DELL EMC D-GAI-F-01 Dell GenAI Foundations Achievement Online Training
DELL EMC D-GAI-F-01 Online Training
The questions for D-GAI-F-01 were last updated at Mar 06,2025.
- Exam Code: D-GAI-F-01
- Exam Name: Dell GenAI Foundations Achievement
- Certification Provider: DELL EMC
- Latest update: Mar 06,2025
Question #11
Why should artificial intelligence developers always take inputs from diverse sources?
- A . To investigate the model requirements properly
- B . To perform exploratory data analysis
- C . To determine where and how the dataset is produced
- D . To cover all possible cases that the model should handle
Question #12
What is the purpose of the explainer loops in the context of Al models?
- A . They are used to increase the complexity of the Al models.
- B . 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.
- C . They are used to reduce the accuracy of the Al models.
- D . They are used to increase the bias in the Al models.
Question #13
What is the purpose of fine-tuning in the generative Al lifecycle?
- A . To put text into a prompt to interact with the cloud-based Al system
- B . To randomize all the statistical weights of the neural network
- C . To customize the model for a specific task by feeding it task-specific content
- D . To feed the model a large volume of data from a wide variety of subjects
Question #14
What is one of the objectives of Al in the context of digital transformation?
- A . To become essential to the success of the digital economy
- B . To reduce the need for Internet connectivity
- C . To replace all human tasks with automation
- D . To eliminate the need for data privacy
Question #15
What is Transfer Learning in the context of Language Model (LLM) customization?
- A . It is where you can adjust prompts to shape the model’s output without modifying its underlying weights.
- B . It is a process where the model is additionally trained on something like human feedback.
- C . 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.
- D . It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.
Question #16
What is the significance of parameters in Large Language Models (LLMs)?
- A . Parameters are used to parse image, audio, and video data in LLMs.
- B . Parameters are used to decrease the size of the LLMs.
- C . Parameters are used to increase the size of the LLMs.
- D . Parameters are statistical weights inside of the neural network of LLMs.
Question #17
What is the primary function of Large Language Models (LLMs) in the context of Natural Language Processing?
- A . LLMs receive input in human language and produce output in human language.
- B . LLMs are used to shrink the size of the neural network.
- C . LLMs are used to increase the size of the neural network.
- D . LLMs are used to parse image, audio, and video data.
Question #18
What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?
- A . To customize the model for a specific task by feeding it task-specific content
- B . To feed the model a large volume of data from a wide variety of subjects
- C . To use the model in a production, research, or test environment
- D . To randomize all the statistical weights of the neural networks
Question #19
What strategy can an organization implement to mitigate bias and address a lack of diversity in technology?
- A . Limit partnerships with nonprofits and nongovernmental organizations.
- B . Partner with nonprofit organizations, customers, and peer companies on coalitions, advocacy groups, and public policy initiatives.
- C . Reduce diversity across technology teams and roles.
- D . Ignore the issue and hope it resolves itself over time.
Question #20
What is P-Tuning in LLM?
- A . Adjusting prompts to shape the model’s output without altering its core structure
- B . Preventing a model from generating malicious content
- C . Personalizing the training of a model to produce biased outputs
- D . Punishing the model for generating incorrect answers
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