NVIDIA NCA-GENM Vce Exam | New NCA-GENM Exam Camp

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NVIDIA Generative AI Multimodal Sample Questions (Q35-Q40):

NEW QUESTION # 35
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

A. Using a simple Multi-Layer Perceptron (MLP) as the generator.
B. Using a more powerful discriminator architecture (e.g., with attention mechanisms).
C. Using a deeper and wider generator network (e.g., with more layers and channels).
D. Reducing the size of the input noise vector to the generator.
E. Implementing spectral normalization in both the generator and discriminator.

Answer: C,E

Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between real and fake images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.

NEW QUESTION # 36
Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

Answer: B

Explanation:
The correct code uses ' AutoModelForSeq2SeqLM' because BLIP (used in the example) is a sequence-to-sequence model. The processor correctly handles the image and text, and 'model.generate' produces the answer which is then decoded. 'AutoModelForQuestionAnswering' is not a generic class and won't work correctly with BLIP without additional adaptation.

NEW QUESTION # 37
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. 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.
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. Train the Audio model first, then train the Face model to recognize emotions based on the results of the audio expression emotions.
D. Use the features of the face data as an attention mechanism to pay attention to the audio, in an end-to-end learning model.
E. Train the face model first, then train the audio model to recognize emotions based on the results of the facial expression emotions.

Answer: A,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 # 38
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task using LoRA (Low-Rank Adaptation).
Which of the following statements accurately describes the benefits and limitations of using LoRA?

A. LoRA can improve the accuracy of the fine-tuned model compared to full fine-tuning by preventing overfitting.
B. A and B.
C. LoRA is not compatible with model parallelism techniques.
D. LoRA allows for efficient task switching by only storing and loading the small LoRA parameters for different tasks, while keeping the original LLM weights frozen.
E. LoRA reduces the number of trainable parameters by inserting low-rank matrices into the original model layers, making fine-tuning more memory-efficient.

Answer: B

Explanation:
LoRA significantly reduces the number of trainable parameters, enabling more memory-efficient fine-tuning, especially for large models. It also facilitates efficient task switching as only the small LoRA parameters need to be stored and loaded for different tasks. While LoRA can help prevent overfitting compared to full fine-tuning, it doesn't guarantee improved accuracy. LoRA can be effectively combined with model parallelism.

NEW QUESTION # 39
Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?

A. Chi-squared test
B. Krippendorff's Alpha
C. Pearson correlation coefficient
D. Spearman's rank correlation coefficient
E. Cohen's Kappa

Answer: B

Explanation:
Krippendorff's Alpha is the most appropriate measure when dealing with multiple annotators, ordinal data (severity scores on a scale), and potential for missing data. While Cohen's Kappa is suitable for two annotators, Krippendorff's Alpha generalizes to multiple annotators. Spearman's rank correlation is better suited for assessing the monotonic relationship between variables, but not agreement among raters. Pearson correlation requires interval data, while chi-squared is for categorical.

NEW QUESTION # 40
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NVIDIA NCA-GENM Vce Exam | New NCA-GENM Exam Camp

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NVIDIA Generative AI Multimodal Sample Questions (Q35-Q40):

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