D-GAI-F-01 Dell GenAI Foundations Achievement Exam Dumps

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D-GAI-F-01 Dell GenAI Foundations Achievement focuses on the concepts of AI and ML. The D-GAI-F-01 Dell GenAI Foundations Achievement exam dumps are designed to help candidates prepare for the Dell GenAI Foundations certification exam. These dumps include a collection of questions and answers that cover the key topics and concepts tested in the exam. D-GAI-F-01 exam dumps can significantly enhance your understanding of the subject matter. Free Dell GenAI Foundations Achievement D-GAI-F-01 exam dumps are available below.

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

Customer facing teams, executive team, and facilities team

Marketing team, executive team, and data science team

Customer facing teams, HR team, and data science team

Customer facing teams, executive team, and data science team

2. What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?

Supervised learning feeds a large corpus of raw data into the Al system, while unsupervised learning uses labeled data to teach the Al system what output is expected.

Supervised learning is common for fine tuning and customization, while unsupervised learning is common for base model training.

Supervised learning uses labeled data to teach the Al system what output is expected, while unsupervised learning feeds a large corpus of raw data into the Al system, which determines the appropriate weights in its neural network.

Supervised learning is common for base model training, while unsupervised learning is common for fine tuning and customization.

3. What are the three broad steps in the lifecycle of Al for Large Language Models?

Training, Customization, and Inferencing

Preprocessing, Training, and Postprocessing

Initialization, Training, and Deployment

Data Collection, Model Building, and Evaluation

4. What impact does bias have in Al training data?

It ensures faster processing of data by the model.

It can lead to unfair or incorrect outcomes.

It simplifies the algorithm's complexity.

It enhances the model's performance uniformly across tasks.

5. What is one of the positive stereotypes people have about Al?

Al is unbiased.

Al is suitable only in manufacturing sectors.

Al can leave humans behind.

Al can help businesses complete tasks around the clock 24/7.

6. What is artificial intelligence?

The study of computer science

The study and design of intelligent agents

The study of data analysis

The study of human brain functions

7. What is Artificial Narrow Intelligence (ANI)?

A. Al systems that can perform any task autonomously

B. Al systems that can process beyond human capabilities

C. Al systems that can think and make decisions like humans

D. Al systems that can perform a specific task autonomously

8. Why is diversity important in Al training data?

A. To make Al models cheaper to develop

B. To reduce the storage requirements for data

C. To ensure the model can generalize across different scenarios

D. To increase the model's speed of computation

9. What is the first step an organization must take towards developing an Al-based application?

Prioritize Al.

Develop a business strategy.

Address ethical and legal issues.

Develop a data strategy.

10. What is the primary purpose of fine-tuning in the lifecycle of a Large Language Model (LLM)?

To randomize all the statistical weights of the neural network

To customize the model for a specific task by feeding it task-specific content

To feed the model a large volume of data from a wide variety of subjects

To put text into a prompt to interact with the cloud-based Al system

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11. Why should artificial intelligence developers always take inputs from diverse sources?

To investigate the model requirements properly

To perform exploratory data analysis

To determine where and how the dataset is produced

To cover all possible cases that the model should handle

12. What is the purpose of the explainer loops in the context of Al models?

They are used to increase the complexity of the Al models.

They are used to provide insights into the model's reasoning, allowing users and developers to understand why a model makes certain predictions or decisions.

They are used to reduce the accuracy of the Al models.

They are used to increase the bias in the Al models.

13. What is the purpose of fine-tuning in the generative Al lifecycle?

To put text into a prompt to interact with the cloud-based Al system

To randomize all the statistical weights of the neural network

To customize the model for a specific task by feeding it task-specific content

To feed the model a large volume of data from a wide variety of subjects

14. What is one of the objectives of Al in the context of digital transformation?

To become essential to the success of the digital economy

To reduce the need for Internet connectivity

To replace all human tasks with automation

To eliminate the need for data privacy

15. What is Transfer Learning in the context of Language Model (LLM) customization?

It is where you can adjust prompts to shape the model's output without modifying its underlying weights.

It is a process where the model is additionally trained on something like human feedback.

It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.

It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.

16. What is the significance of parameters in Large Language Models (LLMs)?

Parameters are used to parse image, audio, and video data in LLMs.

Parameters are used to decrease the size of the LLMs.

Parameters are used to increase the size of the LLMs.

Parameters are statistical weights inside of the neural network of LLMs.

17. What is the primary function of Large Language Models (LLMs) in the context of Natural Language Processing?

LLMs receive input in human language and produce output in human language.

LLMs are used to shrink the size of the neural network.

LLMs are used to increase the size of the neural network.

LLMs are used to parse image, audio, and video data.

18. What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?

To customize the model for a specific task by feeding it task-specific content

To feed the model a large volume of data from a wide variety of subjects

To use the model in a production, research, or test environment

To randomize all the statistical weights of the neural networks

D-GAI-F-01 Dell GenAI Foundations Achievement Exam Dumps

D-GAI-F-01 Dell GenAI Foundations Achievement Exam Dumps

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