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Online NVIDIA NCA-GENL Tests - NCA-GENL Sure Pass

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NVIDIA Generative AI LLMs Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which calculation is most commonly used to measure the semantic closeness of two text passages?

A. Euclidean distance
B. Jaccard similarity
C. Cosine similarity
D. Hamming distance

Answer: C

Explanation:
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

NEW QUESTION # 48
In neural networks, the vanishing gradient problem refers to what problem or issue?

A. The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.
B. The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data.
C. The issue of gradients becoming too large during backpropagation, leading to unstable training.
D. The problem of overfitting in neural networks, where the model performs well on the trainingdata but poorly on new, unseen data.

Answer: A

Explanation:
The vanishing gradient problem occurs in deep neural networks when gradients become too small during backpropagation, causing slow convergence or stagnation in training, particularly in deeper layers. NVIDIA's documentation on deep learning fundamentals, such as in CUDA and cuDNN guides, explains that this issue is common in architectures like RNNs or deep feedforward networks with certain activation functions (e.g., sigmoid). Techniques like ReLU activation, batch normalization, or residual connections (used in transformers) mitigate this problem. Option A (overfitting) is unrelated to gradients. Option B describes the exploding gradient problem, not vanishing gradients. Option C (underfitting) is a performance issue, not a gradient-related problem.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

NEW QUESTION # 49
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

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

Answer: D,E

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 # 50
In the context of transformer-based large language models, how does the use of layer normalization mitigate the challenges associated with training deep neural networks?

A. It stabilizes training by normalizing the inputs to each layer, reducing internal covariate shift.
B. It reduces the computational complexity by normalizing the input embeddings.
C. It increases the model's capacity by adding additional parameters to each layer.
D. It replaces the attention mechanism to improve sequence processing efficiency.

Answer: A

Explanation:
Layer normalization is a technique used in transformer-based large language models (LLMs) to stabilize and accelerate training by normalizing the inputs to each layer. According to the original transformer paper ("Attention is All You Need," Vaswani et al., 2017) and NVIDIA's NeMo documentation, layer normalization reduces internal covariate shift by ensuring that the mean andvariance of activations remain consistent across layers, mitigating issues like vanishing or exploding gradients in deep networks. This is particularly crucial in transformers, which have many layers and process long sequences, making them prone to training instability. By normalizing the activations (typically after the attention and feed-forward sub- layers), layer normalization improves gradient flow and convergence. Option A is incorrect, as layer normalization does not reduce computational complexity but adds a small overhead. Option C is false, as it does not add significant parameters. Option D is wrong, as layer normalization complements, not replaces, the attention mechanism.
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 # 51
In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

A. It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
B. It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
C. It requires AI systems to be developed with an ethical consideration for societal impacts.
D. It ensures that AI systems are transparent in their decision-making processes.

Answer: A

Explanation:
In the development of Trustworthy AI, 'Certification' as a principle involves verifying that AI models are fit for their intended purpose according to regional or industry-specific standards, as discussed in NVIDIA's Generative AI and LLMs course. Certification ensures that models meet performance, safety, and ethical benchmarks, providing assurance to stakeholders about their reliability and appropriateness. Option A is incorrect, as transparency is a separate principle, not certification. Option B is wrong, as ethical considerations are broader and not specific to certification. Option D is inaccurate, as compliance with laws is related but distinct from certification's focus on fitness for purpose. The course states: "Certification in Trustworthy AI verifies that models meet regional or industry-specific standards, ensuring they are fit for their intended purpose and reliable." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 52
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