最新NCA-GENM題庫 & NCA-GENM考試證照綜述

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最新的 NVIDIA-Certified Associate NCA-GENM 免費考試真題 (Q323-Q328):

問題 #323
When working with geospatial data in conjunction with text data (e.g., analyzing tweets related to specific geographical locations), what are some of the key challenges in terms of data curation and quality assessment, and how can these challenges be addressed?

A. The sparsity of geospatial data in certain regions, which can be mitigated by using spatial interpolation techniques to estimate values in unobserved areas.
B. Geospatial data is inherently accurate and requires no specific curation or quality assessment.
C. Inaccurate or ambiguous geolocation information in text data, which can be addressed by using geocoding services and verifying location accuracy with external data sources.
D. Different coordinate systems and projections used in geospatial datasets, which can be resolved by transforming all data to a common coordinate system.
E. The lack of tools for analyzing geospatial data with textual information, requiring custom software development.

答案：A,C,D

解題說明：
Geospatial data often suffers from inaccuracies, inconsistencies in coordinate systems, and sparsity. Addressing these challenges requires geocoding, coordinate system transformations, and spatial interpolation techniques. Many tools are available for geospatial-textual analysis.

問題 #324
You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?

A. None of the above.
B. Implement classifier-free guidance, adjusting the guidance scale to balance fidelity and diversity.
C. Reduce the learning rate during training.
D. Use a larger batch size during training.
E. Increase the number of diffusion steps during image generation.

答案：B

解題說明：
Classifier-free guidance allows you to control the influence of the text description on the generated image. By adjusting the guidance scale, you can find a balance between generating images that are faithful to the text description and generating diverse and high-quality images. Increasing diffusion steps, batch size, or reducing the learning rate may help but is less targeted towards improving fidelity specifically.

問題 #325
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. Increase the number of layers in the encoder network.
C. Implement a custom loss function that penalizes inaccuracies in describing rare objects more heavily.
D. Apply early stopping to prevent overfitting to the common objects.
E. Increase the batch size during training to improve GPU utilization.

答案：C

解題說明：
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.

問題 #326
You are tasked with optimizing a U-Net model for real-time image segmentation on an embedded device with limited GPU memory. The original model is trained in FP32 precision. Which of the following techniques, when applied together, would likely yield the best trade-off between accuracy and performance?

A. FP16 mixed-precision training, layer fusion to combine multiple operations into one, and increasing the batch size to improve GPU utilization.
B. Quantization Aware Training (QAT) to INT8, Knowledge Distillation from the FP32 model to a smaller student model, and channel pruning to reduce the number of filters.
C. Applying standard post-training quantization to INT8, replacing convolutional layers with fully connected layers, and using a smaller batch size.
D. Weight clustering to reduce model size, pruning low-importance connections, and using a larger learning rate during fine-tuning.
E. Converting all layers to FP16, removing skip connections from the IJ-Net architecture, and using a smaller input image resolution.

答案：B

解題說明：
For embedded devices, aggressive optimization is needed. QAT to INT8 provides significant memory and performance gains, but requires retraining. Knowledge distillation allows training a smaller, faster student model to mimic the original model's behavior, and channel pruning reduces computational cost by removing less important filters. The combination provides the best trade-off. FP16 (B) helps but isn't as aggressive as INT8. Increasing batch size (B) might not be feasible given limited memory. Removing skip connections (D) drastically hurts accuracy. Fully connected layers (E) increase the number of parameters.

問題 #327
You are working with a multimodal dataset containing images and corresponding text descriptions. You want to train a model to generate text descriptions for new images. You decide to use a transformer-based architecture with separate encoders for images and text. How should you effectively fuse the image and text representations to enable cross-modal interaction?

A. Train the image and text encoders separately and then combine their outputs using a linear layer.
B. Multiply the final hidden states of the image and text encoders and feed them into a decoder.
C. Average the final hidden states of the image and text encoders and feed the result into a decoder.
D. Use a cross-attention mechanism where the text decoder attends to the image encoder's hidden states and vice-versa.
E. Concatenate the final hidden states of the image and text encoders and feed them into a decoder.

答案：D

解題說明：
Cross-attention allows the decoder to selectively attend to relevant parts of both the image and text representations, enabling fine- grained interaction between the modalities. Concatenation or averaging simply combines the representations without allowing for selective attention. Training the encoders separately and then combining their outputs doesn't allow for cross modal interaction during training. Multiply operation is not standard and is not efficient.

問題 #328
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最新NCA-GENM題庫 & NCA-GENM考試證照綜述

NVIDIA NCA-GENM考試證照綜述 - NCA-GENM題庫分享

最新的 NVIDIA-Certified Associate NCA-GENM 免費考試真題 (Q323-Q328):

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