FREE PDF QUIZ NVIDIA - NEWEST NCA-GENM - NVIDIA GENERATIVE AI MULTIMODAL NEW APP SIMULATIONS

Free PDF Quiz NVIDIA - Newest NCA-GENM - NVIDIA Generative AI Multimodal New APP Simulations

Free PDF Quiz NVIDIA - Newest NCA-GENM - NVIDIA Generative AI Multimodal New APP Simulations

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Tags: NCA-GENM New APP Simulations, NCA-GENM Valid Exam Test, NCA-GENM Test Sample Questions, NCA-GENM Valid Test Sims, NCA-GENM Reliable Exam Syllabus

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Quiz 2025 NVIDIA NCA-GENM: NVIDIA Generative AI Multimodal – Efficient New APP Simulations

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NVIDIA Generative AI Multimodal Sample Questions (Q371-Q376):

NEW QUESTION # 371
Which of the following techniques can be used to improve the factual accuracy of text generated by a large language model?

  • A. Fine-tuning the model on a dataset of factually correct information.
  • B. Always using the same prompt, regardless of the desired output.
  • C. Increasing the model size and training it on more data.
  • D. Using retrieval-augmented generation (RAG) to ground the model's knowledge in external sources.
  • E. Applying a temperature of 0 during text generation.

Answer: A,C,D

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 # 372
You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)

  • A. Reduce the size of the text encoder.
  • B. Use a contrastive loss function that encourages alignment between text and audio representations.
  • C. Replace audio features with raw audio waveform.
  • D. Apply modality-specific dropout to the text encoder.
  • E. Increase the learning rate for the audio encoder.

Answer: B,D

Explanation:
Modality imbalance is a common issue in multimodal learning. Applying modality-specific dropout to the dominant modality (text, in this case) forces the model to rely more on the other modality (audio). A contrastive loss directly encourages the model to learn aligned representations between the two modalities. Increasing the audio encoder's learning rate (A) might help, but it is less targeted than dropout or contrastive loss. Reducing the text encoder size (D) is unlikely to be helpful in a controlled way. Replacing Audio features with raw waveform might introduce noise.


NEW QUESTION # 373
You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

  • A. Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.
  • B. Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'
  • C. Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
  • D. Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').
  • E. Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.

Answer: A,C,D,E

Explanation:
Few-shot prompting provides the LLM with examples to learn from, allowing it to better handle the nuances of informal language and errors. Chain-of-thought helps the model reason step-by-step, leading to better summaries. Negative constraints prevent irrelevant information. Template prompts provide structure and consistency. A zero-shot prompt is less effective in this scenario due to the complexity of the input data.


NEW QUESTION # 374
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 # 375
You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a 'cat' as a 'dog'. What is the MOST critical step to address this issue?

  • A. Apply image sharpening filters to the input images.
  • B. Increase the training data size with more diverse images.
  • C. Use a more complex model architecture.
  • D. Implement a mechanism to verify the generated descriptions against an external knowledge base or object recognition system.
  • E. Fine-tune the model using a smaller learning rate.

Answer: D

Explanation:
Factually incorrect descriptions indicate a lack of grounding in real-world knowledge. Verifying against an external knowledge base (B) directly addresses this issue. Increasing data size (A) might help, but it's not guaranteed. Fine-tuning (C) and increasing model complexity (D) might not solve the grounding problem. Image sharpening (E) is irrelevant to factual accuracy.


NEW QUESTION # 376
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