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Free PDF Quiz 2025 NCA-GENM: NVIDIA Generative AI Multimodal Latest Valid Dumps

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NVIDIA Generative AI Multimodal Sample Questions (Q20-Q25):

NEW QUESTION # 20
You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

A. Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.
B. Deploying Riva on a single GPU with a large batch size.
C. Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.
D. Deploying Riva on a single CPU core with a small batch size.
E. Deploying Riva on CPU using multiprocessing.

Answer: C

Explanation:
For high-throughput, low-latency applications, deploying Riva across multiple GPUs using Triton Inference Server is optimal. Triton enables dynamic batching, which groups incoming requests to maximize GPU utilization, and allows for scaling across multiple GPUs to handle increased load. Riva leverages gRPC to communicate with Triton.

NEW QUESTION # 21
You are building a real-time multimodal application that requires processing both audio and video streams simultaneously. You need to minimize the latency of the system while maximizing throughput. Which of the following hardware and software optimizations would be most effective?

A. Offloading both audio and video processing to a single high-end GPIJ.
B. Compressing the audio and video streams aggressively to reduce the amount of data that needs to be processed.
C. Using separate GPUs for audio and video processing and employing asynchronous data transfer techniques.
D. Using a CPU-based implementation for both audio and video processing.
E. Using a high-latency, high-bandwidth network connection.

Answer: C

Explanation:
Using separate GPUs allows for parallel processing of audio and video streams. Asynchronous data transfer techniques minimize latency by allowing the CPU to continue processing other tasks while data is being transferred to the GPUs. While a single high-end GPU could handle both tasks, using separate GPUs maximizes parallelism. CPU-based implementations are generally slower than GPU-based implementations for multimedia processing. Aggressive compression can reduce data size but may also introduce artifacts and reduce the quality of the output. High-latency networks are detrimental to real-time applications.

NEW QUESTION # 22
You are working on a project that involves generating music from video. The approach uses a pre-trained video encoder and a pre- trained music decoder. You find that the generated music often lacks a clear connection to the visual content of the video. To improve the coherence between the video and the generated music, which of the following steps would be the MOST effective? (Select TWO)

A. Only use videos that are shorter than 5 seconds.
B. Fine-tune the entire system end-to-end with a loss function that encourages temporal alignment between video and music features.
C. Introduce a cross-modal attention mechanism to allow the music decoder to attend to relevant visual features during music generation.
D. Train the video encoder and music decoder separately on larger datasets.
E. Remove the video encoder and generate music directly from random noise.

Answer: B,C

Explanation:
Cross-modal attention (B) allows the model to explicitly learn relationships between visual and musical features. End-to-end fine- tuning with a suitable loss function (D) encourages the model to learn a coherent mapping between the two modalities. Training the encoders and decoders separately won't necessarily improve coherence. Generating from random noise defeats the purpose. Limiting video length may reduce complexity but doesn't fundamentally address the alignment problem.

NEW QUESTION # 23
A financial institution aims to detect fraudulent transactions by analyzing transaction history (time-series), customer profiles (text and numerical data), and network activity (graph data). The system must identify fraudulent patterns in real-time. Which of the following architectural patterns is MOST suitable for building this multimodal fraud detection system, considering both accuracy and latency requirements?

A. An architecture based solely on rule-based systems to minimize latency, ignoring the potential for machine learning to detect complex fraud patterns.
B. A real-time stream processing architecture that uses a combination of rule-based systems and machine learning models to detect fraudulent transactions as they occur.
C. A hybrid approach that combines real-time stream processing for initial detection and batch processing for periodic model retraining and refinement.
D. A batch processing pipeline that aggregates data daily and trains a machine learning model to identify fraudulent transactions.
E. Train three seperate models for each data set, transaction history, customer profile, and network activitiy.

Answer: C

Explanation:
A hybrid approach is best. Real-time stream processing enables immediate detection, while batch processing allows for more sophisticated analysis and model updates, improving accuracy over time. Batch processing alone is too slow for real-time fraud detection, and rule- based systems are less adaptable to evolving fraud patterns.

NEW QUESTION # 24
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 # 25
......

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