EXAM NCA-GENM SUCCESS & PDF NCA-GENM EXAM DUMP

Exam NCA-GENM Success & Pdf NCA-GENM Exam Dump

Exam NCA-GENM Success & Pdf NCA-GENM Exam Dump

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NVIDIA Generative AI Multimodal Sample Questions (Q245-Q250):

NEW QUESTION # 245
You are fine-tuning a pre-trained multimodal model for a new task. You have limited computational resources. Which of the following fine-tuning strategies would be the MOST computationally efficient while still achieving good performance?

  • A. Freeze the lower layers of the model and fine-tune the upper layers and the classification head.
  • B. Train a new random model from scratch for the task, which will avoid the need to load the pre-trained model.
  • C. Fine-tune all the layers of the model.
  • D. Freeze all layers except the classification head and fine-tune only the classification head.
  • E. Randomize the model to train, if it improves the training rate.

Answer: A

Explanation:
Freezing the lower layers and fine-tuning the upper layers and classification head strikes a balance between computational efficiency and performance. The lower layers typically capture more general features that are less specific to the task, while the upper layers capture more task-specific features. Freezing the lower layers reduces the number of trainable parameters, making the fine-tuning process more computationally efficient. Fine-tuning all layers is computationally expensive, freezing all layers except the classification head might not be sufficient for adapting to the new task, and training from scratch does not leverage the knowledge learned during pre-training. Randomizing model is not a general practice.


NEW QUESTION # 246
A research team has developed a novel multimodal model that fuses text, image, and audio dat a. They want to quantitatively evaluate the model's performance in comparison to several existing state-of-the-art models. Which of the following evaluation metrics would be MOST appropriate to assess the model's ability to generate coherent and relevant text descriptions based on the combined multimodal input?

  • A. Perplexity.
  • B. Inception Score.
  • C. Frechet Inception Distance (FID).
  • D. BLEU (Bilingual Evaluation Understudy) and ROIJGE (Recall-Oriented Understudy for Gisting Evaluation).
  • E. Structural Similarity Index Measure (SSIM).

Answer: D

Explanation:
BLEU and ROUGE are standard metrics for evaluating text generation tasks by comparing the generated text to reference texts. They assess the similarity and overlap in terms of n-grams. Perplexity measures the uncertainty of a language model. Inception Score and FID are used for evaluating image generation quality. SSIM measures the similarity between two images.


NEW QUESTION # 247
You are building a multimodal model to predict stock prices using financial news articles (text), historical stock prices (time-series), and company logos (images). You have preprocessed the data and are ready to train your model. Which of the following architectures would be MOST suitable for effectively integrating these three modalities?

  • A. A model that uses a Transformer encoder for each modality, followed by a shared Transformer decoder for prediction, enabling cross-modal attention at the decoder level.
  • B. A simple feed forward neural network with concatenated features from all modalities.
  • C. A model that converts all data into a single text format and uses a large language model (LLM) for prediction.
  • D. A model that combines a Transformer for text, an LSTM for time-series, and a CNN for images, with a late fusion strategy using a weighted averaging of predictions.
  • E. Separate models for each modality trained independently, and then ensembled together at the prediction stage.

Answer: A,D

Explanation:
Combining a Transformer for text, an LSTM for time-series, and a CNN for images with a late fusion approach allows each modality to be processed by a suitable architecture and then combined to generate a final prediction. Using transformers in each modality with shared Transformer decoder can efficiently integrate and predict stock prices using cross modal attention . A simple feedforward network is unlikely to capture the temporal dependencies in the time-series data or the complex relationships between modalities. Ensembling independent models doesn't allow for cross-modal learning. Converting all data into text might lose valuable information from the other modalities. Therefore, hybrid architecture combining transformers, LSTMs, and CNNs with cross-modal attention or late fusion would be most effective.


NEW QUESTION # 248
You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

  • A. Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.
  • B. Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
  • C. Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.
  • D. Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.
  • E. Only utilize pre-trained open-source models

Answer: A,C,D

Explanation:
Differential privacy (A) adds noise to the data to protect individual records. On-premises deployment (B) maintains control over data access. Federated learning (D) trains the model on decentralized data without centralizing it. Avoiding client data entirely (C) may limit the model's effectiveness. NVIDIA Merlin and FLARE are tools that provide methods to create safe and private architecture. (E) is not always the best approach since the model might be very generalized and not adapted to specific tasks.


NEW QUESTION # 249
Consider a multimodal emotion recognition system that uses both facial expressions (images) and speech (audio). You want to fuse the information from these two modalities at the decision level. Which of the following techniques would be MOST suitable for decision-level fusion?

  • A. Train separate classifiers for images and audio, then use a weighted average of their output probabilities based on the confidence scores of each classifier.
  • B. Train separate classifiers for images and audio, then average their output probabilities for each emotion class.
  • C. Train separate classifiers for images and audio, then use the output of the image classifier as input to the audio classifier-
  • D. Train a single transformer to process both images and audio in sequence.
  • E. Concatenate the feature vectors extracted from the images and audio, then train a single classifier.

Answer: A

Explanation:
Weighted averaging allows you to give more weight to the modality that is more reliable or confident in its prediction for a given input. Simply averaging treats all modalities equally. Concatenation is feature-level fusion. The image classifier as input to audio classifier is a specific cascade approach. Using a single transformer is possible, but less common for decision fusion specifically. It is feature level fusion.


NEW QUESTION # 250
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