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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. GPU-accelerated AI workflows 2. NeMo framework usage
Topic 2: Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations
Topic 3: Multimodal AI Systems	- Multimodal model design - Cross-modal learning 1. Audio-visual understanding 2. Text-image integration
Topic 4: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Supervised and unsupervised learning 2. Neural networks fundamentals
Topic 5: Generative AI Concepts	- Generative models 1. Diffusion models 2. Transformers and LLM basics

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NVIDIA Free NCA-GENM Practice Exams: NVIDIA Generative AI Multimodal - Exams4sures Instant Download

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NVIDIA Generative AI Multimodal Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which of the following techniques is LEAST likely to improve the performance of a Generative A1 model tasked with generating realistic images from text descriptions?

A. Implementing classifier-free guidance during the diffusion process.
B. Using a more powerful generative architecture, such as a Transformer-based diffusion model.
C. Increasing the size of the training dataset with high-quality image-text pairs.
D. Applying data augmentation techniques to the training images, such as random cropping and rotations.
E. Reducing the dimensionality of the text embeddings used as input to the image generator.

Answer: E

Explanation:
Reducing the dimensionality of text embeddings will likely degrade performance, as it removes information that the model needs to accurately generate images. The other options (A, B, C, and E) are all established techniques for improving the quality and fidelity of generated images.

NEW QUESTION # 28
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 simple average of all available reports for imputation.
B. Training the model only on the complete data points and ignoring the incomplete ones.
C. 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.
D. Discarding all data points with missing reports.
E. Imputing the missing reports with a generic placeholder text.

Answer: C

Explanation:
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.

NEW QUESTION # 29
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?

A. Normalize all text data to lowercase and remove punctuation.
B. Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.
C. Resize all images to a uniform size.
D. Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
E. Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.

Answer: A,C,D,E

Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.

NEW QUESTION # 30
You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

A. Decrease the CFG (Classifier-Free Guidance) scale.
B. Reduce the number of inference steps.
C. Use a negative prompt to exclude unwanted elements or styles.
D. Change the random seed to explore different variations.
E. Increase the number of inference steps.

Answer: C,D,E

Explanation:
Increasing the number of inference steps allows the diffusion process to refine the image more thoroughly. Using a negative prompt helps to guide the generation process by specifying what not to include in the image. Changing the random seed allows you to explore different variations of the same prompt, which can lead to more desirable results. Decreasing the CFG scale can reduce adherence to the prompt, and reducing the number of inference steps results in less refined images.

NEW QUESTION # 31
You're working with a multimodal model that fuses text and image features. You've noticed that the model performs poorly when the text and image are semantically misaligned (e.g., an image of a dog and the caption 'a cat on a mat'). Which of the following techniques can help improve the model's robustness to such misalignment?

A. Using only positive text-image pairs for training.
B. Increasing the learning rate during training.
C. Removing dropout layers from the model architecture.
D. Decreasing the batch size.
E. Adding a contrastive loss that penalizes embeddings of misaligned text-image pairs.

Answer: E

Explanation:
A contrastive loss function directly addresses the issue of semantic misalignment by penalizing the model when it produces similar embeddings for text and images that don't correspond semantically. This encourages the model to learn more robust and meaningful feature representations.

NEW QUESTION # 32
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