EMC - D-GAI-F-01 - Dell GenAI Foundations Achievement Authoritative Detailed Answers
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EMC Dell GenAI Foundations Achievement Sample Questions (Q42-Q47):
NEW QUESTION # 42
Whatis the role of a decoder in a GPT model?
Answer: A
Explanation:
In the context of GPT (Generative Pre-trained Transformer) models, the decoder plays a crucial role. Here's a detailed explanation:
Decoder Function:The decoder in a GPT model is responsible for taking the input (often a sequence of text) and generating the appropriate output (such as a continuation of the text or an answer to a query).
Architecture:GPT models are based on the transformer architecture, where the decoder consists of multiple layers of self-attention and feed-forward neural networks.
Self-Attention Mechanism:This mechanism allows the model to weigh the importance of different words in the input sequence, enabling it to generate coherent and contextually relevant output.
Generation Process:During generation, the decoder processes the input through these layers to produce the next word in the sequence, iteratively constructing the complete output.
References:
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.
(2017). Attention is All You Need. In Advances in Neural Information Processing Systems.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Blog.
NEW QUESTION # 43
A healthcare company wants to use Al to assist in diagnosing diseases by analyzing medical images.
Which of the following is an application of Generative Al in this field?
Answer: B
Explanation:
Generative AI has a significant application in the healthcare field, particularly in the analysis of medical images for diagnosis. Generative models can be trained to recognize patterns and anomalies in medical images, such as X-rays, MRIs, and CT scans, which can assist healthcare professionals in diagnosing diseases more accurately and efficiently.
The Official Dell GenAI Foundations Achievement document likely covers the scope and impact of AI in various industries, including healthcare. It would discuss how generative AI, through its advanced algorithms, can generate new data instances that mimic real data, which is particularly useful in medical imaging12. These generative models have the potential to help with anomaly detection, image-to-image translation, denoising, and MRI reconstruction, among other applications34.
Creating social media posts (Option OA), inventory management (Option OB), and fraud detection (Option OD) are not directly related to the analysis of medical images for diagnosis. Therefore, the correct answer is C.
Analyzing medical images for diagnosis, as it is the application of Generative AI that aligns with the context of the question.
NEW QUESTION # 44
Whatare the three key patrons involved in supporting the successful progress and formation ofany Al-based application?
Answer: A
Explanation:
Customer Facing Teams: These teams are critical in understanding and defining the requirements of the AI-based application from the end-user perspective. They gather insights on customer needs, pain points, and desired outcomes, which are essential for designing a user-centric AI solution.
NEW QUESTION # 45
A company wants to develop a language model but has limited resources.
What is the main advantage of using pretrained LLMs in this scenario?
Answer: A
Explanation:
Pretrained Large Language Models (LLMs) like GPT-3 are advantageous for a company with limited resources because they have already been trained on vast amounts of data. This pretraining process involves significant computational resources over an extended period, which is often beyond the capacity of smaller companies or those with limited resources.
Advantages of using pretrained LLMs:
* Cost-Effective: Developing a language model from scratch requires substantial financial investment in computing power and data storage. Pretrained models, being readily available, eliminate these initial costs.
* Time-Saving: Training a language model can take weeks or even months. Using a pretrained model allows companies to bypass this lengthy process.
* Less Data Required: Pretrained models have been trained on diverse datasets, so they require less additional data to fine-tune for specific tasks.
* Immediate Deployment: Pretrained models can be deployed quickly for production, allowing companies to focus on application-specific improvements.
In summary, the main advantage is that pretrained LLMs save time and resources for companies, especially those with limited resources, by providing a foundation that has already learned a wide range of language patterns and knowledge. This allows for quicker deployment and cost savings, as the need for extensive data collection and computational training is significantly reduced.
NEW QUESTION # 46
In a Generative Adversarial Network (GAN), you have a network that evaluates whether the data generated by the other network is real or fake. What is this evaluating network called?
Answer: B
NEW QUESTION # 47
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