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NVIDIA Generative AI Multimodal Sample Questions (Q172-Q177):

NEW QUESTION # 172
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: D

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 # 173
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model requires significant GPU memory and you want to maximize throughput. Which Triton configuration parameters would be MOST important to tune, and why?

A. 'optimization' (setting strategy to TRT to enable TensorRT optimization) and 'input_shape' (specifying the exact input shape).
B. Both B and C.
C. 'dynamic_batching' (enabling it and setting and 'model_warmup' (specifying dummy inputs to pre-load the model).
D. 'instance_group' (setting count to the number of available GPUs) and (setting a high value to accumulate requests).
E. 'instance_group' (setting count to the number of available GPUs and kind to KIND_GPU) and (increasing it to the largest value that fits in GPU memory).

Answer: B

Explanation:
'instance_group' with 'KIND_GPIY assigns the model to specific GPUs. Increasing (B) leverages GPU parallelism. Enabling 'dynamic_batching' and setting (C) allows Triton to dynamically batch requests to maximize throughput. Model warmup reduces first request latency. (A) is incomplete (missing KIND_GPU). (D) is relevant for latency optimization but not as crucial for throughput in a memory-constrained scenario. Therefore both B and C are most crucial in optimizing throughput while dealing with memory constraint.

NEW QUESTION # 174
You are building a multimodal model to generate realistic dialogues between virtual characters in a game. The model takes as input the current game state (including character positions, objects, and environment), the character's personality profile (text), and the previous dialogue utterances (text and audio). What specific techniques can you employ to ensure that the generated dialogues are contextually relevant, coherent, and emotionally appropriate?

A. Incorporate attention mechanisms that allow the model to selectively focus on the most relevant aspects of the game state and character personality profile.
B. Use reinforcement learning to train the model to maximize a reward function that reflects the desired dialogue characteristics (e.g., coherence, emotional appropriateness).
C. All of the above. Except D
D. Train each mode separately to achieve the best result and them merge at the end.
E. Implement a hierarchical dialogue generation architecture that first plans the overall dialogue structure and then generates individual utterances.

Answer: C

Explanation:
Reinforcement learning optimizes the dialogue for desired characteristics, attention mechanisms focus on relevant context, and hierarchical architecture improves coherence. Training each model separately is not a multimodal approach.

NEW QUESTION # 175
You're building a generative A1 model that can create realistic 3D models from text descriptions. You have a dataset of text descriptions and corresponding 3D models, but the alignment between the text and the 3D models is weak. The model sometimes generates 3D shapes that don't accurately reflect the text. Which of the following techniques could improve the alignment between the text descriptions and the generated 3D models?

A. Using a contrastive loss function that encourages the model to generate 3D models that are semantically similar to the corresponding text descriptions.
B. Applying data augmentation techniques to the 3D models (e.g., random rotations and scaling).
C. Training the model with a larger batch size.
D. Using a pre-trained text encoder (e.g., BERT or CLIP) to extract meaningful features from the text descriptions.
E. Increasing the number of vertices and faces in the 3D models.

Answer: A,D

Explanation:
A contrastive loss function directly encourages the model to learn a mapping between text and 3D models that preserves semantic similarity. Using a pre-trained text encoder allows the model to leverage existing knowledge about language and extract more meaningful features from the text descriptions, improving alignment. Increasing the number of vertices and faces can improve the resolution of the models but won't directly address alignment. 3D data augmentation can improve robustness, but it's less direct. Batch size has a smaller impact compared to the other options.

NEW QUESTION # 176
You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?

A. Specifying 'oil painting' or another artistic style to mask the lack of detail.
B. Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
C. Using completely unrelated keywords to encourage the model to create something unique.
D. Using a very short and general prompt to allow the model more freedom.
E. Decreasing the 'guidance_scale' to allow for more creative freedom.

Answer: B

Explanation:
Adding keywords specifically related to image quality ('photorealistic', 'high resolution', '8k', 'detailed') helps guide the model towards generating sharper and more detailed images. 'clip_skip' influences the model to incorporate more details into the output images. Adjusting 'clip_skip' along with quality prompt keywords will enhance the image quality.

NEW QUESTION # 177
......

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TRUSTWORTHY NCA-GENM PRACTICE & NCA-GENM EXAM COURSE

Trustworthy NCA-GENM Practice & NCA-GENM Exam Course