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Dell D-GAI-F-01 Exam
Dell GenAI Foundations Achievement (Page 6 )

Updated On: 1-Feb-2026
Page 2 of 13
PDF with 58 Questions

What are the three key patrons involved in supporting the successful progress and formation of any Al-based application?

  1. Customer facing teams, executive team, and facilities team
  2. Marketing team, executive team, and data science team
  3. Customer facing teams, HR team, and data science team
  4. Customer facing teams, executive team, and data science team

Answer(s): D

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.


Reference:

"Customer-facing teams are instrumental in translating user requirements into technical specifications." (Forbes, 2022)
Executive Team: The executive team provides strategic direction, resources, and support for AI initiatives. They are responsible for aligning the AI strategy with the overall business objectives, securing funding, and fostering a culture that supports innovation and technology adoption.
"Executive leadership is crucial in setting the vision and securing the resources necessary for AI projects." (McKinsey & Company, 2021)
Data Science Team: The data science team is responsible for the technical development of the AI application. They handle data collection, preprocessing, model building, training, and evaluation. Their expertise ensures the AI system is accurate, efficient, and scalable.

"Data scientists play a pivotal role in the development and deployment of AI systems." (Harvard Business Review, 2020)



A company wants to develop a language model but has limited resources.

What is the main advantage of using pretrained LLMs in this scenario?

  1. They save time and resources
  2. They require less data
  3. They are cheaper to develop
  4. They are more accurate

Answer(s): 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.



A company is considering using deep neural networks in its LLMs.

What is one of the key benefits of doing so?

  1. They can handle more complicated problems
  2. They require less data
  3. They are cheaper to run
  4. They are easier to understand

Answer(s): A

Explanation:

Deep neural networks (DNNs) are a class of machine learning models that are particularly well-suited for handling complex patterns and high-dimensional data.
When incorporated into Large Language Models (LLMs), DNNs provide several benefits, one of which is their ability to handle more complicated problems.
Key Benefits of DNNs in LLMs:
Complex Problem Solving: DNNs can model intricate relationships within data, making them capable of understanding and generating human-like text.
Hierarchical Feature Learning: They learn multiple levels of representation and abstraction that help in identifying patterns in input data.
Adaptability: DNNs are flexible and can be fine-tuned to perform a wide range of tasks, from translation to content creation.
Improved Contextual Understanding: With deep layers, neural networks can capture context over longer stretches of text, leading to more coherent and contextually relevant outputs. In summary, the key benefit of using deep neural networks in LLMs is their ability to handle more complicated problems, which stems from their deep architecture capable of learning intricate patterns and dependencies within the data. This makes DNNs an essential component in the development of sophisticated language models that require a nuanced understanding of language and context.



A financial institution wants to use a smaller, highly specialized model for its finance tasks.

Which model should they consider?

  1. BERT
  2. GPT-4
  3. Bloomberg GPT
  4. GPT-3

Answer(s): C

Explanation:

For a financial institution looking to use a smaller, highly specialized model for finance tasks, Bloomberg GPT would be the most suitable choice. This model is tailored specifically for financial data and tasks, making it ideal for an institution that requires precise and specialized capabilities in the financial domain.
While BERT and GPT-3 are powerful models, they are more general-purpose. GPT-4, being the latest among the options, is also a generalist model but with a larger scale, which might not be necessary for specialized tasks. Therefore, Option C: Bloomberg GPT is the recommended model to consider for specialized finance tasks.



In a Variational Autoencoder (VAE), you have a network that compresses the input data into a smaller representation.

What is this network called?

  1. Decoder
  2. Discriminator
  3. Generator
  4. Encoder

Answer(s): D

Explanation:

In a Variational Autoencoder (VAE), the network that compresses the input data into a smaller, more compact representation is known as the encoder. This part of the VAE is responsible for taking the high-dimensional input data and transforming it into a lower-dimensional representation, often referred to as the latent space or latent variables. The encoder effectively captures the essential information needed to represent the input data in a more efficient form. The encoder is contrasted with the decoder, which takes the compressed data from the latent space and reconstructs the input data to its original form. The discriminator and generator are components typically associated with Generative Adversarial Networks (GANs), not VAEs. Therefore, the correct answer is D. Encoder.
This information aligns with the foundational concepts of artificial intelligence and machine learning, which are likely to be covered in the Dell GenAI Foundations Achievement document, as it includes topics on machine learning, deep learning, and neural network concepts12.



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