VCE NCA-GENL FREE | REAL NCA-GENL TORRENT

Vce NCA-GENL Free | Real NCA-GENL Torrent

Vce NCA-GENL Free | Real NCA-GENL Torrent

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NVIDIA Generative AI LLMs Sample Questions (Q39-Q44):

NEW QUESTION # 39
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. Providing more memory for data analysis.
  • C. Enabling data processing to scale to multiple GPUs.
  • D. Ensuring that CPUs are running at full clock speed.
  • 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 # 40
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

  • A. Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.
  • B. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.
  • C. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
  • D. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.

Answer: B

Explanation:
Diffusion models, a class of generative AI models, operate in two phases: forward diffusion and reverse diffusion. According to NVIDIA's documentation on generative AI (e.g., in the context of NVIDIA's work on generative models), forward diffusion progressively injects noise into a data sample (e.g., an image or text embedding) over multiple steps, transforming it into a noise distribution. Reverse diffusion, conversely, starts with a noise vector and iteratively denoises it to generate a new sample that resembles the training data distribution. This process is central tomodels like DDPM (Denoising Diffusion Probabilistic Models). Option A is incorrect, as forward diffusion adds noise, not generates samples. Option B is false, as diffusion models typically use convolutional or transformer-based architectures, not recurrent networks. Option C is misleading, as diffusion does not align with bottom-up/top-down processing paradigms.
References:
NVIDIA Generative AI Documentation: https://www.nvidia.com/en-us/ai-data-science/generative-ai/ Ho, J., et al. (2020). "Denoising Diffusion Probabilistic Models."


NEW QUESTION # 41
What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

  • A. To ensure perfect alignment and uniformity across all images in the dataset.
  • B. To simplify the model's architecture, making it easier to interpret the results.
  • C. To reduce the computational resources required for training deep learning models.
  • D. To artificially expand the dataset's size and improve the model's ability to generalize.

Answer: D

Explanation:
Image transformation techniques such as flipping, rotation, and zooming are forms of data augmentation used to artificially increase the size and diversity of a dataset. NVIDIA's Deep Learning AI documentation, particularly for computer vision tasks using frameworks like DALI (Data Loading Library), explains that data augmentation improves a model's ability to generalize by exposing it to varied versions of the training data, thus reducing overfitting. For example, flipping an image horizontally creates a new training sample that helps the model learn invariance to certain transformations. Option A is incorrect because transformations do not simplify the model architecture. Option C is wrong, as augmentation introduces variability, not uniformity. Option D is also incorrect, as augmentation typically increases computational requirements due to additional data processing.
References:
NVIDIA DALI Documentation: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html


NEW QUESTION # 42
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?

  • A. Bootstrapping with random sampling.
  • B. Stratified k-fold cross-validation.
  • C. Grid search for hyperparameter tuning.
  • D. Single hold-out validation with a fixed test set.

Answer: B

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 # 43
Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

  • A. Embeddings
  • B. Class tokens
  • C. Long sequences
  • D. Translations

Answer: C

Explanation:
The transformer architecture, introduced in "Attention is All You Need" (Vaswani et al., 2017), is particularly effective for language modeling due to its ability to handle long sequences. Unlike RNNs, which struggle with long-term dependencies due to sequential processing, transformers use self-attention mechanisms to process all tokens in a sequence simultaneously, capturing relationships across long distances. NVIDIA's NeMo documentation emphasizes that transformers excel in tasks like language modeling because their attention mechanisms scale well with sequence length, especially with optimizations like sparse attention or efficient attention variants. Option B (embeddings) is a component, not a unique strength. Option C (class tokens) is specific to certain models like BERT, not a general transformer feature. Option D (translations) is an application, not a structural advantage.
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 # 44
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