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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
| Topic 2: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 3: Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Topic 4: Generative AI Concepts | - Generative models
|
| Topic 5: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
NVIDIA Generative AI Multimodal Sample Questions:
1. Which of the following best describes the role of machine learning in handling multimodal data?
A) To reduce the amount of data needed for accurate predictions.
B) To focus on textual data analysis.
C) To enable models to learn from and interpret diverse data types.
D) To eliminate the need for human intervention in data analysis.
2. You have been given a dataset with missing values. What is the first step you should take with the data?
A) Remove the rows with missing values.
B) Analyze the patterns and distribution of missing values.
C) Fill in the missing values with a default value.
D) Remove the columns with missing values.
3. Which of the following best describes the role of the Hugging Face model repository in ML software development?
A) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
B) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
C) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
D) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
4. 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 mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
D) It requires AI systems to be developed with an ethical consideration for societal impacts.
5. In machine learning, what is the purpose of data normalization?
A) To remove irrelevant data from the dataset.
B) To convert data into a specific format for easier analysis.
C) To reduce the dimensionality of the dataset.
D) To increase the complexity of the dataset.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: B |
