Study NVIDIA NCA-GENM Plan | NCA-GENM Valid Test Syllabus

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA NCA-GENM Valid Test Syllabus - New NCA-GENM Test Materials

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NVIDIA Generative AI Multimodal Sample Questions (Q11-Q16):

NEW QUESTION # 11
In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

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

Answer: D

NEW QUESTION # 12
You're building a multimodal model to generate captions for videos. You've noticed that your model struggles to capture temporal relationships and sequential dependencies in the video frames. Which of the following architectures or techniques would be BEST suited to address this?

A. A Multilayer Perceptron (MLP) trained on flattened video frames.
B. A standard Convolutional Neural Network (CNN) applied independently to each frame.
C. Principal Component Analysis (PCA) to reduce dimensionality of each frame before feeding to the decoder
D. A 3D Convolutional Neural Network (3D CNN) that processes multiple frames as a volume.
E. A combination of a CNN to extract features from individual frames, followed by a Recurrent Neural Network (RNN) like LSTM or GRU to model temporal dependencies between the extracted features.

Answer: E

Explanation:
RNNs, specifically LSTMs and GRUs, are designed to handle sequential data and capture temporal relationships. Combining a CNN for feature extraction with an RNN allows the model to process individual frames and then model the dependencies between them. 3D CNNs can also capture temporal information, but can be computationally expensive. Other options don't explicitly address the temporal aspect.

NEW QUESTION # 13
Consider a scenario where you are developing a multimodal A1 system to translate sign language videos into text. The system utilizes a CNN for processing video frames and an RNN for generating the text sequence. During evaluation, you observe that the system struggles to accurately translate signs that involve complex hand movements or subtle facial expressions. What are the MOST effective strategies to improve performance in this specific scenario? (Select TWO)

A. Replace the RNN with a simpler feed forward neural network for faster processing.
B. Augment the training data with variations of the sign language videos, including different camera angles, lighting conditions, and signers.
C. Increase the depth of the CNN to capture more fine-grained visual features.
D. Reduce the frame rate of the input video to decrease computational load.
E. Incorporate a 3D CNN architecture to better capture spatial-temporal information from the video.

Answer: B,E

Explanation:
3D CNNs are designed to capture spatial-temporal information, which is essential for recognizing complex hand movements. Data augmentation helps the model generalize to different conditions and variations in signing style. Reducing frame rate (A) would likely worsen performance. Replacing the RNN with a feed forward network (D) would remove the ability to model sequential information, which is critical for translation. Increasing the depth of the CNN (B) might help, but a 3D CNN is more directly suited to the task.

NEW QUESTION # 14
You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model's generalization ability? (Select TWO)

A. Decreasing the amount of training data.
B. Using weight decay (L2 regularization).
C. Increasing the learning rate.
D. Decreasing batch size.
E. Applying data augmentation techniques (e.g., random synonym replacement, back-translation).

Answer: B,E

Explanation:
Weight decay penalizes large weights, preventing the model from overfitting to the training data. Data augmentation increases the diversity of the training data, making the model more robust to variations in unseen data. Increasing the learning rate can lead to instability and overfitting. Decreasing the amount of training data reduces the model's ability to learn general patterns.

NEW QUESTION # 15
You are working with a large multimodal dataset that contains images and corresponding text descriptions. The text descriptions are highly variable in length and content. Which of the following techniques is MOST effective for handling this variability when training a multimodal model?

A. Ignore text descriptions that are longer than a certain threshold.
B. Use dynamic padding and masking to handle variable-length sequences efficiently during batch processing.
C. Create a fixed-size vocabulary and discard any words not in the vocabulary.
D. Pad all text descriptions to the same maximum length using a special padding token.
E. Truncate all text descriptions to a fixed length.

Answer: B

Explanation:
Dynamic padding and masking allow the model to efficiently process variable-length sequences without losing information or introducing bias. Padding to a fixed length can waste computation, truncating loses data and ignoring descriptions also loses information. Discarding words could harm the model's learning abilities.

NEW QUESTION # 16
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