NVIDIA NCA-GENM Exam Questions And Answers & Customized NCA-GENM Lab Simulation

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NVIDIA Generative AI Multimodal Sample Questions (Q126-Q131):

NEW QUESTION # 126
You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

A. Synchronization issues between audio and video streams.
B. Variations in background noise affecting audio quality.
C. All of the above.
D. Subjectivity in emotional expression across cultures and individuals.
E. Differences in lighting conditions influencing facial expression recognition.

Answer: C

Explanation:
All the options pose challenges. Background noise affects audio analysis, varying lighting impairs facial recognition, subjective emotional expression necessitates robust models, and synchronization issues hinder accurate multimodal integration.

NEW QUESTION # 127
Consider the following PyTorch code snippet for a multimodal loss function:

What is the MOST significant issue with this code, preventing it from working as intended for a multimodal task?

A. The code uses 'CrossEntropyLosS , which is not suitable for feature vectors but for classification scores.
B. The 'alpha' parameter is not being used correctly to balance the image and text losses.
C. The function only works for a specific batch size.
D. The code lacks normalization of image and text features before computing the loss.
E. The code doesn't include any regularization to prevent overfitting.

Answer: A

Explanation:
The 'CrossEntropyLoss' expects classification scores as input (logits before softmax), not feature vectors. The code passes the feature vectors directly to the loss function, which will lead to incorrect and meaningless results. The other options are less critical: the alpha parameter is used correctly even if the balancing might be suboptimal, feature normalization and regularization are always beneficial but not strictly required, and the function does not require a specific batch size.

NEW QUESTION # 128
You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?

A. Riva ASR, Riva TTS, and Audi02Emotion.
B. Only a 3D avatar model.
C. Riva ASR, Riva TTS, Audi02Emotion, and a 3D avatar model.
D. Riva ASR, Riva TTS, Audi02Emotion, a 3D avatar model, and an animation engine.
E. only Riva ASR and TTS.

Answer: D

Explanation:
A complete ACE setup for realistic avatar interaction requires: Automatic Speech Recognition (ASR) to understand the user's query, Text-to-Speech (TTS) to generate the avatar's response, Audi02Emotion to infer emotional expressions from the text/audio, a 3D avatar model to represent the avatar visually, and an animation engine to drive facial expressions and lip synchronization. This combination ensures a lifelike and engaging user experience.

NEW QUESTION # 129
You're building a system that uses a pre-trained large language model (LLM) for generating creative stories. After deploying the system, you notice that the generated stories often contain biases present in the training data of the LLM. What are the MOST effective strategies to mitigate these biases in your generated stories? (Select TWO)

A. Fine-tune the LLM on a diverse and representative dataset.
B. Increase the temperature parameter in the LLM's decoding strategy.
C. Apply bias detection and mitigation techniques to the LLM's output.
D. Use prompt engineering to steer the LLM away from biased outputs.
E. Reduce the size of the LLM to minimize memory usage.

Answer: C,D

Explanation:
Bias detection and mitigation techniques can be applied to the LLM's output to identify and remove or modify biased content. Prompt engineering involves carefully crafting prompts to guide the LLM towards generating more balanced and unbiased stories. Fine-tuning on a diverse dataset is beneficial in the long run but is a more resource-intensive approach and doesn't guarantee the complete elimination of biases. Increasing the temperature parameter might lead to more creative but also more unpredictable and potentially biased outputs. Reducing the size of the LLM doesn't address the bias problem and might even worsen it by limiting the model's ability to capture nuanced relationships.

NEW QUESTION # 130
You are developing a multimodal model that takes both images and text as input. You want to fuse these modalities at an early stage.
Which of the following techniques is MOST appropriate for early fusion?

A. Passing the image and text through separate encoders and then using a late fusion technique such as a weighted sum of their outputs.
B. Using a cross-attention mechanism between the image and text feature maps in the early layers of the model.
C. Concatenating the feature vectors extracted from the image and text encoders before passing them to a shared decoder.
D. Concatenating the raw image pixels and text tokens as a single input sequence.
E. Training separate models for images and text and then averaging their predictions.

Answer: B

Explanation:
Cross-attention mechanisms at early layers allow the model to learn interactions between image and text features from the beginning, enabling a more nuanced understanding of their relationships. Concatenating feature vectors is a form of early fusion (A), but cross- attention is more powerful. Averaging predictions (B) is a late fusion technique. Concatenating raw pixels and tokens (E) is unlikely to work well due to the different nature of the data. Weighted sum (D) represents late fusion.

NEW QUESTION # 131
......

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NVIDIA NCA-GENM Exam Questions And Answers & Customized NCA-GENM Lab Simulation

Customized NVIDIA NCA-GENM Lab Simulation, NCA-GENM Valid Test Voucher

NVIDIA Generative AI Multimodal Sample Questions (Q126-Q131):

NCA-GENM Learning Materials Ensure Success in Any NCA-GENM Exam - Real4Prep

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