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Question #:1 - [Python Libraries for LLMs]
Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?
Built-in support for CPU-based data preprocessing pipelines.
Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.
Automatic conversion of models to ONNX format for cross-platform deployment.
Simplified API for classical machine learning algorithms like SVM.
Answer: B
Explanation
The HuggingFace Transformers library is widely used for fine-tuning large language models (LLMs) due to its seamless integration with PyTorch and NVIDIA’s TensorRT, enabling GPU-accelerated training and inference. NVIDIA’s NeMo documentation references HuggingFace Transformers for its compatibility with CUDA and TensorRT, which optimize model performance on NVIDIA GPUs through features like mixedprecision training and dynamic shape inference. This makes it ideal for scaling LLM fine-tuning on GPU clusters. Option A is incorrect, as Transformers focuses on GPU, not CPU, pipelines. Option C is partially true but not the primary feature for fine-tuning. Option D is false, as Transformers is for deep learning, not classical algorithms.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp /intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index
Question #:2 - [LLM Integration and Deployment]
In the context of machine learning model deployment, how can Docker be utilized to enhance the process?
A. B. C. D.
To automatically generate features for machine learning models.
To provide a consistent environment for model training and inference.
To reduce the computational resources needed for training models.
To directly increase the accuracy of machine learning models.
Answer: B
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
Question #:3 - [Prompt Engineering]
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?
A. B. C. D.
Retrieval-augmented generation without context
Few-shot prompting with unrelated examples.
Zero-shot prompting with detailed task descriptions.
Chain-of-thought prompting with explicit intermediate steps.
Answer: D
Explanation
Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA’s NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model’s ability to follow structured
reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.
References:
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp /intro.html
Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."
Question #:4 - [LLM Integration and Deployment]
What is Retrieval Augmented Generation (RAG)?
B. C. D.
RAG is an architecture used to optimize the output of an LLM by retraining the model with domainspecific data.
RAG is a methodology that combines an information retrieval component with a response generator.
RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
RAG is a technique used to fine-tune pre-trained LLMs for improved performance.
Answer: B
Explanation
Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large language models (LLMs) by integrating an information retrieval component with a generative model. As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an external knowledge base (e.g., using dense vector representations) and uses them to inform the generative process, enabling more accurate and contextually relevant responses. NVIDIA’s documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data, especially for tasks requiring factual accuracy or domainspecific knowledge. OptionA is incorrect because RAG does not involve retraining the model but rather augments it with retrieved data. Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning, which is a separate process.
References:
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp /intro.html
Question #:5 - [Experiment Design]
A.
When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?
Chi-squared test
Paired t-test
Mann-Whitney U test
ANOVA test
Answer: B
Explanation
The paired t-test is the most appropriate statistical test to compare the performance (e.g., accuracy) of two large language models (LLMs) on the same question-answering dataset, assuming the data follows a normal distribution. This test evaluates whether the mean difference in paired observations (e.g., accuracy on each question) is statistically significant. NVIDIA’s documentation on model evaluation in NeMo suggests using paired statistical tests for comparing model performance on identical datasets to account for correlated errors. Option A (Chi-squared test) is for categorical data, not continuous metrics like accuracy. Option C (MannWhitney U test) is non-parametric and used for non-normal data. Option D (ANOVA) is for comparing more than two groups, not two models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp /model_finetuning.html
Question #:6 - [Data Analysis and Visualization]
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)
Ensuring that CPUs are running at full clock speed.
Subsampling datasets to provide rapid but approximate answers.
Using the GPU for parallel processing of data.
Enabling data processing to scale to multiple GPUs.
Providing more memory for data analysis.
Answer: C D
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/
Question #:7 - [Fundamentals of Machine Learning and Neural Networks]
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?
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.
Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.
Answer: D
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."
Question #:8 - [Prompt Engineering]
A. B. C.
D.
What is the purpose of few-shot learning in prompt engineering?
To give a model some examples
To train a model from scratch
C.
To optimize hyperparameters
To fine-tune a model on a massive dataset
Answer: A
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/deeplearning/nemo/user-guide/docs/en/stable/nlp /intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."
Question #:9 - [Fundamentals of Machine Learning and Neural Networks]
What are the main advantages of instructed large language models over traditional, small language models (< 300M parameters)? (Pick the 2 correct responses)
Trained without the need for labeled data.
Smaller latency, higher throughput.
It is easier to explain the predictions.
Cheaper computational costs during inference.
Single generic model can do more than one task.
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."
Question #:10 - [Experimentation]
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.
Cross-validation
B. C. D.
Randomized controlled trial
Average entropy approximation
Greedy decoding
Answer: A
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/deeplearning/nemo/user-guide/docs/en/stable/nlp /model_finetuning.html
Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.
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