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NVIDIA Generative AI Multimodal Sample Questions (Q241-Q246):

NEW QUESTION # 241
You are building a system to generate captions for images. You want to evaluate how well the generated captions describe the content of the images. Which of the following metrics are most suitable for evaluating the quality of image captions?

A. ROUGE (Recall-Oriented Understudy for Gisting Evaluation).
B. BLEU (Bilingual Evaluation Understudy).
C. Pixel Accuracy.
D. F 1-Score
E. Inception Score.

Answer: A,B

Explanation:
BLEU and ROUGE are standard metrics for evaluating the quality of generated text, especially in the context of machine translation and text summarization. BLEU measures the precision of n-grams in the generated text compared to reference texts, while ROUGE measures the recall. Pixel accuracy and Inception Score are more relevant for image classification and image generation tasks, respectively. F1-Score could be used if you manually labeled different image aspects of the caption.

NEW QUESTION # 242
You are tasked with building a Generative A1 model that can generate realistic images of birds based on text descriptions. You have a large dataset of bird images and corresponding text captions. Which of the following architectures is MOST suitable for this task, considering both image quality and training efficiency?

A. A standard Convolutional Neural Network (CNN) for image generation.
B. A Variational Autoencoder (VAE) trained on the image dataset.
C. A Generative Adversarial Network (GAN) conditioned on the text descriptions (e.g., a StackGAN or AttnGAN).
D. A simple Recurrent Neural Network (RNN) to generate pixel values sequentially.
E. An Image Transformer model trained from scratch.

Answer: C

Explanation:
GANs, especially those conditioned on text descriptions, are specifically designed for generating realistic images based on textual input. StackGAN and AttnGAN are examples that have shown good performance. CNNs and VAEs are less effective for high-quality image generation from text. RNNs are not well-suited for generating images pixel by pixel. Image transformers are possible, but computationally expensive for training from scratch on a large dataset like this, although diffusion models based on transformers are becoming more popular

NEW QUESTION # 243
You are training a multimodal Generative A1 model for generating video captions. The model is overfitting to the training data, resulting in poor generalization to unseen videos. Which of the following regularization techniques would be MOST suitable to mitigate overfitting?

A. Batch normalization
B. Early stopping based on a validation set
C. Data augmentation (e.g., adding noise to the video frames)
D. L1 regularization on the model weights
E. Dropout

Answer: B,C,D,E

Explanation:
Dropout randomly deactivates neurons during training, preventing the model from relying on specific neurons. L1 regularization adds a penalty to the loss function based on the absolute values of the weights, encouraging sparsity. Data augmentation increases the diversity of the training data. Early stopping prevents the model from training for too long and overfitting. Batch normalization primarily improves training stability and speed but also provides some regularization.

NEW QUESTION # 244
You are building a real-time multimodal application that requires processing both audio and video streams simultaneously. You need to minimize the latency of the system while maximizing throughput. Which of the following hardware and software optimizations would be most effective?

A. Compressing the audio and video streams aggressively to reduce the amount of data that needs to be processed.
B. Using a high-latency, high-bandwidth network connection.
C. Using separate GPUs for audio and video processing and employing asynchronous data transfer techniques.
D. Using a CPU-based implementation for both audio and video processing.
E. Offloading both audio and video processing to a single high-end GPIJ.

Answer: C

Explanation:
Using separate GPUs allows for parallel processing of audio and video streams. Asynchronous data transfer techniques minimize latency by allowing the CPU to continue processing other tasks while data is being transferred to the GPUs. While a single high-end GPU could handle both tasks, using separate GPUs maximizes parallelism. CPU-based implementations are generally slower than GPU-based implementations for multimedia processing. Aggressive compression can reduce data size but may also introduce artifacts and reduce the quality of the output. High-latency networks are detrimental to real-time applications.

NEW QUESTION # 245
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. 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.
C. Use the features of the face data as an attention mechanism to pay attention to the audio, in an end-to-end learning model.
D. Train the Audio model first, then train the Face model to recognize emotions based on the results of the audio expression emotions.
E. 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.

Answer: C,E

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 # 246
......

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NCA-GENM Certification Exam Cost, Practice NCA-GENM Exam Fee

Practice NCA-GENM Exam Fee, NCA-GENM Valid Exam Pass4sure

NVIDIA Generative AI Multimodal Sample Questions (Q241-Q246):

NVIDIA NCA-GENM Certification Exam Cost: NVIDIA Generative AI Multimodal - DumpsActual Fast Download

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