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EMC Dell GenAI Foundations Achievement Sample Questions (Q40-Q45):

NEW QUESTION # 40
What impact does bias have in Al training data?

A. It enhances the model's performance uniformly across tasks.
B. It simplifies the algorithm's complexity.
C. It can lead to unfair or incorrect outcomes.
D. It ensures faster processing of data by the model.

Answer: C

Explanation:
Definition of Bias: Bias in AI refers to systematic errors that can occur in the model due to prejudiced assumptions made during the data collection, model training, or deployment stages.

NEW QUESTION # 41
What is a principle that guides organizations, government, and developers towards the ethical use of Al?

A. Only regulatory agencies should be held accountable for the accuracy, fairness, and use of Al models
B. Al models must ensure data privacy and confidentiality.
C. Al models must always agree with the user's point of view.
D. The value of Al models must only be measured in financial gain.

Answer: B

Explanation:
One of the guiding principles for the ethical use of AI is ensuring data privacy and confidentiality. Here's a detailed explanation:
* Ethical Principle:
* Explanation: Organizations, governments, and developers are increasingly recognizing the importance of protecting individuals' data. Ensuring data privacy and confidentiality is crucial to maintaining trust and compliance with legal standards.
* Implementation: AI models must be designed to handle data responsibly, employing techniques such as encryption, anonymization, and secure data storage to protect sensitive information.
* Regulatory Compliance: Adhering to regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is essential for legal and ethical AI deployment.
* References:
* Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines.
Nature Machine Intelligence, 1(9), 389-399.
* Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360.

NEW QUESTION # 42
What is the primary function of Large Language Models (LLMs) in the context of Natural Language Processing?

A. LLMs are used to shrink the size of the neural network.
B. LLMs receive input in human language and produce output in human language.
C. LLMs are used to parse image, audio, and video data.
D. LLMs are used to increase the size of the neural network.

Answer: B

Explanation:
The primary function of Large Language Models (LLMs) in Natural Language Processing (NLP) is to process and generate human language. Here's a detailed explanation:
Function of LLMs:LLMs are designed to understand, interpret, and generate human language text.
They can perform tasks such as translation, summarization, and conversation.
Input and Output:LLMs take input in the form of text and produce output in text, making them versatile tools for a wide range of language-based applications.
Applications:These models are used in chatbots, virtual assistants, translation services, and more, demonstrating their ability to handle natural language efficiently.
References:
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D.
(2020). Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems.

NEW QUESTION # 43
What are the enablers that contribute towards the growth of artificial intelligence and its related technologies?

A. The abundance of data, lower cost high-performance compute, and improved algorithms
B. The introduction of 5G networks and the expansion of internet service provider coverage
C. The creation of the Internet and the widespread use of cloud computing
D. The development of blockchain technology and quantum computing

Answer: A

Explanation:
Several key enablers have contributed to the rapid growth of artificial intelligence (AI) and its related technologies. Here's a comprehensive breakdown:
Abundance of Data:The exponential increase in data from various sources (social media, IoT devices, etc.) provides the raw material needed for training complex AI models.
High-Performance Compute:Advances in hardware, such as GPUs and TPUs, have significantly lowered the cost and increased the availability of high-performance computing power required to train large AI models.
Improved Algorithms:Continuous innovations in algorithms and techniques (e.g., deep learning, reinforcement learning) have enhanced the capabilities and efficiency of AI systems.
References:
LeCun, Y., Bengio, Y., & Hinton, G. (2015).Deep Learning. Nature, 521(7553), 436-444.
Dean, J. (2020). AI and Compute. Google Research Blog.

NEW QUESTION # 44
What is feature-based transfer learning?

A. Enhancing the model's features with real-time data
B. Selecting specific features of a model to keep while removing others
C. Training a model on entirely new features
D. Transferring the learning process to a new model

Answer: B

Explanation:
Feature-based transfer learning involves leveraging certain features learned by a pre-trained model and adapting them to a new task. Here's a detailed explanation:
Feature Selection:This process involves identifying and selecting specific features or layers from a pre-trained model that are relevant to the new task while discarding others that are not.
Adaptation:The selected features are then fine-tuned or re-trained on the new dataset, allowing the model to adapt to the new task with improved performance.
Efficiency:This approach is computationally efficient because it reuses existing features, reducing the amount of data and time needed for training compared to starting from scratch.
References:
Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How Transferable Are Features in Deep Neural Networks? In Advances in Neural Information Processing Systems.

NEW QUESTION # 45
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