LATEST UPDATED NVIDIA EXAM NCA-GENM BOOTCAMP ARE LEADING MATERIALS & TOP NCA-GENM: NVIDIA GENERATIVE AI MULTIMODAL

Latest updated NVIDIA Exam NCA-GENM Bootcamp Are Leading Materials & Top NCA-GENM: NVIDIA Generative AI Multimodal

Latest updated NVIDIA Exam NCA-GENM Bootcamp Are Leading Materials & Top NCA-GENM: NVIDIA Generative AI Multimodal

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NVIDIA Generative AI Multimodal Sample Questions (Q59-Q64):

NEW QUESTION # 59
Consider the following Python code snippet using Triton Inference Server's Python client. The code intends to send a request to a model that expects two input tensors: 'input_image' (shape: [1, 3, 224, 224], datatype: FP32) and 'input_text' (shape: [1 ,], datatype: BYTES). Identify potential issues in this code that could prevent successful inference.

  • A. The function is used incorrectly; it should directly accept the Triton datatype string (e.g., 'FP32').
  • B. The data is not converted to the appropriate NumPy datatype before being sent to Triton.
  • C. The model name and input/output names must be specified, but they are missing in the code.
  • D. The input data for 'input_text' needs to be encoded to bytes using UTF-8 encoding before being passed to Triton.
  • E. All of the above.

Answer: E

Explanation:
All the mentioned issues (A, B, C, D) can prevent successful inference. requires correction. The input text requires explicit byte encoding. Converting input_image to the correct numpy data type. Specifying the model name and input/output names is important for triton to understand the request. If all of these requirements are not met, the triton request will fail.


NEW QUESTION # 60
When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

  • A. Increase the number of diffusion steps.
  • B. Employ distillation techniques to train a faster, smaller model.
  • C. Train the GAN for a longer duration.
  • D. Use a smaller batch size.
  • E. Use a larger UNet architecture within the Diffusion model.

Answer: B

Explanation:
Model distillation involves training a smaller, faster 'student' model to mimic the behavior of a larger, slower 'teacher' model. This allows you to retain much of the quality of the original model while significantly improving inference speed. Increasing the number of diffusion steps or using a larger UNet would further slow down the Diffusion model. Training the GAN longer doesn't address the speed issue of the Diffusion model. Using a smaller batch size might help with memory limitations, but won't significantly improve inference speed.


NEW QUESTION # 61
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. Using completely unrelated keywords to encourage the model to create something unique.
  • B. Specifying 'oil painting' or another artistic style to mask the lack of detail.
  • C. Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
  • 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: C

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 # 62
You are working with a large dataset of images for training a generative model. The dataset contains a significant amount of noise and outliers. Which of the following data preprocessing techniques would be MOST effective in mitigating the impact of noise and outliers on the model's performance?

  • A. Using a robust statistics-based normalization technique (e.g., Z-score normalization with median and interquartile range).
  • B. Applying a Gaussian blur to all images.
  • C. Clipping pixel values to a specific range (e.g., [0, 255]).
  • D. Applying histogram equalization to all images.
  • E. Converting all images to grayscale.

Answer: A

Explanation:
Robust statistics-based normalization techniques, such as Z-score normalization using the median and interquartile range (IQR), are less sensitive to outliers than traditional methods like mean and standard deviation. Clipping pixel values can help to limit the impact of extreme outliers, but it may also remove valid data. Histogram equalization and Gaussian blur can improve image quality, but they are not specifically designed to handle outliers. Converting to grayscale reduces information but doesn't address noise specifically.


NEW QUESTION # 63
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. Number of lines of code in the model.
  • B. GPU utilization.
  • C. Model size on disk.
  • D. Inference latency (time per request).
  • E. BLEU score (or similar text generation metric) for generated descriptions.

Answer: D,E

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 # 64
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