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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Associate - Generative AI LLMs |
| Exam Number: | NCA-GENL |
| Exam Format: | Multiple-Select, Multiple-Choice |
| Exam Duration: | 60 minutes |
| Exam Price: | USD 125 |
| Certificate Validity Period: | 2 years |
| Available Languages: | Simplified Chinese, English |
| Real Exam Qty: | 50-60 |
| Related Certifications: | NVIDIA Certified Associate Generative AI LLMs Certification |
| Sample Questions: | NVIDIA NCA-GENL Sample Questions |
| Exam Way: | Online proctored via Certiverse platform |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-llm-professional |
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NEW QUESTION # 61
What is Retrieval Augmented Generation (RAG)?
Answer: D
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 # 62
When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?
Answer: D
Explanation:
NVIDIA RAPIDS cuDF is a GPU-accelerated library that mimics Pandas' API but performs data manipulation on GPUs, significantly speeding up preprocessing tasks for LLM fine-tuning. The key feature enabling this performance is GPU-accelerated columnar data processing with zero-copy memory access, which allows cuDF to leverage the parallel processing power of GPUs and avoid unnecessary data transfers between CPU and GPU memory. According to NVIDIA's RAPIDS documentation, cuDF's columnar format and CUDA-based operations enable orders-of-magnitude faster data operations (e.g., filtering, grouping) compared to CPU-based Pandas. Option A is incorrect, as cuDF uses GPUs, not CPUs. Option C is false, as cloud integration is not a core cuDF feature. Option D is wrong, as cuDF does not rely on SQL tables.
References:
NVIDIA RAPIDS Documentation: https://rapids.ai/
NEW QUESTION # 63
Which of the following is a feature of the NVIDIA Triton Inference Server?
Answer: C
Explanation:
The NVIDIA Triton Inference Server is designed to optimize and deploy machine learning models for inference, and one of its key features is dynamic batching, as noted in NVIDIA's Generative AI and LLMs course. Dynamic batching automatically groups inference requests into batches to maximize GPU utilization, reducing latency and improving throughput for real-time applications. Option A, model quantization, is incorrect, as it is typically handled by frameworks like TensorRT, not Triton. Option C, gradient clipping, is a training technique, not an inference feature. Option D, model pruning, is a model optimization method, not a Triton feature. The course states: "NVIDIA Triton Inference Server supports dynamic batching, which optimizes inference by grouping requests to maximize GPU efficiency and throughput." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.
NEW QUESTION # 64
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?
Answer: C
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 # 65
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)
Answer: C,D
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 # 66
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