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NCA-GENM Buch - NCA-GENM Fragen Beantworten

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NVIDIA Generative AI Multimodal NCA-GENM Prüfungsfragen mit Lösungen (Q304-Q309):

304. Frage
You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?

A. Increasing the number of layers in the CNN to extract more detailed visual features.
B. Reducing the frame rate of the input video to reduce the temporal complexity.
C. Using a smaller batch size during training to reduce memory consumption.
D. Incorporating a Transformer-based architecture, such as a Vision Transformer (ViT) for visual feature extraction and a standard Transformer for sequence generation.

Antwort: B

Begründung:
Transformers are known for their ability to capture long-range dependencies due to their self-attention mechanism. Replacing LSTMs with Transformers allows the model to attend to relevant parts of the video sequence regardless of their temporal distance. While CNNs can extract visual features, they don't inherently address long-range dependencies. RNNs are prone to vanishing gradients, making it difficult to learn long- range dependencies. Reducing the frame rate or batch size doesn't directly address the issue of capturing long-range dependencies within the video sequence.

305. Frage
You are working with a large multimodal dataset containing images and text. You want to efficiently load and preprocess this data for training a generative A1 model on an NVIDIA GPU. Which of the following approaches would be most effective for maximizing data loading speed and GPU utilization?

A. Loading the entire dataset into CPU memory before starting training.
B. Storing the images and text in a relational database and querying the database during training.
C. Compressing the dataset into a single large archive file and decompressing it on the fly during training.
D. Using a Python-based data loader that reads images and text directly from disk during training.
E. Employing NVIDIA's DALI (Data Loading Library) to perform data loading and preprocessing on the GPU.

Antwort: E

Begründung:
NVIDIA DALI is specifically designed for accelerating data loading and preprocessing on NVIDIA GPUs. It allows you to perform tasks like image decoding, resizing, and data augmentation directly on the GPIJ, minimizing CPIJ overhead and maximizing GPU utilizatiom Loading the entire dataset into CPU memory is impractical for large datasets. Python-based data loaders can be slow due to the GIL (Global Interpreter Lock). Querying a relational database adds overhead. Compressing the dataset can save storage space but may introduce decompression bottlenecks during training.

306. Frage
You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?

A. Increasing the model size by adding more layers.
B. Training the model on a smaller dataset.
C. Removing dropout layers.
D. Reducing the temperature parameter during generation.
E. Implementing a retrieval-augmented generation (RAG) approach.

Antwort: E

Begründung:
Retrieval-augmented generation (RAG) involves retrieving relevant information from an external knowledge source (e.g., a database or a collection of documents) and incorporating it into the model's generation process. This can help the model generate more factually accurate and coherent outputs by grounding its responses in external knowledge. Increasing the model size might improve performance, but doesn't guarantee factual accuracy. Reducing temperature makes the output more predictable but doesn't address factual correctness. Smaller datasets and removing dropout would likely worsen performance.

307. Frage
Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?

A. Applying Batch Normalization to the image features.
B. Applying standard image augmentation techniques to the image data.
C. Using a learning rate scheduler to adjust the learning rate during training-
D. Padding or truncating text sequences to a fixed length.
E. Using the exact same length of text and same images.

Antwort: D

Begründung:
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.

308. Frage
You are working with a multimodal dataset containing medical images (X-rays) and corresponding patient reports (text). Some of the reports are missing or incomplete. Which of the following strategies would be most appropriate to handle this missing data in a multimodal AI model?

A. Using a multimodal autoencoder to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.
B. Using a simple average of all available reports for imputation.
C. Training the model only on the complete data points and ignoring the incomplete ones.
D. Discarding all data points with missing reports.
E. Imputing the missing reports with a generic placeholder text.

Antwort: A

Begründung:
Using a multimodal autoencoder or a masked language model allows the model to leverage the relationship between the image and text modalities to infer the missing information. Discarding data or using simple imputation methods can lead to information loss or biased results. A multimodal autoencoder or masked language model can help to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.

309. Frage
......

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Ihre Aufbage: Egal welche Version für NCA-GENM Prüfung Sie wählen, was Sie nach dem Kauf tun müssen, ist, durch fleißiges Lernen alle richtigen Antworten im Fragenbogen im Kopf zu behalten.

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