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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

Prompt Engineering: This section of the exam measures the skills of Prompt Designers and covers how to craft effective prompts that guide LLMs to produce desired outputs. It focuses on prompt strategies, formatting, and iterative refinement techniques used in both development and real-world applications of LLMs.

Topic 2

Experiment Design

Topic 3

Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.

Topic 4

Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

Topic 5

Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.

Topic 6

Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:

NVIDIA Generative AI LLMs Sample Questions (Q37-Q42):

NEW QUESTION # 37
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. Stratified k-fold cross-validation.
B. Grid search for hyperparameter tuning.
C. Single hold-out validation with a fixed test set.
D. Bootstrapping with random sampling.

Answer: A

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 # 38
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

A. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
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 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.

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 # 39
When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?

A. Reducing the input sequence length to minimize token processing.
B. Switching to a CPU-based inference engine for better scalability.
C. Increasing the model's parameter count to improve response quality.
D. Enabling dynamic batching to process multiple requests simultaneously.

Answer: D

Explanation:
NVIDIA Triton Inference Server is designed for high-performance model deployment, and dynamicbatching is a key optimization technique for reducing latency while maintaining high throughput in real-time applications like chatbots. Dynamic batching groups multiple inference requests into a single batch, leveraging GPU parallelism to process them simultaneously, thus reducing per-request latency. According to NVIDIA's Triton documentation, this is particularly effective for LLMs with variable input sizes, as it maximizes resource utilization. Option A is incorrect, as increasing parameters increases latency. Option C may reduce latency but sacrifices context and quality. Option D is false, as CPU-based inference is slower than GPU-based for LLMs.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 40
Which metric is commonly used to evaluate machine-translation models?

A. ROUGE score
B. Perplexity
C. F1 Score
D. BLEU score

Answer: D

Explanation:
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."

NEW QUESTION # 41
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. Cheaper computational costs during inference.
E. 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."

NEW QUESTION # 42
......

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