[2024] Get Top-Rated EMC D-GAI-F-01 Exam Dumps Now [Q15-Q34] – Unlimited Lifetime Access Free Exams PDF

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[2024] Get Top-Rated EMC D-GAI-F-01 Exam Dumps Now

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NO.15 A data scientist is working on a project where she needs to customize a pre-trained language model to perform a specific task.
Which phase in the LLM lifecycle is she currently in?

Inferencing

Data collection

Training

Fine-tuning

NO.16 Whatis the role of a decoder in a GPT model?

It is used to fine-tune the model.

It takes the output and determines the input.

It takes the input and determines the appropriate output.

It is used to deploy the model in a production or test environment.

NO.17 You are tasked with creating a model that uses a competitive setting between two neural networks to create new data.
Which model would you use?

Feedforward Neural Networks

Variational Autoencoders (VAEs)

Generative Adversarial Networks (GANs)

Transformers

NO.18 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

NO.19 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 feed the model a large volume of data from a wide variety of subjects

To randomize all the statistical weights of the neural network

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

NO.20 Whatare the three key patrons involved in supporting the successful progress and formation ofany 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

NO.21 What is Artificial Narrow Intelligence (ANI)?

Al systems that can perform any task autonomously

Al systems that can process beyond human capabilities

Al systems that can think and make decisions like humans

Al systems that can perform a specific task autonomously

NO.22 A legal team is assessing the ethical issues related to Generative Al.
What is a significant ethical issue they should consider?

Improved customer service

Enhanced creativity

Increased productivity

NO.23 What is the primary purpose offine-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

NO.24 You are developing a new Al model that involves two neural networks working together in a competitive setting to generate new data.
What is this model called?

Feedforward Neural Networks

Generative Adversarial Networks (GANs)

Transformers

Variational Autoencoders (VAEs)

NO.25 What is P-Tuning in LLM?

Adjusting prompts to shape the model’s output without altering its core structure

Preventing a model from generating malicious content

Personalizing the training of a model to produce biased outputs

Punishing the model for generating incorrect answers

NO.26 A company is considering using Generative Al in its operations.
Which of the following is a benefit of using Generative Al?

Decreased innovation

Higher operational costs

Enhanced customer experience

Increased manual labor

NO.27 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.

NO.28 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

NO.29 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.

NO.30 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

NO.31 In a Variational Autoencoder (VAE), you have a network that compresses the input data into a smaller representation.
What is this network called?

Decoder

Discriminator

Generator

Encoder

NO.32 What is feature-based transfer learning?

Transferring the learning process to a new model

Training a model on entirely new features

Enhancing the model’s features with real-time data

Selecting specific features of a model to keep while removing others

NO.33 A company is planning its resources for the generative Al lifecycle.
Which phase requires the largest amount of resources?

Deployment

Inferencing

Fine-tuning

Training

NO.34 What impact does bias have in Al training data?

It can lead to unfair or incorrect outcomes.

It simplifies the algorithm’s complexity.

It ensures faster processing of data by the model.

It enhances the model’s performance uniformly across tasks.

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