Free PDF Quiz NVIDIA - NCA-GENM Useful Exam Papers

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q13-Q18):

NEW QUESTION # 13
Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?

A. The learning rate is set too high.
B. The text data is not in IJTF-8 encoding.
C. The system doesn't have CUDA drivers installed.
D. The image files are corrupted or in an unsupported format.
E. The batch size is too large.

Answer: D

Explanation:
Since the error occurs specifically during the image loading step, the most likely cause is related to the image files themselves. Corrupted files or unsupported formats would prevent the image loading library from successfully reading the images. The other options are less likely to cause an error specifically during image loading.

NEW QUESTION # 14
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?

A. Using only pretrained model weights.
B. Use a larger vocabulary for the text encoder.
C. Increase the frame rate of the training videos.
D. Decrease the resolution of the video frames.
E. Implement a contrastive learning objective that encourages similar embeddings for corresponding video frames and text descriptions.

Answer: E

Explanation:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.

NEW QUESTION # 15
In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

A. It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
B. It ensures that AI systems are transparent in their decision-making processes.
C. It requires AI systems to be developed with an ethical consideration for societal impacts.
D. It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

Answer: A

Explanation:
Within Trustworthy AI frameworks, "Certification" is best understood as the formal verification process confirming that an AI system meets defined standards of fitness-for-purpose - whether those standards are set by regulatory bodies, industry consortia, or internal governance frameworks - for the specific context in which the system will be deployed. This is distinct from, though related to, the broader Trustworthy AI principles of ethics (option A), transparency (option B), and legal compliance (option C): certification is the
*verification mechanism* that attests a system satisfies applicable standards, rather than being one of those underlying values itself.
The distinction between C and D is subtle and worth being precise about: C describes compliance as an obligation ("must follow laws and regulations"), while D describes certification as a verification activity ("confirming fitness according to standards") - certification is the audit/attestation process, and compliance is one of the things that process may confirm. A system can be legally compliant without having undergone formal certification, and certification processes often assess criteria broader than legal compliance alone, including performance benchmarks, robustness testing, and domain-appropriate validation (e.g., clinical validation standards for a medical imaging model).
In practice, certification connects Trustworthy AI to concrete deployment gates: a healthcare AI model, for instance, may require certification against medical device standards before clinical use - the verification step, not merely the legal requirement, is the "Certification" principle's substance.
Reference: Trustworthy AI domain - certification, compliance, and verification as distinct governance mechanisms.

NEW QUESTION # 16
You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model's performance with audio data?

A. Decrease the weight of the audio modality in the loss function.
B. Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.
C. Increase the learning rate for the audio modality during training.
D. Use transfer learning by pre-training the audio component of the model on a large audio dataset.
E. Reduce the dimensionality of the audio features to simplify the learning task.

Answer: B,D

Explanation:
Data augmentation (C) increases the robustness of the model to variations in audio, including noise. Transfer learning (E) allows the model to leverage knowledge from a large, pre-existing audio dataset, improving its initial performance.

NEW QUESTION # 17
You are building a multimodal application that takes an image and a short text description as input and generates a more detailed text description of the image. Which of the following model architectures is BEST suited for this task?

A. A Generative Adversarial Network (GAN) with separate image and text encoders.
B. A Recurrent Neural Network (RNN) with attention mechanisms.
C. A Vision Transformer (ViT) for image encoding and a Transformer for text decoding.
D. A simple CNN followed by an LSTM.
E. A Multilayer Perceptron (MLP) for both image and text.

Answer: C

Explanation:
A Vision Transformer (ViT) excels at encoding image information, and a Transformer architecture is highly effective for text generation. The combination allows for effective processing of both modalities and generation of coherent, detailed text descriptions based on the image content and initial text prompt. CNN+LSTM could work, but is generally less performant. RNNs struggle with long-range dependencies. GANs are not ideal for this specific text generation task. MLPs don't capture the sequential dependencies well.

NEW QUESTION # 18
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