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[General] Free PDF 2026 Latest NVIDIA NCA-GENL: NVIDIA Generative AI LLMs Exam Outline

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【General】 Free PDF 2026 Latest NVIDIA NCA-GENL: NVIDIA Generative AI LLMs Exam Outline

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NVIDIA Generative AI LLMs Sample Questions (Q37-Q42):NEW QUESTION # 37
What is the purpose of few-shot learning in prompt engineering?
  • A. To optimize hyperparameters
  • B. To fine-tune a model on a massive dataset
  • C. To give a model some examples
  • D. To train a model from scratch
Answer: C
Explanation:
Few-shot learning in prompt engineering involves providing a small number of examples (demonstrations) within the prompt to guide a large language model (LLM) to perform a specific task without modifying its weights. NVIDIA's NeMo documentation on prompt-based learning explains that few-shot prompting leverages the model's pre-trained knowledge by showing it a few input-output pairs, enabling it to generalize to new tasks. For example, providing two examples of sentiment classification in a prompt helps the model understand the task. Option B is incorrect, as few-shot learning does not involve training from scratch. Option C is wrong, as hyperparameter optimization is a separate process. Option D is false, as few-shot learning avoids large-scale fine-tuning.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

NEW QUESTION # 38
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
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/deeplear ... del_finetuning.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

NEW QUESTION # 39
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. Random initialization
  • B. Transfer learning
  • C. Early stopping
  • 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/deeplear ... /docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

NEW QUESTION # 40
What is confidential computing?
  • A. A technique for securing computer hardware and software from potential threats.
  • B. A process for designing and applying AI systems in a manner that is explainable, fair, and verifiable.
  • C. A technique for aligning the output of the AI models with human beliefs.
  • D. A method for interpreting and integrating various forms of data in AI systems.
Answer: A
Explanation:
Confidential computing is a technique for securing computer hardware and software from potential threats by protecting data in use, as covered in NVIDIA's Generative AI and LLMs course. It ensures that sensitive data, such as model weights or user inputs, remains encrypted during processing, using technologies like secure enclaves or trusted execution environments (e.g., NVIDIA H100 GPUs with confidential computing capabilities). This enhances the security of AI systems. Option B is incorrect, as it describes Trustworthy AI principles, not confidential computing. Option C is wrong, as aligning outputs with human beliefs is unrelated to security. Option D is inaccurate, as data integration is not the focus of confidential computing. The course notes: "Confidential computing secures AI systems by protecting data in use, leveraging trusted execution environments to safeguard sensitive information during processing." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 41
Which of the following optimizations are provided by TensorRT? (Choose two.)
  • A. Variable learning rate
  • B. Data augmentation
  • C. Residual connections
  • D. Multi-Stream Execution
  • E. Layer Fusion
Answer: D,E
Explanation:
NVIDIA TensorRT provides optimizations to enhance the performance of deep learning models during inference, as detailed in NVIDIA's Generative AI and LLMs course. Two key optimizations are multi-stream execution and layer fusion. Multi-stream execution allows parallel processing of multiple input streams on the GPU, improving throughput for concurrent inference tasks. Layer fusion combines multiple layers of a neural network (e.g., convolution and activation) into a single operation, reducing memory access and computation time. Option A, data augmentation, is incorrect, as it is a preprocessing technique, not a TensorRT optimization. Option B, variable learning rate, is a training technique, not relevant to inference. Option E, residual connections, is a model architecture feature, not a TensorRT optimization. The course states:
"TensorRT optimizes inference through techniques like layer fusion, which combines operations to reduce overhead, and multi-stream execution, which enables parallel processing for higher throughput." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 42
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