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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q28-Q33):

NEW QUESTION # 28
In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

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

Answer: C

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 # 29
You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

A. Deploying Riva on a single CPU core with a small batch size.
B. Deploying Riva on a single GPU with a large batch size.
C. Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.
D. Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.
E. Deploying Riva on CPU using multiprocessing.

Answer: C

Explanation:
For high-throughput, low-latency applications, deploying Riva across multiple GPUs using Triton Inference Server is optimal. Triton enables dynamic batching, which groups incoming requests to maximize GPU utilization, and allows for scaling across multiple GPUs to handle increased load. Riva leverages gRPC to communicate with Triton.

NEW QUESTION # 30
You're building a system that uses a pre-trained large language model (LLM) for generating creative stories. After deploying the system, you notice that the generated stories often contain biases present in the training data of the LLM. What are the MOST effective strategies to mitigate these biases in your generated stories? (Select TWO)

A. Fine-tune the LLM on a diverse and representative dataset.
B. Apply bias detection and mitigation techniques to the LLM's output.
C. Reduce the size of the LLM to minimize memory usage.
D. Increase the temperature parameter in the LLM's decoding strategy.
E. Use prompt engineering to steer the LLM away from biased outputs.

Answer: B,E

Explanation:
Bias detection and mitigation techniques can be applied to the LLM's output to identify and remove or modify biased content. Prompt engineering involves carefully crafting prompts to guide the LLM towards generating more balanced and unbiased stories. Fine-tuning on a diverse dataset is beneficial in the long run but is a more resource-intensive approach and doesn't guarantee the complete elimination of biases. Increasing the temperature parameter might lead to more creative but also more unpredictable and potentially biased outputs. Reducing the size of the LLM doesn't address the bias problem and might even worsen it by limiting the model's ability to capture nuanced relationships.

NEW QUESTION # 31
You are tasked with generating realistic images of human faces using a GAN. However, you notice that the generated images often contain artifacts, such as distorted facial features or unrealistic textures. Which of the following techniques would be most effective in improving the realism and quality of the generated faces?

A. Using a smaller batch size.
B. Employing a StyleGAN architecture with adaptive instance normalization (AdalN) and mapping network.
C. Using a simpler discriminator architecture.
D. Applying L1 regularization to the generator's weights.
E. Training the GAN for fewer epochs.

Answer: B

Explanation:
StyleGAN architecture, with its AdalN and mapping network, is specifically designed to control and manipulate the style attributes of generated images, leading to more realistic and high-quality outputs, particularly for complex structures like human faces. AdalN helps in normalizing feature statistics based on style codes, enabling fine-grained control over the visual appearance.

NEW QUESTION # 32
You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?

A. Converting the video footage to grayscale to simplify feature extraction.
B. All of the above.
C. Normalizing the time-series data to a consistent range.
D. Synchronizing the timestamps of the time-series data and video frames.
E. Downsampling the video footage to reduce computational cost.

Answer: A

Explanation:
All the mentioned steps are crucial. Synchronizing timestamps is essential for temporal alignment. Normalizing time-series data ensures features are on the same scale, preventing bias. Downsampling video reduces computational burden, and grayscale conversion simplifies feature extraction without losing vital information for anomaly detection.

NEW QUESTION # 33
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