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NVIDIA Generative AI Multimodal Sample Questions (Q127-Q132):
NEW QUESTION # 127
You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?
Answer: E
Explanation:
The best approach is to use a multimodal SHAP implementation that considers the interaction between image and text features. This ensures a holistic explanation of the model's prediction based on both modalities. Treating them separately or simply concatenating features ignores potential synergistic effects.
NEW QUESTION # 128
You are working with a large dataset of images to train a Generative A1 model. You suspect that some images are corrupted or of poor quality, which could negatively impact training. Which of the following methods would be the MOST effective in identifying and removing these problematic images?
Answer: A,B,D
Explanation:
Checking file integrity to remove corruption images is an important first step. Computing Image Sharpness is an effective way to programmatically identify and filter blur or out-of-focus Images. Using pre-trained image assessment models is another advanced and automativ approach to identifying and removing images of low quality, even if they are not overtly corrupted. Manually checking would take too long. Average pixel intensity is often useful to filter.
NEW QUESTION # 129
Which of the following NVIDIA SDKs is most suitable for deploying a real-time, low-latency speech-to-text service as part of a multimodal AI application?
Answer: E
Explanation:
NVIDIA Riva is specifically designed for building and deploying real-time conversational A1 services, including speech-to-text, text-to- speech, and natural language understanding. Its focus is on low latency and high accuracy for conversational applications.
NEW QUESTION # 130
You're developing a real-time video captioning system. Latency is critical. Which of the following optimization strategies would provide the MOST significant reduction in end-to-end latency, assuming the captioning model is already optimized for inference?
Answer: D
Explanation:
While all listed strategies contribute to reducing latency, Option C - overlapping image feature extraction and caption generation - offers a substantial reduction by parallelizing these typically sequential processes. It leverages asynchronous execution to minimize idle time. Smaller batch sizes (A) and Model Quantization (B) would directly improve model inference. Caching frequent words (D) could reduce lookup time during caption generation. However, the overlapping approach provides a more significant change by improving concurrency. E is a combination of all of the above which leads to best optimized model.
NEW QUESTION # 131
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 # 132
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