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NVIDIA NCA-GENL Exam Syllabus Topics:
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NVIDIA Generative AI LLMs Sample Questions (Q14-Q19):
NEW QUESTION # 14
Which of the following is a parameter-efficient fine-tuning approach that one can use to fine-tune LLMs in a memory-efficient fashion?
Answer: D
Explanation:
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning approach specifically designed for large language models (LLMs), as covered in NVIDIA's Generative AI and LLMs course. It fine-tunes LLMs by updating a small subset of parameters through low-rank matrix factorization, significantly reducing memory and computational requirements compared to full fine-tuning. This makes LoRA ideal for adapting large models to specific tasks while maintaining efficiency. Option A, TensorRT, is incorrect, as it is an inference optimization library, not a fine-tuning method. Option B, NeMo, is a framework for building AI models, not a specific fine-tuning technique. Option C, Chinchilla, is a model, not a fine-tuning approach. The course emphasizes: "Parameter-efficient fine-tuning methods like LoRA enable memory-efficient adaptation of LLMs by updating low-rank approximations of weight matrices, reducing resource demands while maintaining performance." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.
NEW QUESTION # 15
In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?
Answer: B
Explanation:
When preparing a multilingual dataset for fine-tuning an LLM, applying Unicode normalization (e.g., NFKC or NFC forms) is the most effective preprocessing technique to handle text from diverse scripts like Latin, Cyrillic, or Devanagari. Unicode normalization standardizes character encodings, ensuring that visually identical characters (e.g., precomposed vs. decomposed forms) are represented consistently, which improves model performance across languages. NVIDIA's NeMo documentation on multilingual NLP preprocessing recommends Unicode normalization to address encoding inconsistencies in diverse datasets. Option A (transliteration) may lose linguistic nuances. Option C (removing non-Latin characters) discards critical information. Option D (phonetic conversion) is impractical for text-based LLMs.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
NEW QUESTION # 16
What is the Open Neural Network Exchange (ONNX) format used for?
Answer: C
Explanation:
The Open Neural Network Exchange (ONNX) format is an open-standard representation for deep learning models, enabling interoperability across different frameworks, as highlighted in NVIDIA's Generative AI and LLMs course. ONNX allows models trained in frameworks like PyTorch or TensorFlow to be exported and used in other compatible tools for inference or further development, ensuring portability and flexibility.
Option B is incorrect, as ONNX is not designed to reduce training time but to standardize model representation. Option C is wrong, as model compression is handled by techniques like quantization, not ONNX. Option D is inaccurate, as ONNX is unrelated to sharing literature. The course states: "ONNX is an open format for representing deep learning models, enabling seamless model exchange and deployment across various frameworks and platforms." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.
NEW QUESTION # 17
You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is the rationale for using A/B testing with deep learning model performance?
Answer: D
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.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html
NEW QUESTION # 18
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)
Answer: A,C
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.
References:
NVIDIA RAPIDS Documentation:https://rapids.ai/
NEW QUESTION # 19
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