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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q14-Q19):

NEW QUESTION # 14
You are fine-tuning a pre-trained Generative A1 model for a specific downstream task with limited dat a. The model starts to exhibit catastrophic forgetting of the original pre-trained knowledge. Which technique could mitigate this issue effectively?

A. Adding more data augmentation
B. Elastic Weight Consolidation (EWC)
C. Increasing the learning rate significantly
D. Using a smaller batch size
E. Freezing the weights of earlier layers in the network

Answer: B,E

NEW QUESTION # 15
You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A. Using prompts consisting only of keywords related to the desired image.
B. Using negative prompts to explicitly exclude unwanted features or styles.
C. Using random prompts to explore the model's creative capabilities.
D. Using short, concise prompts to minimize ambiguity.
E. Using overly verbose and descriptive prompts to maximize detail.

Answer: B

Explanation:
Negative prompting is a powerful technique where you specify what you don't want in the generated image. This helps refine the output and steer the model away from undesirable artifacts or styles. For example, specifying "a futuristic city, but without flying cars".

NEW QUESTION # 16
You are building a multimodal application that analyzes images and generates descriptive captions. The application needs to handle noisy images and maintain caption consistency. Which of the following techniques would be MOST effective in achieving this?

A. Employing a denoising autoencoder to clean the images followed by a transformer-based captioning model and using beam search with consistency constraints during caption generation.
B. Directly feeding noisy images into a standard image captioning model.
C. Preprocessing the images using a simple Gaussian blur before feeding them into the captioning model.
D. Using a smaller, less complex captioning model to avoid overfitting to the noise.
E. Increasing the learning rate of the captioning model during training to compensate for the noise.

Answer: A

Explanation:
Option C is the most effective. Denoising autoencoders can effectively remove noise. Transformer models are state-of-the-art for captioning. Beam search with consistency constraints helps generate more coherent captions, which addresses the consistency requirement. Other options are either too simplistic or could worsen the problem (e.g., increasing learning rate with noise).

NEW QUESTION # 17
A multimodal dataset consists of video footage of human actions and corresponding wearable sensor data (accelerometer, gyroscope). The goal is to predict the type of action being performed. However, the sensor data is noisy and often misaligned with the video frames. Consider the following code snippet designed to synchronize and clean the sensor data:
What is the primary purpose of the 'resample' function in this code, and what potential issues might arise from using a simple aggregation method during resampling?

A. The 'resample' function increases the sensor data frequency. Using .mean()' is only useful if there is no noise in the sensor data
B. The 'resample' function aligns the sensor data to the video frame rate. Using is appropriate as it averages out the noise in the sensor data.
C. The 'resample' function decreases the video framerate to the rate of the sensor. Using .mean()' is only useful if there is no noise in the sensor data
D. The 'resample' function aligns the sensor data to the video frame rate. Using '.mean()' might smooth out important peaks and valleys in the sensor data, potentially losing crucial information.
E. The 'resample' function filters the sensor data and .mean() only returns the most relevant sensor data

Answer: D

Explanation:
The 'resample' function aligns the sensor data to the video frame rate, making the data streams compatible for analysis. However, using .mean()' during resampling can smooth out critical features in the time-series data, potentially leading to a loss of important information for action recognition. More sophisticated resampling techniques (e.g., interpolation, or using a median value) might be more appropriate.

NEW QUESTION # 18
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. Training the model only on the complete data points and ignoring the incomplete ones.
B. Discarding all data points with missing reports.
C. Imputing the missing reports with a generic placeholder text.
D. Using a simple average of all available reports for imputation.
E. 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.

Answer: E

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 # 19
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