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NVIDIA Generative AI LLMs Sample Questions (Q49-Q54):

NEW QUESTION # 49
What type of model would you use in emotion classification tasks?

A. SVM model
B. Siamese model
C. Auto-encoder model
D. Encoder model

Answer: D

Explanation:
Emotion classification tasks in natural language processing (NLP) typically involve analyzing text to predict sentiment or emotional categories (e.g., happy, sad). Encoder models, such as those based on transformer architectures (e.g., BERT), are well-suited for this task because they generate contextualized representations of input text, capturing semantic and syntactic information. NVIDIA's NeMo framework documentation highlights the use of encoder-based models like BERT or RoBERTa for text classification tasks, including sentiment and emotion classification, due to their ability to encode input sequences into dense vectors for downstream classification. Option A (auto-encoder) is used for unsupervised learning or reconstruction, not classification. Option B (Siamese model) is typically used for similarity tasks, not direct classification. Option D (SVM) is a traditional machine learning model, less effective than modern encoder-based LLMs for NLP tasks.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/text_classification.html

NEW QUESTION # 50
Which technology will allow you to deploy an LLM for production application?

A. Pandas
B. Falcon
C. Triton
D. Git

Answer: C

Explanation:
NVIDIA Triton Inference Server is a technology specifically designed for deploying machine learning models, including large language models (LLMs), in production environments. It supports high-performance inference, model management, and scalability across GPUs, making it ideal for real-time LLM applications.
According to NVIDIA's Triton Inference Server documentation, it supports frameworks like PyTorch and TensorFlow, enabling efficient deployment of LLMs with features like dynamic batching and model ensemble. Option A (Git) is a version control system, not a deployment tool. Option B (Pandas) is a data analysis library, irrelevant to model deployment. Option C (Falcon) refers to a specific LLM, not a deployment platform.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 51
Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

A. Developing AI systems with autonomy from human decision-making.
B. Ensuring AI systems have explicable decision-making processes.
C. Achieving the highest possible level of prediction accuracy in AI models.
D. Implementing complex algorithms to enhance AI's problem-solving capabilities.

Answer: B

Explanation:
Ensuring explicable decision-making processes, often referred to as explainability or interpretability, is critical for aligning AI systems with societal values and norms. NVIDIA's Trustworthy AI framework emphasizes that explainable AI allows stakeholders to understand how decisions are made, fostering trust and ensuring compliance with ethical standards. This is particularly important for addressing biases and ensuring fairness. Option A (prediction accuracy) is important but does not guarantee ethical alignment. Option B (complex algorithms) may improve performance but not societal alignment. Option C (autonomy) can conflict with ethical oversight, making it less desirable.
References:
NVIDIA Trustworthy AI:https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

NEW QUESTION # 52
You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is the rationale for using A/B testing with deep learning model performance?

A. A/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.
B. A/B testing ensures that the deep learning model is robust and can handle different variations of input data.
C. A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.
D. A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.

Answer: A

Explanation:
A/B testing is a controlled experimentation method used to compare two versions of a system (e.g., two model variants) to determine which performs better based on a predefined metric (e.g., user engagement, accuracy).
NVIDIA's documentation on model optimization and deployment, such as with Triton Inference Server, highlights A/B testing as a method to validate model improvements in real-world settings by comparing performance metrics statistically. For a recommendation system, A/B testing might compare click-through rates between two models. Option B is incorrect, as A/B testing focuses on outcomes, not designer commentary. Option C is misleading, as robustness is tested via other methods (e.g., stress testing). Option D is partially true but narrow, as A/B testing evaluates broader performance metrics, not just latency.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

NEW QUESTION # 53
Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

A. It only involves reducing the number of bits of the parameters.
B. Helps reduce memory requirements and achieve better cache utilization.
C. It consists of removing a quantity of weights whose values are zero.
D. Quantization might help in saving power and reducing heat production.
E. It leads to a substantial loss of model accuracy.

Answer: B,D

Explanation:
Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32- bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA's documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:
* Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.
References:
NVIDIA TensorRT Documentation: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

NEW QUESTION # 54
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