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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q27-Q32):

NEW QUESTION # 27
You have developed a multimodal generative A1 model that generates images based on textual descriptions. You want to set up an automated system to monitor the model's performance and identify potential issues like degradation in image quality or introduction of biases over time. Which of the following components are essential for such a monitoring system? (Select THREE)

A. A web interface for users to manually upload images and rate the generated images.
B. Alerting mechanisms that trigger notifications when performance metrics fall below predefined thresholds or when biases are detected.
C. A database to store all training data used for the model.
D. Automated metrics calculation pipelines to continuously compute relevant metrics like FID, CLIP score, and bias detection metrics on generated images.
E. A mechanism to store and analyze the text prompts used to generate the images.

Answer: B,D,E

Explanation:
Automated metrics calculation (B) and alerting mechanisms (C) are crucial for continuously monitoring performance and detecting issues. Storing and analyzing text prompts (E) can help identify patterns related to performance degradation or bias. While storing training data (A) can be useful, it's not essential for monitoring. A manual evaluation interface (D) can be helpful for occasional spot-checks, but it's not a core component of an automated monitoring system.

NEW QUESTION # 28
You have a dataset of customer reviews for a Generative A1 service. The dataset contains text reviews, numerical ratings (1-5 stars), and categorical data about the customer's subscription plan (Basic, Premium, Enterprise). You want to build a model to predict the numerical rating based on the text review and subscription plan. Which data analysis and modeling approach would be MOST suitable?

A. Train a deep learning model (e.g., BERT or RoBERTa) on the text reviews, concatenate the output embeddings with the one-hot encoded subscription plan, and use a regression layer to predict the numerical rating.
B. Use a decision tree to predict the numerical rating based on the text reviews (using TF-IDF) and subscription plan.
C. Use topic modeling on the text reviews, then use logistic regression to predict the numerical rating based on the topic distributions and subscription plan.
D. Perform sentiment analysis on the text reviews, then use linear regression to predict the numerical rating based on the sentiment score and subscription plan (one-hot encoded).
E. Calculate the average word length of the text reviews and use that as a feature in a linear regression model along with the subscription plan to predict the rating.

Answer: A

Explanation:
Using a pre-trained language model like BERT or RoBERTa captures the semantic meaning of the text reviews most effectively. Concatenating the embeddings with the subscription plan allows the model to learn the combined effect of both inputs. Regression layer is used as numeric ratings (1-5 stars) are provided as the target values. Sentiment and topic modeling can work as features but BERT/RoBERTa gives better context. Other options aren't able to capture complex context.

NEW QUESTION # 29
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A. Decrease the learning rate of the model during training.
B. Increase the number of attention heads in the Transformer.
C. Increase the temperature parameter during text generation.
D. Implement beam search with a larger beam width.
E. Use a smaller vccabulary size.

Answer: C

Explanation:
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.

NEW QUESTION # 30
When deploying a Generative A1 model to a resource-constrained edge device (e.g., a mobile phone), what are the key considerations for model optimization and which techniques are most effective?

A. Knowledge distillation to transfer knowledge from a large, accurate model to a smaller, faster model suitable for the edge.
B. A and B.
C. Model quantization (e.g., INT8) to reduce model size and memory bandwidth, and model pruning to remove unimportant connections.
D. Focusing solely on reducing the model's parameter count without considering its computational complexity.
E. Increasing the model's complexity to improve accuracy on the limited hardware.

Answer: B

Explanation:
Edge deployment requires optimizing for both model size and computational efficiency. Quantization reduces model size, pruning removes unnecessary connections, and knowledge distillation creates smaller, faster models. Increasing model complexity is counterproductive in resource-constrained environments. Both parameter count and computational complexity are important factors.

NEW QUESTION # 31
You're building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot's performance on complex visual scenes?

A. Remove the image processing component entirely.
B. Integrate an object detection model to identify and localize objects in the image before feeding the information to the chatbot.
C. Use a larger language model for the chatbot.
D. Train the chatbot on a dataset with only simple images containing a single object.
E. Reduce the resolution of the input images.

Answer: B

Explanation:
Integrating an object detection model allows the chatbot to explicitly identify and localize the objects within the image, providing crucial information for answering questions about specific objects in complex scenes. A larger language model can help with general language understanding, but doesn't address the fundamental issue of object identification. Reducing image resolution or training on simple images will degrade performance on complex scenes. Removing image processing defeats the purpose.

NEW QUESTION # 32
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