VALID NCA-GENM EXAM QUESTIONS - RELIABLE TEST NCA-GENM TEST

Valid NCA-GENM Exam Questions - Reliable Test NCA-GENM Test

Valid NCA-GENM Exam Questions - Reliable Test NCA-GENM Test

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Tags: Valid NCA-GENM Exam Questions, Reliable Test NCA-GENM Test, Reliable NCA-GENM Exam Practice, Practice Test NCA-GENM Fee, NCA-GENM Valid Exam Pdf

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>> Valid NCA-GENM Exam Questions <<

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NVIDIA Generative AI Multimodal Sample Questions (Q361-Q366):

NEW QUESTION # 361
Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?

  • A. Using batch normalization.
  • B. Increasing the learning rate.
  • C. Employing skip connections (e.g., in ResNets).
  • D. Using sigmoid activation functions.
  • E. Using ReLU (Rectified Linear Unit) activation functions.

Answer: A,C,E

Explanation:
ReLU avoids saturation like sigmoid, helping gradients flow. Skip connections provide alternative pathways for gradients. Batch normalization stabilizes learning and can help mitigate vanishing gradients. Increasing learning rate is unrelated, and sigmoid exacerbates the problem due to saturation.


NEW QUESTION # 362
You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?

  • A. Apply aggressive data augmentation techniques, such as random rotations, flips, and crops.
  • B. Train a very deep convolutional neural network from scratch-
  • C. Reduce the image resolution to decrease the number of parameters in the model.
  • D. Use a simple linear classifier.
  • E. Use a pre-trained convolutional neural network on a large dataset like ImageNet and fine-tune it on your flower dataset

Answer: E

Explanation:
Transfer learning, specifically fine-tuning a pre-trained model, is highly effective when dealing with small datasets. Pre-trained models have already learned useful features from large datasets, and fine-tuning them allows the model to adapt to the specific characteristics of your flower dataset. Training a deep network from scratch with limited data will likely lead to overfitting. Data augmentation helps, but transfer learning is generally more impactful. Reducing image resolution might lose important details, and a linear classifier might be too simple to capture the complexity of image features.


NEW QUESTION # 363
Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?

  • A. Chi-squared test
  • B. Spearman's rank correlation coefficient
  • C. Krippendorff's Alpha
  • D. Pearson correlation coefficient
  • E. Cohen's Kappa

Answer: C

Explanation:
Krippendorff's Alpha is the most appropriate measure when dealing with multiple annotators, ordinal data (severity scores on a scale), and potential for missing data. While Cohen's Kappa is suitable for two annotators, Krippendorff's Alpha generalizes to multiple annotators. Spearman's rank correlation is better suited for assessing the monotonic relationship between variables, but not agreement among raters. Pearson correlation requires interval data, while chi-squared is for categorical.


NEW QUESTION # 364
Consider the following code snippet used for evaluating a Generative Adversarial Network (GAN):

What does the code snippet calculate, and what do 'images1' and "images2 represent in the context of GAN evaluation?

  • A. Calculates the Inception Score; 'images1' and 'images2 represent copyright images respectively.
  • B. Calculates the Kernel Inception Distance (KID); 'images1' and 'images2 represent copyright images respectively.
  • C. Calculates the Structural Similarity Index (SSIM); 'images1 and 'images2 represent copyright images respectively.
  • D. Calculates the Peak Signal-to-Noise Ratio (PSNR); 'images1 and 'images2 represent copyright images respectively.
  • E. Calculates the Frechet Inception Distance (FID); 'images1' and 'images2 represent copyright images respectively.

Answer: E

Explanation:
The code snippet calculates the Frechet Inception Distance (FID). FID measures the distance between the distributions of real and generated images, with lower FID scores indicating better image quality and diversity, and 'images1 represent feature vectors extracted from copyright images using an Inception network (or a similar feature extractor). The code calculates the mean (mu) and covariance (sigma) of these feature vectors and uses them to compute the FID score. The other options represent different image quality metrics. The Inception Score assesses the quality and diversity of generated images but does not compare them to real images directly.


NEW QUESTION # 365
You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?

  • A. Treat the image and text as separate models and explain each independently.
  • B. Represent both the image and text as numerical vectors and then apply a standard SHAP explainer.
  • C. Use a multimodal SHAP implementation that is designed to handle both image and text features simultaneously, considering their interaction.
  • D. Use DeepExplainer for the image component and a simple linear SHAP explainer for the text.
  • E. Apply KernelSHAP separately to the image and text, then combine the results.

Answer: C

Explanation:
The best approach is to use a multimodal SHAP implementation that considers the interaction between image and text features. This ensures a holistic explanation of the model's prediction based on both modalities. Treating them separately or simply concatenating features ignores potential synergistic effects.


NEW QUESTION # 366
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