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NCA-GENM Test Lab Questions - NCA-GENM Latest Learning Material
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NVIDIA Generative AI Multimodal Sample Questions (Q373-Q378):
NEW QUESTION # 373
You are building a multimodal emotion recognition system that uses facial expressions (images) and speech (audio). You want to use transfer learning to leverage pre-trained models for both modalities. You have access to a large pre-trained facial recognition model (trained on millions of faces) and a large pre-trained speech recognition model (trained on thousands of hours of speech). How do you design a multimodal transfer learning strategy to efficiently train the entire system on a smaller dataset of peoples face and audio samples?
- A. Train the face model first, then train the audio model to recognize emotions based on the results of the facial expression emotions.
- B. Extract features separately using each of the pre-trained face and speech models and then train a separate classifier model, combining those features to recognize emotion.
- C. Fine-tune each of the pre-trained models for the emotion recognition task using a joint loss function that combines the outputs of face emotion and speech emotion to create an overall expression.
- D. Use the features of the face data as an attention mechanism to pay attention to the audio, in an end-to-end learning model.
- E. Train the Audio model first, then train the Face model to recognize emotions based on the results of the audio expression emotions.
Answer: C,D
Explanation:
Fine-tuning the pre-trained models using a joint loss function helps the model to adapt to a combined face and speech emotion recognition task. In addition, using the features of one modality as an attention mechanism for the other modality can help guide an end-to-end training model. Feature extraction is more of a traditional method and does not fully allow pre-trained models to fully transfer. Training in series might not result in the best model performance since multimodal emotion recognition is about using all facets of information to predict.
NEW QUESTION # 374
When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)
- A. Utilize asynchronous processing and caching strategies to pre-compute and store frequently accessed data.
- B. Prioritize model accuracy over inference speed.
- C. Deploy the model on a single CPU core to minimize resource contention.
- D. Employ model quantization and pruning techniques to reduce model size and computational requirements.
- E. Disable any logging or monitoring to reduce overhead.
Answer: A,D
Explanation:
Model quantization and pruning reduce the model's size and computational complexity, leading to faster inference. Asynchronous processing and caching allow for pre-computation and storage of frequently used data, minimizing delays. Prioritizing accuracy over speed (A) is not suitable for real-time applications where responsiveness is crucial. Deploying on a single CPU core (D) would severely limit performance. Disabling logging (E) is detrimental for debugging and monitoring.
NEW QUESTION # 375
You are developing a system that uses a generative A1 model deployed with Triton Inference Server to create personalized avatars. You want to ensure that the system is robust against malicious inputs designed to generate offensive or harmful content. Which of the following security measures are most critical to implement in conjunction with Triton?
- A. Implementing input validation to enforce a maximum length for text prompts, using a content moderation API to filter the output generated by the model, and logging all user activity for auditing purposes.
- B. Encrypting all communication between the client and the Triton server using HTTPS, restricting access to the Triton server to a private network, and using strong passwords for all Triton accounts.
- C. All of the above.
- D. Rate limiting the number of requests per user, implementing input validation and sanitization to filter out potentially harmful prompts, and regularly auditing the generated content for offensive material.
- E. Using a web application firewall (WAF) to protect the Triton server from denial-of-service attacks, implementing role-based access control (RBAC) to limit user privileges, and regularly updating the Triton software to patch security vulnerabilities.
Answer: C
Explanation:
All the security measures listed are crucial for protecting a generative A1 system from malicious inputs. (A, B, C, D). These steps ensure the system's security, protect sensitive data, prevent misuse, and allow you to monitor and respond to potential issues effectively.
NEW QUESTION # 376
You are experimenting with different architectures for a text-to-speech (TTS) model. You have implemented a Tacotron 2 model and a FastSpeech 2 model. Which of the following statements accurately describes the key differences between these two architectures and their implications?
- A. Both Tacotron 2 and FastSpeech 2 use attention mechanisms, but FastSpeech 2 incorporates length regulator and variance adaptor modules to address the one-to-many mapping problem, leading to more stable and controllable synthesis.
- B. Tacotron 2 uses an attention mechanism for aligning text and speech, while FastSpeech 2 relies on a fixed alignment, resulting in faster training and inference for FastSpeech 2 but potentially lower quality.
- C. FastSpeech 2 uses an attention mechanism for aligning text and speech, while Tacotron 2 relies on a fixed alignment, resulting in faster training and inference for Tacotron 2 but potentially lower quality.
- D. Tacotron 2 is an autoregressive model, while FastSpeech 2 is a non-autoregressive model. This allows FastSpeech 2 to generate speech in parallel, resulting in significantly faster inference speeds.
- E. Both Tacotron 2 and FastSpeech 2 rely on fixed alignments, but FastSpeech 2 uses a more complex decoder architecture, leading to higher quality but slower inference.
Answer: A,D
Explanation:
Tacotron 2 is an autoregressive model that uses an attention mechanism for aligning text and speech, whereas FastSpeech 2 is a non-autoregressive model, generating speech in parallel for faster inference. FastSpeech 2 also addresses the one-to-many mapping problem (one phoneme can have different durations) with length regulator and variance adaptor modules, improving stability and controllability. A is incorrect because attention based Tacotron 2 is slow to train and infer. B and D are incorrect becuase Tacotron 2 architecture has attention mechanism.
NEW QUESTION # 377
A multimodal A1 model is designed to translate sign language videos into text. The model performs well on videos with clear hand gestures and lighting conditions but struggles with videos recorded in low light or with partial hand occlusions. Which of the following strategies would be MOST effective in improving the model's robustness to these challenging conditions?
- A. Using a simpler text encoder.
- B. Applying image enhancement techniques (e.g., contrast adjustment, noise reduction) to the video frames.
- C. Training the model on a smaller dataset.
- D. Reducing the frame rate of the input videos.
- E. Increasing the size of the text vocabulary.
Answer: B
Explanation:
Applying image enhancement techniques to the video frames can improve the visibility of hand gestures in low-light conditions and reduce the impact of noise, making the model more robust. Reducing the frame rate or training on a smaller dataset would likely decrease performance. Increasing the text vocabulary or using a simpler text encoder would not directly address the issue of poor video quality.
NEW QUESTION # 378
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