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NCA-GENM Valid Exam Review, NCA-GENM Pdf Version

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NVIDIA Generative AI Multimodal Sample Questions (Q300-Q305):

NEW QUESTION # 300
You are building a multimodal Generative A1 system to generate image captions based on both the visual content of an image and a short audio description of the scene. Which architectural approach would be MOST effective for fusing these two modalities into a coherent representation for caption generation?

A. Early Fusion: Concatenate the raw image pixel data with the raw audio waveform data before feeding it into a single model.
B. Concatenate the image file name with the audio file name before feeding into the LLM.
C. Intermediate Fusion: Train separate image and audio encoders, then use cross-attention mechanisms to allow the image features to attend to the audio features (and vice-versa) at multiple layers of the model.
D. Late Fusion: Train separate image and audio encoders, then concatenate their high-level feature vectors before feeding into a caption generation model.
E. Ignore the audio entirely, as images are sufficient for generating captions.

Answer: C

Explanation:
Intermediate Fusion, particularly using cross-attention, allows for nuanced interaction between the modalities at multiple levels of abstraction. Early fusion is generally ineffective due to the vast differences in data type. Late fusion may miss important correlations. Ignoring a modality is obviously suboptimal when aiming for multimodal understanding.

NEW QUESTION # 301
Consider the following scenario: You're training a GAN for generating high-resolution images (e.g., 1024x1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

A. Increasing the learning rate of both the generator and discriminator.
B. Using ReLU activation functions in the discriminator.
C. Replacing standard convolutional layers with transposed convolutional layers in the generator.
D. Applying batch normalization in both the generator and discriminator.
E. Using Wasserstein GAN (WGAN) with gradient penalty (GP).

Answer: D,E

Explanation:
WGAN with gradient penalty (GP) addresses the instability caused by the Jensen-Shannon divergence used in standard GANs. Batch normalization can help stabilize training by reducing internal covariate shift. Transposed convolutions are a common practice but don't inherently stabilize training. Increasing the learning rate can exacerbate instability. ReLU activation can lead to vanishing gradients.

NEW QUESTION # 302
You have a text-to-image model deployed using Triton Inference Server. You want to monitor the GPU utilization and inference latency to ensure optimal performance. Which of the following methods is the MOST effective way to achieve this?

A. Using 'nvidia-smi' to periodically check GPU utilization and manually calculate latency.
B. Relying solely on the operating system's resource monitor to track GPU usage.
C. Writing custom scripts to parse Triton's log files and extract performance metrics.
D. Using Triton's built-in Prometheus metrics endpoint and Grafana for visualization.
E. Using the Triton Inference Server client API to measure inference latency from the client-side.

Answer: D

Explanation:
Triton Inference Server exposes a Prometheus metrics endpoint that provides detailed information about GPIJ utilization, inference latency, and other performance metrics. Prometheus is a popular time-series database and monitoring solution. Grafana can then be used to visualize these metrics in real-time dashboards. This is the recommended approach for monitoring Triton deployments.

NEW QUESTION # 303
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?

A. Use a larger vocabulary for the text encoder.
B. Implement a contrastive learning objective that encourages similar embeddings for corresponding video frames and text descriptions.
C. Decrease the resolution of the video frames.
D. Using only pretrained model weights.
E. Increase the frame rate of the training videos.

Answer: B

Explanation:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.

NEW QUESTION # 304
You are developing an Avatar Cloud Engine (ACE) application for a virtual assistant that needs to generate realistic facial expressions based on user emotions detected from text. Which ACE microservice would be most directly responsible for this functionality?

A. Facial Animation
B. speech to Text (STT)
C. Natural Language Understanding (NLU)
D. Lip Sync
E. Text to Speech (TTS)

Answer: A

Explanation:
The Facial Animation microservice within ACE is specifically designed to generate realistic facial expressions for avatars. While NLU detects the emotion, Facial Animation translates that emotion into corresponding facial movements.

NEW QUESTION # 305
......

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