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NVIDIA Generative AI Multimodal Sample Questions (Q344-Q349):

NEW QUESTION # 344
You are working with a large multimodal dataset containing images and text. You want to efficiently load and preprocess this data for training a generative A1 model on an NVIDIA GPU. Which of the following approaches would be most effective for maximizing data loading speed and GPU utilization?

A. Employing NVIDIA's DALI (Data Loading Library) to perform data loading and preprocessing on the GPU.
B. Loading the entire dataset into CPU memory before starting training.
C. Storing the images and text in a relational database and querying the database during training.
D. Using a Python-based data loader that reads images and text directly from disk during training.
E. Compressing the dataset into a single large archive file and decompressing it on the fly during training.

Answer: A

Explanation:
NVIDIA DALI is specifically designed for accelerating data loading and preprocessing on NVIDIA GPUs. It allows you to perform tasks like image decoding, resizing, and data augmentation directly on the GPIJ, minimizing CPIJ overhead and maximizing GPU utilizatiom Loading the entire dataset into CPU memory is impractical for large datasets. Python-based data loaders can be slow due to the GIL (Global Interpreter Lock). Querying a relational database adds overhead. Compressing the dataset can save storage space but may introduce decompression bottlenecks during training.

NEW QUESTION # 345
You're building a multimodal model that takes images and text as input. You notice that your model is heavily biased towards the text modality, essentially ignoring the visual input. Which of the following strategies could you employ to address this modality imbalance? (Select TWO)

A. Increase the learning rate for the text encoder.
B. Implement a gating mechanism that dynamically adjusts the contribution of each modality based on the input.
C. Reduce the size of the visual encoder.
D. Remove the text modality entirely.
E. Use a modality-specific loss function, weighting the loss from the visual modality more heavily.

Answer: B,E

NEW QUESTION # 346
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)

A. SPICE (Semantic Propositional Image Caption Evaluation)
B. BLEU (Bilingual Evaluation Understudy)
C. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
D. Self-BLEU
E. CIDEr (Consensus-based Image Description Evaluation)

Answer: D

Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.

NEW QUESTION # 347
You're training a large language model (LLM) and notice that it struggles to maintain consistency and context over long passages of text. Which of the following architectural modifications would be most effective in addressing this issue?

A. Reducing the number of layers in the transformer architecture.
B. Increasing the maximum sequence length the model can process.
C. Using a smaller embedding dimension.
D. Increasing the size of the vocabulary.
E. Implementing a sparse attention mechanism to reduce computational cost

Answer: B

Explanation:
Increasing the maximum sequence length allows the model to consider a larger context window when generating text, improving its ability to maintain consistency and coherence over longer passages. Other options might address other issues, but increasing sequence length directly tackles the problem of limited context.

NEW QUESTION # 348
You are developing an Avatar Cloud Engine (ACE) application for a virtual assistant that needs to generate realistic facial expressions based on user emotions detected from text. Which ACE microservice would be most directly responsible for this functionality?

A. Text to Speech (TTS)
B. Natural Language Understanding (NLU)
C. speech to Text (STT)
D. Lip Sync
E. Facial Animation

Answer: E

Explanation:
The Facial Animation microservice within ACE is specifically designed to generate realistic facial expressions for avatars. While NLU detects the emotion, Facial Animation translates that emotion into corresponding facial movements.

NEW QUESTION # 349
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NVIDIA Generative AI Multimodal valid study torrent & NCA-GENM reliable study dumps & NVIDIA Generative AI Multimodal test practical information

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NVIDIA Generative AI Multimodal Sample Questions (Q344-Q349):

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