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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Generative AI Concepts | - Generative models
|
| Topic 2: Multimodal AI Systems | - Cross-modal learning
|
| Topic 3: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Topic 4: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 5: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
In a multimodal machine learning context, how are different modalities usually linked to each other?
A. Different modalities are linked through separate models that are ensembled by tree-based models.
B. Different modalities are not linked to each other in a multimodal machine learning context.
C. Different modalities are linked through random connections.
D. Different modalities are linked through a shared representation that captures the relationships between the modalities.
Question 2
Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?
A. Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
B. More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
C. Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
D. Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.
Question 3
Which metric is commonly used to evaluate machine-translation models?
A. Mean Absolute Error (MAE)
B. Accuracy
C. F1 score
D. BLEU score
Question 4
In large-language models, what is the purpose of the attention mechanism?
A. To capture the order of the words in the input sequence.
B. To measure the importance of the words in the output sequence.
C. To determine the order in which words are generated.
D. To assign weights to each word in the input sequence.
Question 5
Which of the following best describes the role of the Hugging Face model repository in ML software development?
A. A platform for sharing and accessing pre-trained models and transformers for natural language processing.
B. A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
C. A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
D. A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
Solutions:
| Question 1 Answer: D | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: A |
