NCA-GENM TEST PRICE, NCA-GENM TEST DISCOUNT

NCA-GENM Test Price, NCA-GENM Test Discount

NCA-GENM Test Price, NCA-GENM Test Discount

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Tags: NCA-GENM Test Price, NCA-GENM Test Discount, NCA-GENM New Exam Materials, NCA-GENM Reliable Exam Vce, NCA-GENM Reliable Test Syllabus

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NVIDIA Generative AI Multimodal Sample Questions (Q80-Q85):

NEW QUESTION # 80
Consider the following Python code snippet that utilizes a pre-trained language model from the Hugging Face Transformers library:

Which of the following statements are TRUE regarding the generated output?

  • A. The GPT-2 model is guaranteed to generate grammatically correct and factually accurate text.
  • B. The parameter controls the number of different completion the model should return.
  • C. The output will contain a single sequence of text generated by the GPT-2 model, starting with the provided prompt.
  • D. The output will always be exactly 50 tokens long.
  • E. The output will always start with "The quick brown fox jumps over the lazy".

Answer: B,C,E

Explanation:
The code uses the Hugging Face Transformers pipeline to generate text using the GPT-2 model. The 'max_length' parameter sets the maximum length of the generated sequence, but the model may stop generating earlier if it reaches a natural stopping point. num_return_sequences' controls the number of sequences that return. Pre-trained language models are not guaranteed to be grammatically perfect or factually accurate. The output always includes the prompt.


NEW QUESTION # 81
A self-driving car uses multimodal data (camera images, LiDAR point clouds, radar data, and GPS information) to navigate. The LiDAR sensor occasionally fails, resulting in missing point cloud dat a. How should the system be designed to handle this sensor failure gracefully and maintain safe navigation?

  • A. Immediately stop the car until the LiDAR sensor is fixed.
  • B. Employ a Kalman filter to predict the LiDAR point cloud based on the previous sensor readings and the car's motion model.
  • C. Rely solely on the camera images and ignore the missing LiDAR data.
  • D. Use a sensor fusion technique that prioritizes the available modalities (camera, radar, GPS) and estimates the missing LiDAR data based on these modalities.
  • E. Switch to a pre-programmed route that does not require LiDAR data.

Answer: B,D

Explanation:
Stopping the car or relying solely on a single modality is not a robust solution. Using sensor fusion to prioritize available modalities and estimate missing data allows the system to continue navigating safely. A Kalman filter is a specific technique for estimating the state of a system (in this case, the LiDAR point cloud) based on noisy sensor readings and a motion model.


NEW QUESTION # 82
Which of the following techniques are commonly used to address the 'hallucination' problem in generative A1 models, where the model generates content that is factually incorrect or nonsensical? (Select all that apply)

  • A. Employing adversarial training techniques to improve the model's robustness to noisy input data.
  • B. Lowering the temperature parameter during decoding to encourage more conservative and predictable outputs.
  • C. Implementing retrieval-augmented generation (RAG), where the model consults an external knowledge source before generating content.
  • D. Increasing the model size and training dataset.
  • E. Using Reinforcement Learning from Human Feedback (RLHF) to align the model's output with human preferences and factual correctness.

Answer: B,C,E

Explanation:
RLHF helps align the model with human values, reducing nonsensical outputs. RAG grounds the generation process in external knowledge, ensuring factual accuracy. Lowering the temperature reduces the likelihood of sampling low-probability, potentially hallucinated, tokens. Increasing model Size or using adversarial training can improve model performance, but doesn't directly address hallucination.


NEW QUESTION # 83
You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

  • A. Increase the number of inference steps.
  • B. Change the random seed to explore different variations.
  • C. Use a negative prompt to exclude unwanted elements or styles.
  • D. Reduce the number of inference steps.
  • E. Decrease the CFG (Classifier-Free Guidance) scale.

Answer: A,B,C

Explanation:
Increasing the number of inference steps allows the diffusion process to refine the image more thoroughly. Using a negative prompt helps to guide the generation process by specifying what not to include in the image. Changing the random seed allows you to explore different variations of the same prompt, which can lead to more desirable results. Decreasing the CFG scale can reduce adherence to the prompt, and reducing the number of inference steps results in less refined images.


NEW QUESTION # 84
You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?

  • A. Incorporating a Transformer-based architecture, such as a Vision Transformer (ViT) for visual feature extraction and a standard Transformer for sequence generation.
  • B. Reducing the frame rate of the input video to reduce the temporal complexity.
  • C. Using a smaller batch size during training to reduce memory consumption.
  • D. Increasing the number of layers in the CNN to extract more detailed visual features.

Answer: B

Explanation:
Transformers are known for their ability to capture long-range dependencies due to their self-attention mechanism. Replacing LSTMs with Transformers allows the model to attend to relevant parts of the video sequence regardless of their temporal distance. While CNNs can extract visual features, they don't inherently address long-range dependencies. RNNs are prone to vanishing gradients, making it difficult to learn long- range dependencies. Reducing the frame rate or batch size doesn't directly address the issue of capturing long-range dependencies within the video sequence.


NEW QUESTION # 85
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