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| Section | Objectives |
|---|---|
| Topic 1: Generative AI Concepts | - Generative models
|
| Topic 2: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 3: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Topic 4: Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Topic 5: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
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NEW QUESTION # 38
Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:
Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)
Answer: B,E
Explanation:
torch.cat concatenates the embeddings along dimension 1, resulting in shape (32, 1024). Element-wise addition maintains the original shape (32, 512). The weighted sum is also element-wise, preserving the (32, 512) shape.
NEW QUESTION # 39
A multimodal A1 model is designed to translate sign language videos into text. The model performs well on videos with clear hand gestures and lighting conditions but struggles with videos recorded in low light or with partial hand occlusions. Which of the following strategies would be MOST effective in improving the model's robustness to these challenging conditions?
Answer: B
Explanation:
Applying image enhancement techniques to the video frames can improve the visibility of hand gestures in low-light conditions and reduce the impact of noise, making the model more robust. Reducing the frame rate or training on a smaller dataset would likely decrease performance. Increasing the text vocabulary or using a simpler text encoder would not directly address the issue of poor video quality.
NEW QUESTION # 40
You're using a pre-trained multimodal model that combines visual and textual information for a new downstream task: generating marketing slogans for product images. The model performs poorly, generating generic slogans that are unrelated to the specific product features. What is the MOST effective strategy to adapt this pre-trained model to your specific task?
Answer: E
Explanation:
Fine-tuning the entire pre-trained model (B) allows the model to learn the specific nuances of the new task while leveraging the knowledge it gained during pre-training. Replacing only the output layer (A) might not be sufficient. Freezing the pre-trained model (C) limits its ability to adapt to the new task. Only fine-tuning the visual encoder (D) might not address the language generation aspect. Using the model without adaptation (E) will likely result in poor performance.
NEW QUESTION # 41
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,C
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 # 42
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?
Answer: D
Explanation:
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.
NEW QUESTION # 43
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