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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?

A) To optimize search algorithms for faster data retrieval.
B) To classify and categorize data based on patterns and features.
C) To optimize decision-making processes based on historical data.
D) To produce new data that is similar to the training data.

2. In large-language models, what is the purpose of the attention mechanism?

A) To measure the importance of the words in the output sequence.
B) To capture the order of the words in the input sequence.
C) To assign weights to each word in the input sequence.
D) To determine the order in which words are generated.

3. What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.

A) 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.
B) 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.
C) 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.
D) Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
E) 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.

4. What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?

A) CLIP is used to convert textual input into image embeddings.
B) CLIP is used to enhance datasets through data augmentation for text-to-image generation.
C) CLIP is used to generate image captions from textual input.
D) CLIP provides a common embedding space for both the textual and image modalities.

5. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?

A) K-Means Clustering
B) Convolutional Neural Network (CNN)
C) Linear Regression
D) Logistic Regression

Solutions: