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NEW QUESTION # 164
Which of the following techniques can be used to improve the factual accuracy of text generated by a large language model?
Answer: A,D,E
Explanation:
Increasing model size and training data can improve factual accuracy, but it's not a guaranteed solution. Fine-tuning on factually correct data directly teaches the model to generate accurate information. RAG allows the model to access external knowledge sources and incorporate them into the generated text, which significantly improves factual accuracy. A temperature of 0 makes the model more deterministic but doesn't guarantee accuracy. Varying prompts is important for exploring the model's capabilities, but it doesn't directly address factual accuracy.
NEW QUESTION # 165
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 # 166
You are fine-tuning a pre-trained multimodal model for a specific task that involves generating short video clips from text prompts. The pre-trained model was trained on a large dataset of diverse videos and text descriptions. However, you observe that the fine-tuned model tends to generate video clips that are visually appealing but often deviate significantly from the meaning of the text prompts. Which of the following techniques is LEAST likely to improve the semantic consistency between the generated video clips and the text prompts?
Answer: B
Explanation:
Freezing the weights of the video encoder will prevent it from adapting to the specific nuances of the fine-tuning task, potentially hindering the model's ability to generate videos that accurately reflect the meaning of the text prompts. A lower learning rate, reinforcement learning, data augmentation, or contrastive learning are all techniques that can help improve semantic consistency.
NEW QUESTION # 167
You have a multimodal model that processes images and text, and you want to deploy it on an edge device with limited computational resources. Which of the following hardware acceleration strategies would be MOST effective in improving the model's inference speed on the edge device?
Answer: B,D
Explanation:
NVIDIA TensorRT is specifically designed to optimize models for NVIDIA GPUs, which are commonly found in edge devices. Converting to a smaller architecture reduces the computational burden on the edge device. Distributed inference is complex to setup and cloud offloading defeats the purpose of edge deployments. A larger batch size requires more memory, which can be limiting on edge devices. Accepting a lower accuracy can improve inference, which can be acceptable for certain edge deployments
NEW QUESTION # 168
You are tasked with building a Generative A1 model to generate realistic images of outdoor scenes. The training dataset contains a large number of images with varying lighting conditions, weather conditions, and object compositions. Which data augmentation techniques would be MOST effective in improving the model's robustness and generalization ability?
Answer: C
Explanation:
Color jittering, adding Gaussian noise, and random perspective transformations simulate variations in lighting, noise, and camera angles, making the model more robust to different real-world scenarios. Random cropping and resizing are also helpful, but less impactful than changes in the color space and noise. Horizontal flipping can be useful but is limited. A fixed rotation or Vertical flipping is not a general augmentation and might harm the model's performance.
NEW QUESTION # 169
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