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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following tasks can be performed using the transformer LLM encoder model?

A) Image generation
B) Generating code
C) Speech recognition
D) Semantic analysis

2. Which metric is commonly used to evaluate machine-translation models?

A) BLEU score
B) Accuracy
C) F1 score
D) Mean Absolute Error (MAE)

3. 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) 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.
B) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
C) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
D) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.

4. Which of the following best describes the role of the Hugging Face model repository in ML software development?

A) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
B) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
C) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
D) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.

5. What role does 'late fusion' play in multimodal machine learning?

A) It refers to the process of combining multiple modalities at the training stage.
B) It refers to the process of combining multiple modalities at the preprocessing stage.
C) It refers to the process of combining multiple modalities at the decision level.
D) It refers to the process of combining multiple modalities at the feature level.

Solutions: