Free NVIDIA NCA-GENM Exam Dumps Questions & Answers

You're developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?

• A.  Reduce the dimensionality of the image features before fusion.
• B.  Increase the batch size for each epoch.
• C.  Apply modality-specific dropout to the image pathway.
• D.  Oversample the audio data during training.
• E.  Increase the learning rate for the audio modality pathway during training.

Consider the following code snippet intended to generate an image embedding using CLIP. What is the most likely reason for the 'RuntimeErroN?

• A.  The image tensor does not require gradient calculation.
• B.  The CLIP model was not properly loaded onto the GPIJ.
• C.  The image pixel values are not normalized correctly.
• D.  The image is not in RGB format.
• E.  The image size is not compatible with the CLIP model's input requirements.

You're tasked with building a generative A1 model for music composition. You have a large dataset of MIDl files, but the data is inconsistent in terms of tempo, key, and instrumentation. What are the crucial data transformation steps needed before training the model?

• A.  Normalizing the tempo of all MIDl files to a standard BPM.
• B.  Standardizing the instrumentation by mapping different instrument patches to a predefined set.
• C.  Transposing all MIDl files to the same key (e.g., C major/A minor).
• D.  Rescaling the MIDl note velocities to a uniform range.
• E.  Converting all MIDl files to MP3 format.

You are tasked with building a Generative A1 model to generate realistic images of outdoor scenes. The training dataset contains a large number of images with varying lighting conditions, weather conditions, and object compositions. Which data augmentation techniques would be MOST effective in improving the model's robustness and generalization ability?

• A.  Random cropping and resizing.
• B.  Vertical flipping only
• C.  Color jittering (brightness, contrast, saturation, hue), adding Gaussian noise, and random perspective transformations.
• D.  Applying a fixed rotation of 90 degrees to all images.
• E.  Horizontal flipping only.

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