NVIDIA NCA-GENL TEST FEE - NCA-GENL EXAM TIPS

NVIDIA NCA-GENL Test Fee - NCA-GENL Exam Tips

NVIDIA NCA-GENL Test Fee - NCA-GENL Exam Tips

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NVIDIA Generative AI LLMs Sample Questions (Q51-Q56):

NEW QUESTION # 51
In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

  • A. Multi-head attention simplifies the training process by reducing the number of parameters.
  • B. Multi-head attention eliminates the need for positional encodings in the input sequence.
  • C. Multi-head attention reduces the model's memory footprint by sharing weights across heads.
  • D. Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.

Answer: D

Explanation:
Multi-head attention, a core component of the transformer architecture, improves model performance by allowing the model to attend to multiple aspects of the input sequence simultaneously. Each attention head learns to focus on different relationships (e.g., syntactic, semantic) in the input, capturing diverse contextual dependencies. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA's NeMo documentation, multi-head attention enhances the expressive power of transformers, making them highly effective for complex NLP tasks like translation or question-answering. Option A is incorrect, as multi-head attention increases memory usage. Option C is false, as positional encodings are still required. Option D is wrong, asmulti-head attention adds parameters.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html


NEW QUESTION # 52
In the context of machine learning model deployment, how can Docker be utilized to enhance the process?

  • A. To provide a consistent environment for model training and inference.
  • B. To automatically generate features for machine learning models.
  • C. To reduce the computational resources needed for training models.
  • D. To directly increase the accuracy of machine learning models.

Answer: A

Explanation:
Docker is a containerization platform that ensures consistent environments for machine learning model training and inference by packaging dependencies, libraries, and configurations into portable containers.
NVIDIA's documentation on deploying models with Triton Inference Server and NGC (NVIDIA GPU Cloud) emphasizes Docker's role in eliminating environment discrepancies between development and production, ensuring reproducibility. Option A is incorrect, as Docker does not generate features. Option C is false, as Docker does not reduce computational requirements. Option D is wrong, as Docker does not affect model accuracy.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html


NEW QUESTION # 53
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

  • A. Using the GPU for parallel processing of data.
  • B. Ensuring that CPUs are running at full clock speed.
  • C. Enabling data processing to scale to multiple GPUs.
  • D. Providing more memory for data analysis.
  • E. Subsampling datasets to provide rapid but approximate answers.

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 # 54
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. Dropout
  • B. Random initialization
  • C. Early stopping
  • D. Transfer learning

Answer: D

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/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/


NEW QUESTION # 55
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

  • A. It is easier to explain the predictions.
  • B. Trained without the need for labeled data.
  • C. Smaller latency, higher throughput.
  • D. Single generic model can do more than one task.
  • E. Cheaper computational costs during inference.

Answer: D,E

Explanation:
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Brown, T., et al. (2020). "Language Models are Few-Shot Learners."


NEW QUESTION # 56
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