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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
| Topic 2: Multimodal AI Systems | - Cross-modal learning
|
| Topic 3: Generative AI Concepts | - Generative models
|
| Topic 4: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 5: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
NVIDIA Generative AI Multimodal Sample Questions:
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. Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
- B. More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
- C. Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
- 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.
Correct Answer: C 🗳️
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What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.
- A. Contrastive learning is a technique used to train deep learning models by comparing similar and dissimilar inputs and optimizing the model to maximize the similarity between representations of similar inputs and minimize the similarity between representations of dissimilar inputs.
- B. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
- C. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.
- D. In a multimodal context, usually, contrastive learning decreases the similarity of representations across modalities for the same objects and increases the similarity of representations across modalities for different objects.
- E. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.
Correct Answer: A,E 🗳️
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Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?
- A. CTC Loss
- B. Mean Opinion Score (MOS)
- C. Word Error Rate (WER)
- D. F1 Score
Correct Answer: C 🗳️
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What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?
- A. To generate new images from pure noise.
- B. To segment noisy patches in input images.
- C. To detect noisy objects in input images.
- D. To classify input images as noisy or clean.
Correct Answer: A 🗳️
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In machine learning, what is the purpose of data normalization?
- A. To reduce the dimensionality of the dataset.
- B. To convert data into a specific format for easier analysis.
- C. To increase the complexity of the dataset.
- D. To remove irrelevant data from the dataset.
Correct Answer: B 🗳️
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