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NVIDIA Generative AI Multimodal Sample Questions (Q188-Q193):
NEW QUESTION # 188
You are building a multimodal model that takes video and audio as input. You want to fuse the information extracted from both modalities. Which of the following fusion techniques allows for learning temporal dependencies between modalities?
Answer: A
Explanation:
Attention-based Fusion, particularly using Transformers, is well-suited for capturing temporal dependencies in multimodal data. Transformers can learn which parts of each modality are most relevant at different points in time, enabling a more nuanced fusion of information. Early Fusion (A) fuses features statically and doesn't capture temporal dependencies directly. Late Fusion (B) also struggles to capture fine- grained temporal relationships. Simple addition (D) and max pooling (E) are too simplistic to model complex temporal interactions.
NEW QUESTION # 189
You're building a system that uses a pre-trained large language model (LLM) for generating creative stories. After deploying the system, you notice that the generated stories often contain biases present in the training data of the LLM. What are the MOST effective strategies to mitigate these biases in your generated stories? (Select TWO)
Answer: A,B
Explanation:
Bias detection and mitigation techniques can be applied to the LLM's output to identify and remove or modify biased content. Prompt engineering involves carefully crafting prompts to guide the LLM towards generating more balanced and unbiased stories. Fine-tuning on a diverse dataset is beneficial in the long run but is a more resource-intensive approach and doesn't guarantee the complete elimination of biases. Increasing the temperature parameter might lead to more creative but also more unpredictable and potentially biased outputs. Reducing the size of the LLM doesn't address the bias problem and might even worsen it by limiting the model's ability to capture nuanced relationships.
NEW QUESTION # 190
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?
Answer: E
Explanation:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.
NEW QUESTION # 191
Which of the following are potential benefits of using multi-modal learning compared to single-modal learning? (Select all that apply)
Answer: A,B,E
Explanation:
Multi-modal learning leverages the complementary information from different modalities to enhance performance. (A) It improves robustness because if one modality is noisy or missing, the others can still provide useful information. (B) It learns more comprehensive representations by integrating information across modalities. (D) It reduces overfitting by leveraging information from multiple sources. (C) is correct but not a benefit. (E) is incorrect as higher accuracy is not guaranteed, depending on data and task.
NEW QUESTION # 192
When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?
Answer: C
Explanation:
Diverse beam search or sampling strategies encourage the model to explore different caption possibilities during inference, leading to more diverse and informative captions. Standard beam search often converges to the most likely caption, which tends to be generic. Increasing the image encoder Size might improve image feature extraction but doesn't directly address the caption informativeness problem. Decreasing the learning rate is a general training technique that might improve convergence but doesn't specifically target caption informativeness.
NEW QUESTION # 193
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