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NVIDIA NCA-GENM Brain Dump Free - Exam NCA-GENM Lab Questions

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NVIDIA Generative AI Multimodal Sample Questions (Q377-Q382):

NEW QUESTION # 377
You are training a multimodal generative A1 model for image captioning. After initial training, you observe that the model excels at describing common objects but struggles with nuanced details and rare objects. Which of the following performance optimization strategies would be MOST effective in addressing this issue?

A. Reduce the learning rate to fine-tune the model on the existing dataset.
B. Apply early stopping to prevent overfitting to the common objects.
C. Increase the batch size during training to improve GPU utilization.
D. Increase the number of layers in the encoder network.
E. Implement a custom loss function that penalizes inaccuracies in describing rare objects more heavily.

Answer: E

Explanation:
Implementing a custom loss function is the most effective strategy because it directly addresses the model's weakness by focusing on accurate descriptions of rare objects. Increasing batch size improves training speed but not necessarily accuracy. Early stopping prevents overfitting, but doesn't specifically target the issue of rare object recognition. Reducing the learning rate might help with fine-tuning, but not as effectively as a targeted loss function. Increasing the number of layers may increase complexity but not guarantee better performance on rare objects.

NEW QUESTION # 378
You are working on a project involving generating photorealistic images of human faces using a generative model. Ethical considerations are paramount. Which of the following practices are MOST important to incorporate into your development workflow to mitigate potential biases and misuse?

A. Focusing solely on improving the technical performance of the model, ignoring potential ethical concerns, and releasing the model as open-source to promote innovation.
B. Prioritizing speed and efficiency in the development process, neglecting to address potential biases, and deploying the model without conducting thorough testing or evaluation.
C. Using synthetic data for training to avoid any potential privacy concerns related to real-world data, ignoring potential biases in the synthetic data, and claiming that the model is completely unbiased.
D. Implementing strict controls over the types of images the model can generate, limiting its use to specific applications, and restricting access to the model to a small group of trusted individuals.
E. Training the model on a diverse and representative dataset, implementing mechanisms to detect and mitigate biases in the generated images, and providing transparency about the limitations and potential risks of the technology.

Answer: E

Explanation:
Addressing ethical considerations requires a multi-faceted approach, including training on diverse data, bias detection/mitigation, and transparency. Option A encompasses all these aspects. Ignoring ethical concerns (B, D) is irresponsible. Restricting access (C) might not be feasible or effective. Synthetic data (E) can still be biased. Claiming a model is completely unbiased is misleading and incorrect.

NEW QUESTION # 379
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?

A. Training a custom text encoder from scratch with a larger dataset specifically tailored to your application's domain.
B. Increasing the temperature parameter of the diffusion model used in conjunction with CLIP to introduce more randomness and potentially more detail.
C. Applying prompt engineering techniques such as adding descriptive adjectives and context to the text prompt and fine-tuning the prompt with iterative feedback.
D. Using a larger image decoder network with more parameters to add detail during the image generation process.
E. Reducing the number of training steps for the diffusion model to prevent overfitting to the training data and promote generalization.

Answer: C

Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.

NEW QUESTION # 380
Consider a multimodal emotion recognition system that uses both facial expressions and speech audio as input. You want to fuse the information from these two modalities. Which of the following fusion techniques would be most suitable if the modalities have significantly different temporal resolutions (e.g., facial expressions change more rapidly than overall vocal tone)?

A. Late Fusion (averaging probabilities from individual classifiers)
B. Feature Extraction (extracting features)
C. Early Fusion (concatenating raw features)
D. Decision Fusion (majority voting based on modality predictions)
E. Intermediate Fusion (using attention mechanisms to align features)

Answer: E

Explanation:
Intermediate fusion, particularly with attention mechanisms, is well-suited for modalities with different temporal resolutions. Attention allows the model to dynamically align and weight the features from each modality based on their relevance at different time steps, addressing the temporal misalignment issue. Early fusion would be problematic as the temporal differences are not handled. Late fusion ignores the potential interactions between the modalities. Decision fusion suffers from the same issues as late fusion. Feature extraction is not fusion technique.

NEW QUESTION # 381
You are developing a multimodal generative A1 model that takes both image and text inputs. The image branch uses a ResNet50 pre- trained on ImageNet, while the text branch uses a BERT model. To effectively combine the features, you need to align their representations. Which of the following techniques is MOST suitable for projecting the image and text features into a common embedding space?

A. Employing Contrastive Learning with a shared embedding space and using positive and negative pairs of image and text.
B. Direct concatenation of ResNet50 and BERT output features.
C. Fine-tuning the entire ResNet50 and BERT models jointly on the multimodal dataset.
D. Training separate linear projection layers for both ResNet50 and BERT outputs, followed by concatenation.
E. Using Principal Component Analysis (PCA) to reduce the dimensionality of ResNet50 and BERT features before concatenation.

Answer: A

Explanation:
Contrastive learning is highly effective for aligning representations from different modalities. By training the model to pull together embeddings of related image-text pairs while pushing apart embeddings of unrelated pairs, it learns a shared embedding space where semantically similar concepts are close to each other, regardless of their modality. While (B) is a possible approach, it doesn't explicitly enforce alignment based on semantic similarity. (A) is unlikely to produce good results due to differing feature spaces. (C) is computationally expensive. (D) is a dimensionality reduction technique, not primarily an alignment method.

NEW QUESTION # 382
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