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NVIDIA NCA-GENM Reliable Real Exam, Study NCA-GENM Reference

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NVIDIA Generative AI Multimodal Sample Questions (Q153-Q158):

NEW QUESTION # 153
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. 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.
B. 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.
C. All of the above.
D. 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.
E. 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.

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 # 154
You're building a multimodal model that processes both images and text. The image encoder outputs a feature vector of size 2048, and the text encoder outputs a feature vector of size 512. Which of the following strategies is MOST appropriate for combining these feature vectors before feeding them into a downstream classifier?

A. Average the two feature vectors, resulting in a combined vector of size 2048
B. Project both feature vectors into a common embedding space of size 512 using separate linear layers, then concatenate the resulting vectors.
C. Project both feature vectors into a common embedding space of size 256 using separate linear layers, then concatenate the resulting vectors.
D. Concatenate the two feature vectors directly, resulting in a combined vector of size 2560.
E. Project both feature vectors into a common embedding space of size 2048 using separate linear layers, then average the resulting vectors.

Answer: C

Explanation:
Projecting into a lower-dimensional common embedding space reduces dimensionality and potential overfitting, while concatenation after projection retains information from both modalities. Averaging might mask distinct features. Projecting to original vector sizes before averaging is possible, but concatenating smaller projected vectors is most memory efficient

NEW QUESTION # 155
You're tasked with building a model that can generate recipes from images of food. You decide to use a Variational Autoencoder (VAE) architecture. What would be a suitable loss function combination for this task, considering both reconstruction accuracy and recipe relevance?

A. KL divergence loss only-
B. Reconstruction loss (MSE) + KL divergence loss + Cross-entropy loss between the generated recipe and a plausible recipe given the generated image embedding-
C. KL divergence loss + Cosine Similarity loss between the generated image embedding and a text embedding of a random recipe.
D. Reconstruction loss (MSE) between the input image and the decoded image only
E. Reconstruction loss (MSE) + Perceptual loss (based on a pre-trained image classifier) only

Answer: B

Explanation:
The Reconstruction loss ensures the generated image is similar to the input. KL divergence enforces a smooth latent space. The Cross-entropy loss ensures the generated recipe is relevant to the decoded image. Perceptual loss, while helpful for image quality, doesn't directly address recipe relevance. Using a text embedding of a random recipe would not guide the model towards generating relevant recipes.

NEW QUESTION # 156
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. Increasing the model size and training dataset.
C. Using Reinforcement Learning from Human Feedback (RLHF) to align the model's output with human preferences and factual correctness.
D. Implementing retrieval-augmented generation (RAG), where the model consults an external knowledge source before generating content.
E. Lowering the temperature parameter during decoding to encourage more conservative and predictable outputs.

Answer: C,D,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 # 157
You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

A. Increasing the batch size to utilize the available memory more efficiently.
B. Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.
C. Freezing all layers of the pre-trained model and training only a small classification head.
D. Fine-tuning the entire model with a small learning rate.
E. Using quantization to reduce the precision of the model's weights and activations.

Answer: B,E

Explanation:
Quantization reduces the memory footprint of the model by using lower-precision representations for weights and activations. Knowledge distillation allows you to train a smaller, more efficient model that performs similarly to the larger pre-trained model. Freezing layers reduces the number of trainable parameters but may limit the model's ability to adapt to the new task. Fine-tuning the entire model, even with a small learning rate, is computationally expensive. Increasing batch size might lead to Out of Memory errors.

NEW QUESTION # 158
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