NCA-GENM Guaranteed Questions Answers & Latest NCA-GENM Exam Format

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NVIDIA Generative AI Multimodal Sample Questions (Q341-Q346):

NEW QUESTION # 341
You are building a multimodal emotion recognition system that uses facial expressions (images) and speech (audio). You want to use transfer learning to leverage pre-trained models for both modalities. You have access to a large pre-trained facial recognition model (trained on millions of faces) and a large pre-trained speech recognition model (trained on thousands of hours of speech). How do you design a multimodal transfer learning strategy to efficiently train the entire system on a smaller dataset of peoples face and audio samples?

A. Train the face model first, then train the audio model to recognize emotions based on the results of the facial expression emotions.
B. Use the features of the face data as an attention mechanism to pay attention to the audio, in an end-to-end learning model.
C. Extract features separately using each of the pre-trained face and speech models and then train a separate classifier model, combining those features to recognize emotion.
D. Fine-tune each of the pre-trained models for the emotion recognition task using a joint loss function that combines the outputs of face emotion and speech emotion to create an overall expression.
E. Train the Audio model first, then train the Face model to recognize emotions based on the results of the audio expression emotions.

Answer: B,D

Explanation:
Fine-tuning the pre-trained models using a joint loss function helps the model to adapt to a combined face and speech emotion recognition task. In addition, using the features of one modality as an attention mechanism for the other modality can help guide an end-to-end training model. Feature extraction is more of a traditional method and does not fully allow pre-trained models to fully transfer. Training in series might not result in the best model performance since multimodal emotion recognition is about using all facets of information to predict.

NEW QUESTION # 342
Which of the following Python code snippets correctly demonstrates how to load pre-trained word embeddings (e.g., GloVe or Word2Vec) using spaCy and then calculate the cosine similarity between two words?

Answer: B,C,D,E

Explanation:
Option A loads a small spacy model without word vectors. Option B loads the large spacy model with word vectors correctly, and calculates the similarity. Option C correctly loads word embeddings from a text file and uses cosine_similarity from sklearn.metrics.pairwise to get similarity Option D shows word similarity and usage of the gensim model.

NEW QUESTION # 343
You are working with a large multimodal dataset containing images and text. You want to efficiently load and preprocess this data for training a generative A1 model on an NVIDIA GPU. Which of the following approaches would be most effective for maximizing data loading speed and GPU utilization?

A. Storing the images and text in a relational database and querying the database during training.
B. Using a Python-based data loader that reads images and text directly from disk during training.
C. Employing NVIDIA's DALI (Data Loading Library) to perform data loading and preprocessing on the GPU.
D. Loading the entire dataset into CPU memory before starting training.
E. Compressing the dataset into a single large archive file and decompressing it on the fly during training.

Answer: C

Explanation:
NVIDIA DALI is specifically designed for accelerating data loading and preprocessing on NVIDIA GPUs. It allows you to perform tasks like image decoding, resizing, and data augmentation directly on the GPIJ, minimizing CPIJ overhead and maximizing GPU utilizatiom Loading the entire dataset into CPU memory is impractical for large datasets. Python-based data loaders can be slow due to the GIL (Global Interpreter Lock). Querying a relational database adds overhead. Compressing the dataset can save storage space but may introduce decompression bottlenecks during training.

NEW QUESTION # 344
You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

A. Reduce the number of inference steps.
B. Increase the number of inference steps.
C. Use a negative prompt to exclude unwanted elements or styles.
D. Decrease the CFG (Classifier-Free Guidance) scale.
E. Change the random seed to explore different variations.

Answer: B,C,E

Explanation:
Increasing the number of inference steps allows the diffusion process to refine the image more thoroughly. Using a negative prompt helps to guide the generation process by specifying what not to include in the image. Changing the random seed allows you to explore different variations of the same prompt, which can lead to more desirable results. Decreasing the CFG scale can reduce adherence to the prompt, and reducing the number of inference steps results in less refined images.

NEW QUESTION # 345
You are working with a multimodal dataset that contains images and corresponding captions. You want to use contrastive learning to learn joint embeddings for images and text. Which of the following loss functions is the most suitable for this task?

A. Negative Log Likelihood (NLL) loss
B. Triplet loss
C. Cross-entropy loss
D. Mean Squared Error (MSE) loss
E. Binary Cross-entropy loss

Answer: B

Explanation:
Triplet loss is specifically designed for contrastive learning, where the goal is to learn embeddings such that similar pairs are closer in the embedding space than dissimilar pairs- Cross-entropy and binary cross-entropy are classification losses. MSE loss is a regression loss- NLL loss is often used with sequence models but doesn't directly address contrastive learning goals.

NEW QUESTION # 346
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NCA-GENM Guaranteed Questions Answers & Latest NCA-GENM Exam Format

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NVIDIA Generative AI Multimodal Sample Questions (Q341-Q346):

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