping with random sampling.
  • B. Grid search for hyperparameter tuning.
  • C. Single hold-out validation with a fixed test set.
  • D. Stratified k-fold cross-validation.

Answer: D

Explanation:
Stratified k-fold cross-validation is a robust experimental design technique for evaluating machine learning models, especially on imbalanced datasets. It divides the dataset into k folds while preserving the class distribution in each fold, ensuring that the model is evaluated on representative samples of all classes.
NVIDIA's NeMo documentation on model evaluation recommends stratified cross-validation for tasks like text classification to obtain reliable performance estimates, particularly when classes are unevenly distributed (e.g., in sentiment analysis with few negative samples). Option A (single hold-out) is less robust, as it may not capture class imbalance. Option C (bootstrapping) introduces variability and is less suitable for imbalanced data. Option D (grid search) is for hyperparameter tuning, not performance estimation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html


NEW QUESTION # 13
Which metric is commonly used to evaluate machine-translation models?

  • A. F1 Score
  • B. BLEU score
  • C. ROUGE score
  • D. Perplexity

Answer: B

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.
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."


NEW QUESTION # 14
What is the fundamental role of LangChain in an LLM workflow?

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

Answer: A

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.
References:
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


NEW QUESTION # 15
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 bystatistically analyzing the outcomes of different versions to make informed decisions for model optimization.
  • 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 allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
  • E. A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.

Answer: A,D

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.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html


NEW QUESTION # 16
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