100% PASS 2025 FIRST-GRADE NVIDIA NCA-GENL: PASS NVIDIA GENERATIVE AI LLMS GUARANTEE

100% Pass 2025 First-grade NVIDIA NCA-GENL: Pass NVIDIA Generative AI LLMs Guarantee

100% Pass 2025 First-grade NVIDIA NCA-GENL: Pass NVIDIA Generative AI LLMs Guarantee

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NVIDIA Generative AI LLMs Sample Questions (Q36-Q41):

NEW QUESTION # 36
You have access to training data but no access to test data. What evaluation method can you use to assess the performance of your AI model?

  • A. Greedy decoding
  • B. Average entropy approximation
  • C. Randomized controlled trial
  • D. Cross-validation

Answer: D

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, repeatingthis 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.
References:
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.


NEW QUESTION # 37
What type of model would you use in emotion classification tasks?

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

Answer: C

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


NEW QUESTION # 38
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

  • A. Few-shot prompting with unrelated examples.
  • B. Chain-of-thought prompting with explicit intermediate steps.
  • C. Zero-shot prompting with detailed task descriptions.
  • D. Retrieval-augmented generation without context

Answer: B

Explanation:
Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model's ability to follow structured reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."


NEW QUESTION # 39
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 bystatistically 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.

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 # 40
What is Retrieval Augmented Generation (RAG)?

  • A. RAG is a methodology that combines an information retrieval component with a response generator.
  • B. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.
  • C. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
  • D. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.

Answer: A

Explanation:
Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large language models (LLMs) by integrating an information retrieval component with a generative model. As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an external knowledge base (e.g., using dense vector representations) and uses them to inform the generative process, enabling more accurate and contextually relevant responses. NVIDIA's documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data, especially for tasks requiring factual accuracy or domain- specific knowledge. OptionA is incorrect because RAG does not involve retraining the model but rather augments it with retrieved data. Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning, which is a separate process.
References:
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html


NEW QUESTION # 41
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