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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Multimodal Data	15%	- Handling and integrating text, image, and audio data - Applications and use cases
Data Analysis & Visualization	10%	- Visualization techniques for multimodal data - Data preprocessing and feature engineering
Trustworthy AI	5%	- Ensuring fairness and transparency - Ethical considerations in AI development
Experimentation	25%	- Experimental design - Model evaluation and comparison - Hypothesis testing - A/B testing
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
Core ML & AI Knowledge	20%	- Key algorithms and techniques - Basic concepts and terminology

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NVIDIA Generative AI Multimodal Sample Questions (Q44-Q49):

NEW QUESTION # 44
You are tasked with evaluating the trustworthiness of a multimodal A1 model that predicts diagnoses based on medical images and patient history text. Which of the following evaluation metrics or techniques are MOST relevant for assessing the model's trustworthiness in this critical application?

A. Measuring inference throughput (samples per second).
B. Robustness testing by introducing adversarial perturbations to the input data.
C. Calibration error, measuring the alignment between predicted probabilities and actual outcomes.
D. Attribution methods (e.g., Grad-CAM) to visualize which parts of the image and text the model focuses on.
E. Accuracy and F1-score on a held-out test set.

Answer: B,C,D

Explanation:
Trustworthiness goes beyond simple accuracy. Calibration error assesses how well the model's predicted probabilities reflect the true likelihood of the diagnosis. Attribution methods provide insights into the model's reasoning process, helping to identify potential biases or reliance on irrelevant features. Robustness testing assesses the model's sensitivity to noise and adversarial attacks, which can indicate vulnerability to manipulation. Accuracy and Fl-score are important but insufficient for trustworthiness. Throughput is a performance metric, not a trustworthiness metric.

NEW QUESTION # 45
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model takes an image with masked regions as input and outputs the completed image. You need to pre-process the input image before sending it to the server.
Which pre-processing steps are crucial for ensuring optimal performance and accuracy of the inpainting model?

A. Creating a binary mask indicating the regions to be inpainted.
B. Applying data augmentation techniques like random rotations and flips.
C. Converting the image to grayscale.
D. Sharpening the image to enhance details.
E. Resizing the image to a fixed resolution and normalizing pixel values to a specific range (e.g., [0, 1] or [-1, 1]).

Answer: A,E

Explanation:
Resizing and normalization ensure the input image conforms to the model's expected input size and data range. A binary mask is essential to indicate which regions the model should inpaint. Converting to grayscale would remove color information, hindering the inpainting process. Data augmentation should be done during the training phase. Sharpening is not typically a necessary pre-processing step for inpainting.

NEW QUESTION # 46
How does the batch size influence VRAM consumption during inference with ML models on GPUs?

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

Answer: D

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 # 47
You are developing a multimodal model that combines time-series data from sensor readings with natural language descriptions of events. The time-series data has varying sampling rates and the text descriptions are often vague and ambiguous. How would you best address the challenge of aligning and fusing these two modalities to improve model performance?

A. Ignore the time-series data and train the model only on the text descriptions.
B. Train separate models for time-series and text and average their predictions.
C. Resample the time-series data to a uniform sampling rate and directly concatenate it with the text embeddings.
D. Average the time-series data over a fixed time window and concatenate it with the text embeddings.
E. Use a dynamic time warping (DTW) algorithm to align the time-series data with the text descriptions and then use a cross-modal attention mechanism for fusion.

Answer: E

Explanation:
DTW helps align time-series data with varying lengths and temporal distortions to text. Cross-modal attention then effectively fuses the aligned modalities, allowing the model to learn relationships between them. Resampling and direct concatenation (A) doesn't account for temporal variations. Ignoring data (B) is counterproductive. Averaging (D) loses temporal information. Averaging separate model outputs (E) is a form of late fusion and less effective than joint learning after alignment.

NEW QUESTION # 48
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

A. Reducing the amount of training data
B. Using data augmentation techniques.
C. Implementing dropout layers.
D. Adding Ll or L2 regularization.
E. Increasing the complexity of the model.

Answer: B,C,D

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.

NEW QUESTION # 49
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