Sample NCA-GENM Questions Pdf | NCA-GENM Valid Exam Braindumps

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Multimodal Data	15%	- Applications and use cases - Handling and integrating text, image, and audio data
Performance Optimization	10%	- Techniques for optimizing AI performance - Monitoring and improving system efficiency
Software Development & Engineering	15%	- Integration and deployment of multimodal AI systems - Python libraries for multimodal AI
Data Analysis & Visualization	10%	- Data preprocessing and feature engineering - Visualization techniques for multimodal data
Core ML & AI Knowledge	20%	- Key algorithms and techniques - Basic concepts and terminology
Trustworthy AI	5%	- Ensuring fairness and transparency - Ethical considerations in AI development
Experimentation	25%	- A/B testing - Experimental design - Model evaluation and comparison - Hypothesis testing

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NVIDIA Generative AI Multimodal Sample Questions (Q48-Q53):

NEW QUESTION # 48
How does the batch size influence VRAM consumption during inference with ML models on GPUs?

A. The batch size has no impact on VRAM consumption during inference.
B. Decreasing the batch size reduces VRAM consumption.
C. Increasing or decreasing the batch size has the same impact on VRAM consumption.
D. Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.

Answer: B

Explanation:
Batch size has a direct, proportional relationship with VRAM consumption during both training and inference: each sample in a batch requires its own memory allocation for input tensors, intermediate activations at every layer, and output tensors, all of which must reside in GPU memory simultaneously while the batch is being processed. Decreasing the batch size means fewer samples occupy memory concurrently, directly reducing peak VRAM consumption - this is precisely why reducing batch size is one of the first, most common remedies when a model run fails with an out-of-memory (OOM) error on a GPU with limited VRAM.
Option C states the inverse of the correct relationship and is a genuinely important misconception to correct:
increasing batch size increases VRAM consumption, not decreases it - parallelism across the batch means more simultaneous memory occupancy, not less. It's true that larger batches improve GPU compute
*utilization* and *throughput* (better amortizing fixed kernel-launch overhead and better exploiting parallel hardware) up to the point VRAM allows, but that throughput benefit is a separate effect from, and does not reduce, memory consumption. Options A and B both incorrectly claim batch size is memory-neutral, when it is in fact one of the most direct, easily controlled levers for managing VRAM usage - alongside model precision (quantization, mixed precision) and activation checkpointing, covered in the Performance Optimization domain elsewhere in this set.
Reference: Performance Optimization domain - batch size, VRAM/memory management, inference-time resource tradeoffs.

NEW QUESTION # 49
Consider a scenario where you are using a pre-trained multimodal model for image captioning and want to fine-tune it on a specific dataset. Which of the following strategies is MOST likely to lead to improved performance and faster convergence?

A. Fine-tune the entire model with a smaller learning rate and gradually unfreeze layers, starting from the captioning head.
B. Fine-tune only the captioning head (language model) while keeping the image encoder frozen.
C. Randomly initialize the entire model and train from scratch.
D. Fine-tune the entire model (image encoder and captioning head) with a very large learning rate.
E. Train a new captioning head from scratch while keeping the image encoder frozen.

Answer: A

Explanation:
Fine-tuning the entire model with a smaller learning rate allows the model to adapt to the specific dataset without drastically changing the pre-trained weights. Gradually unfreezing layers helps prevent overfitting and accelerates convergence. Freezing the image encoder may limit the model's ability to adapt to the specific image features in the dataset. Randomly initializing the entire model negates the benefits of pre-training.

NEW QUESTION # 50
A self-driving car uses multimodal data (camera images, LiDAR point clouds, radar data, and GPS information) to navigate. The LiDAR sensor occasionally fails, resulting in missing point cloud dat a. How should the system be designed to handle this sensor failure gracefully and maintain safe navigation?

A. Rely solely on the camera images and ignore the missing LiDAR data.
B. Use a sensor fusion technique that prioritizes the available modalities (camera, radar, GPS) and estimates the missing LiDAR data based on these modalities.
C. Immediately stop the car until the LiDAR sensor is fixed.
D. Switch to a pre-programmed route that does not require LiDAR data.
E. Employ a Kalman filter to predict the LiDAR point cloud based on the previous sensor readings and the car's motion model.

Answer: B,E

Explanation:
Stopping the car or relying solely on a single modality is not a robust solution. Using sensor fusion to prioritize available modalities and estimate missing data allows the system to continue navigating safely. A Kalman filter is a specific technique for estimating the state of a system (in this case, the LiDAR point cloud) based on noisy sensor readings and a motion model.

NEW QUESTION # 51
Assume you have trained a text-to-image diffusion model using a large dataset of landscape photographs. You now want to adapt this model to generate images of photorealistic portraits. Which of the following fine-tuning strategies is most likely to yield the best results with the least amount of training data and time?

A. Only fine tune the final layer of the IJ-Net model with the portrait dataset.
B. Fine-tune only the CLIP model with portrait-related text descriptions and corresponding images.
C. Fine-tune both the CLIP model and the U-Net architecture with the portrait dataset, using a smaller learning rate than the initial training.
D. Fine-tune the IJ-Net architecture of the diffusion model while keeping the CLIP model fixed.
E. Retrain the entire diffusion model from scratch using the portrait dataset.

Answer: C

Explanation:
Fine-tuning both the CLIP model and the IJ-Net architecture is the most effective approach. The CLIP model needs to learn the semantic relationship between portrait-related text and images, and the U-Net needs to adapt to generating portraits instead of landscapes. Using a smaller learning rate prevents overfitting and allows the model to leverage its existing knowledge from the landscape dataset. Retraining from scratch is wasteful, and fine-tuning only one component may not be sufficient for good performance. Simply fine-tuning the last layer will not change much.

NEW QUESTION # 52
You're building a real-time voice cloning application using NVIDIA Riv
a. You need to ensure high-quality synthesized speech with minimal latency. Which of the following Riva configurations would provide the BEST trade-off between quality and speed?

A. Using only the open source implementation and not NVIDIA Riva to implement a Voice Cloning application
B. Using a large, transformer-based text-to-speech model with aggressive quantization and pruning, deployed on a cloud-based TPIJ instance.
C. Using a pre-trained, open-source text-to-speech model and a CPU-based vocoder, optimized for minimal memory footprint.
D. Using a large, high-capacity Tacotron 2 text-to-speech model and a high-resolution WaveGlow vocoder, deployed on a single, low-power GPU.
E. Using a smaller, faster FastSpeech text-to-speech model and a parallel WaveGAN vocoder, deployed on a multi-GPU server with TensorRT optimization enabled.

Answer: E

Explanation:
FastSpeech and parallel WaveGAN offer a good balance of speed and quality. Multi-GPU deployment and TensorRT optimization further reduce latency. Tacotron 2 and WaveGlow (A) provide higher quality but are slower. CPU-based vocoders (C) are generally too slow for real-time applications. TPIJs (D) are not directly supported by Riva. (E) does not leverage Riva features. Riva's TensorRT optimizatins would be ideal here. The best option would be a faster TTS model along with a Vocoder for real time synthesis.

NEW QUESTION # 53
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