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NCA-GENM Test Lab Questions, NCA-GENM Latest Practice Materials

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NVIDIA Generative AI Multimodal Sample Questions (Q366-Q371):

NEW QUESTION # 366
Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A. Using a greedy decoding strategy.
B. Increasing the model's training data size.
C. Utilizing a temperature scaling parameter during decoding and increasing its value.
D. Employing a nucleus sampling (top-p sampling) decoding strategy.
E. Using a beam search decoding strategy with a small beam width.

Answer: C,D

Explanation:
Nucleus sampling (top-p sampling) randomly samples from the smallest set of words whose cumulative probability mass exceeds a threshold p, encouraging more diverse outputs. Increasing the temperature scaling parameter makes the probability distribution flatter, leading to more exploration and less predictable (more creative) outputs. Beam search with a small beam width might still result in repetitive outputs. Increasing the training data size can help, but it might not directly address the lack of diversity. Greedy decoding always selects the most probable word, leading to repetitive and predictable outputs.

NEW QUESTION # 367
You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A. Model size on disk.
B. Inference latency (time per request).
C. BLEU score (or similar text generation metric) for generated descriptions.
D. GPU utilization.
E. Number of lines of code in the model.

Answer: B,C

Explanation:
Inference latency directly impacts the user experience and resource utilization. A high latency indicates potential bottlenecks. The BLEU score (or similar metric) measures the quality of the generated text, ensuring that the model is producing accurate and relevant descriptions. GPU utilization is important but secondary compared to latency and quality. Model size and lines of code are not direct indicators of runtime performance or output quality.

NEW QUESTION # 368
You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?

A. Incorporating positional encodings and increasing the attention window size.
B. Using a smaller embedding dimension.
C. Reducing the number of layers in the Transformer.
D. Using only audio as input.
E. Removing the self-attention mechanism.

Answer: A

Explanation:
Positional encodings help the Transformer understand the order of words in the sequence, which is crucial for maintaining coherence. Increasing the attention window size allows the model to attend to a larger context when generating each word, enabling it to capture longer-range dependencies. Reducing layers or embedding dimension would likely worsen the problem. Removing self-attention would defeat the purpose of using a Transformer. Positional encodings and attention window size are key to transformer performance with respect to long range dependencies.

NEW QUESTION # 369
You're working with a client to develop a generative A1 model for creating personalized marketing content. During requirements acquisition, the client expresses a desire for 'highly creative' and 'unique' outputs. However, they struggle to articulate specific aesthetic preferences. How would you best approach translating these subjective requirements into concrete model training and prompt engineering strategies?

A. Conduct extensive A/B testing with a large user group, presenting them with various model outputs and gathering feedback on which content they perceive as most 'creative' and 'unique'. Use this feedback to refine the model and prompts.
B. Use a pre-trained style transfer model to apply different artistic styles to the generated content, offering the client a diverse range of options to choose from and identify their preferred aesthetic.
C. Implement a system for interactive prompt refinement, allowing the client to iteratively modify prompts and observe the resulting outputs in real-time, facilitating a collaborative exploration of the model's creative potential.
D. B and D
E. Focus solely on quantitative metrics like perplexity and FID score to ensure the model generates diverse and high-quality content, assuming that 'creative' and 'unique' will naturally emerge.

Answer: D

Explanation:
Subjective requirements like 'creative' and 'unique' require iterative exploration and client feedback. AIB testing (B) provides quantitative data on user perception. Interactive prompt refinement (D) allows the client to actively shape the model's output and discover their preferences. Quantitative metrics alone (A) are insufficient for capturing subjective qualities. Style transfer (C) can be helpful but doesn't directly address the client's specific vision. Thus, iterative AIB testing and Interactive prompt refinement would be the ideal method.

NEW QUESTION # 370
You are building a generative A1 model that combines text and image inputs to generate novel images. You have access to NVIDIA NeMo and want to leverage its pre-trained models and tools. Which NeMo modules or features would be MOST beneficial for this multimodal task? (Select all that apply)

A. NeMo's TTS models for generating image descriptions.
B. NeMo's support for PyTorch Lightning for efficient training and scaling.
C. NeMo's core building blocks for constructing custom neural network architectures.
D. NeMo's ASR models for processing text inputs.
E. NeMo's pre-trained language models for text understanding and feature extraction.

Answer: B,C,E

Explanation:
NeMo's core building blocks simplify the creation of complex neural networks. PyTorch Lightning integration streamlines the training process, and NeMo's pre-trained language models provide a strong foundation for understanding and processing text inputs. ASR and TTS are irrelevant for the pure text and image to image creation. NeMo's core building blocks are fundamental to the framework.

NEW QUESTION # 371
......

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