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Test NCA-GENM Answers | NVIDIA Real NCA-GENM Testing Environment: NVIDIA Generative AI Multimodal Pass Certainly

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NVIDIA Generative AI Multimodal Sample Questions (Q29-Q34):

NEW QUESTION # 29
You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

A. Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.
B. Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'
C. Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
D. Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').
E. Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.

Answer: A,C,D,E

Explanation:
Few-shot prompting provides the LLM with examples to learn from, allowing it to better handle the nuances of informal language and errors. Chain-of-thought helps the model reason step-by-step, leading to better summaries. Negative constraints prevent irrelevant information. Template prompts provide structure and consistency. A zero-shot prompt is less effective in this scenario due to the complexity of the input data.

NEW QUESTION # 30
You are designing a IJ-Net architecture for semantic segmentation of medical images. Your input images are 512x512 with 3 channels.
You want to ensure the final output segmentation map is also 512x512. Which of the following design choices are crucial for achieving this resolution, considering the downsampling and upsampling stages?

A. Ensuring that the number of downsampling and upsampling blocks are equal, and employing skip connections from corresponding encoder layers to decoder layers.
B. Employing only IXI convolutions in the bottleneck of the U-Net architecture to reduce computational complexity.
C. Using max pooling with a kernel size of 3x3 and stride of 2 for downsampling, and nearest neighbor interpolation for upsampling.
D. Using only strided convolutions for downsampling and transposed convolutions for upsampling without skip connections.
E. Using a batch size of 1 during training to simplify memory management.

Answer: A

Explanation:
Maintaining the same resolution in IJ-Net requires symmetric downsampling and upsampling and the crucial use of skip connections. These connections pass feature maps from the downsampling (encoder) path to the corresponding layers in the upsampling (decoder) path, allowing the decoder to recover spatial information lost during downsampling. Option A omits skip connections. Option C's nearest neighbor interpolation can lead to blocky artifacts. Option D describes a bottleneck optimization unrelated to output resolution. Option E is about training parameters.

NEW QUESTION # 31
You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?

A. Decrease the dimensionality of the image embeddings using PCA
B. Increase batch size substantially.
C. Apply a modality-specific loss weight, giving higher weight to the image loss during training-
D. Increase the learning rate of the BERT text encoder
E. Freeze the weights of the ResNet-50 image encoder

Answer: C

Explanation:
Applying a modality-specific loss weight allows you to explicitly control the importance of each modality during training. By increasing the weight of the image loss, you encourage the model to pay more attention to the image information and generate captions that are more representative of the visual content. Increasing BERT's learning rate could worsen the imbalance. PCA is a data reduction technique not a balancing technique, freezing the weights on resnet 50, will not allow the network to learn about the relationship, batch size is to do with training speed not modality balance.

NEW QUESTION # 32
During data analysis for a multimodal A1 project involving image and text data, you discover that the image dataset contains a large number of blurry or low-resolution images. The text data, however, is relatively clean and well-structured. What is the BEST approach to mitigate the impact of the noisy image data on the overall model performance?

A. Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.
B. Train the model on the noisy image data without any preprocessing or data augmentation.
C. Discard the blurry and low-resolution images from the dataset to ensure data quality.
D. Increase the weight of the text data during model training to compensate for the noisy image data.
E. Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.

Answer: E

Explanation:
A combination of image enhancement and robust loss functions provides the best approach. Image enhancement techniques can improve the quality of the blurry images, making them more informative for the model. Robust loss functions, such as Huber loss or Tukey's biweight loss, are less sensitive to outliers and noisy data, which can further mitigate the impact of the remaining noise. Discarding data (A) reduces the dataset size. Increasing the weight of text data (C) may lead to the model ignoring visual information. Training on raw noisy data (D) will severely impact the model's ability to learn correct mappings.

NEW QUESTION # 33
Consider this Python code snippet using PyTorch:

A. Error. The transpose operation is incorrect for achieving cross-modal attention.
B. torch.Size ([32, 32]). This is correct and computes attention weights for each text-image pair in the batch independently
C.
D. torch.Size ([32, 5121). The issue is a dimension mismatch.
E. torch.Size([256, 512]). This implementation is correct and efficient for cross-modal attention.

Answer: C

Explanation:
The shape of the 'attention' tensor is torch.Size([32, 32]). The matrix multiplication of (32, 256) with (512, 32) results in a (32, 32) tensor. The crucial issue here is the batch-wise attention calculation. The attention weights are being computed between all text embeddings and all image embeddings in the batch. During training, this leads to 'information leakage' because the model is learning relationships between samples that shouldn't be related (i.e., different text-image pairs in the batch are influencing each other). For proper cross-modal attention, you would typically want to compute the attention weights only between corresponding text and image embeddings within the same sample.

NEW QUESTION # 34
......

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