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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?

A) Decision Trees
B) K-Means Clustering
C) Convolutional Neural Networks (CNN)
D) Linear Regression

2. Which framework is used for conversational AI models development?

A) NVIDIA Clara
B) NVIDIA Metropolis
C) NVIDIA DeepStream
D) NVIDIA NeMo

3. What are some methods to overcome limited throughput between CPU and GPU?

A) Increase the clock speed of the CPU.
B) Upgrade the GPU to a higher-end model.
C) Increase the number of CPU cores.
D) Using techniques like memory pooling.

4. For building a zero-shot image classification pipeline, what could be a crucial step in the process?

A) Manually labeling each image in the dataset for precise classification.
B) Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.
C) Designing an algorithm to replace the need for textual descriptions in the classification process.
D) Focusing on enhancing the resolution and quality of images before classification.

5. In multimodal machine learning, what does 'early fusion' refer to?

A) Ignoring certain modalities and only using one modality for analysis and prediction.
B) Training separate models for each modality and then combining their predictions.
C) Integrating different modalities at the beginning of the model pipeline.
D) Implementing the model in the early stages of development of the ML solution.

Solutions: