Free NVIDIA NCA-GENL Exam Dumps Questions & Answers

What statement best describes the diffusion models in generative AI?

• A.  Diffusion models are probabilistic generative models that progressively inject noise into data, then learn to reverse this process for sample generation.
• B.  Diffusion models are unsupervised models that use clustering algorithms to group similar data points together.
• C.  Diffusion models are discriminative models that use gradient-based optimization algorithms to classify data points.
• D.  Diffusion models are generative models that use a transformer architecture to learn the underlying probability distribution of the data.

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?

• A.  Converting text to phonetic representations for cross-lingual alignment.
• B.  Normalizing all text to a single script using transliteration.
• C.  Applying Unicode normalization to standardize character encodings.
• D.  Removing all non-Latin characters to simplify the input.

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.  Randomized controlled trial
• C.  Average entropy approximation
• D.  Cross-validation

How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?

• A.  RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.
• B.  RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.
• C.  RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.
• D.  RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.

Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?

• A.  Built-in support for CPU-based data preprocessing pipelines.
• B.  Automatic conversion of models to ONNX format for cross-platform deployment.
• C.  Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.
• D.  Simplified API for classical machine learning algorithms like SVM.