Free NVIDIA NCA-GENM Exam Dumps Questions & Answers

You are building an A1 model that takes video and corresponding subtitles as input to generate short summaries of video content. Which of the following strategies are most important to reduce the chance of your model generating biased summaries? (Select all that apply)

• A.  Randomly shuffle data during training.
• B.  Use a pre-trained language model that has been debiased.
• C.  Ensure the training dataset contains diverse representation of all demographic groups and viewpoints.
• D.  Evaluate the model's summaries on different demographic groups to identify and mitigate any disparities in performance.
• E.  Increase the number of training epochs.

Which of the following techniques is MOST suitable for aligning the feature spaces of text and images in a multimodal model?

• A.  Only using image data during the training process.
• B.  Concatenating the features from the text and image encoders without any further processing.
• C.  Using separate loss functions for text and image encoders.
• D.  Employing a contrastive loss function that encourages similar representations for semantically related text and images.
• E.  Training the text and image encoders independently.

You are building a multimodal AI system that generates 3D models of furniture from text descriptions and a few 2D images of similar furniture pieces. The system uses separate encoders for text and images. You want to fuse the information from both modalities effectively. Which TWO of the following fusion techniques would be the most appropriate for this task, considering the different nature of the text and image data?

• A.  Simple concatenation of the text and image embeddings before feeding them into the decoder.
• B.  Applying element-wise addition of the text and image embeddings.
• C.  Using a cross-attention mechanism where the text embedding attends to the image features, and vice-versa, allowing the model to dynamically weight the importance of different parts of each modality.
• D.  Using a gating mechanism (e.g., a learned weight) to control the contribution of the text and image embeddings based on the input.
• E.  Performing a simple average of the text and image embeddings.

When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

• A.  Use a larger UNet architecture within the Diffusion model.
• B.  Train the GAN for a longer duration.
• C.  Increase the number of diffusion steps.
• D.  Employ distillation techniques to train a faster, smaller model.
• E.  Use a smaller batch size.

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