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EMC D-GAI-F-01 Valid Examcollection | D-GAI-F-01 Latest Test Discount

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EMC D-GAI-F-01 Exam Syllabus Topics:

Topic
Details

Topic 1

Implementation and Best Practices: For IT managers and system integrators, this part of the exam may address best practices for implementing Generative AI solutions using Dell technologies.

Topic 2

Dell's Generative AI Technologies: For Dell system administrators and AI implementers, this part of the exam probably focuses on Dell's specific implementations and tools related to Generative AI.

Topic 3

Ethics and Responsible AI: For all professionals working with AI, this section likely covers ethical considerations and responsible use of Generative AI in enterprise environments.

Topic 4

Introduction to Generative AI: For AI enthusiasts and IT professionals, this section of the exam likely covers the basic concepts and principles of Generative AI.

Topic 5

Use Cases and Applications: For business analysts and solution architects, this section might cover practical applications and use cases of Generative AI within Dell's ecosystem.

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EMC Dell GenAI Foundations Achievement Sample Questions (Q52-Q57):

NEW QUESTION # 52
Why should artificial intelligence developers always take inputs from diverse sources?

A. To perform exploratory data analysis
B. To cover all possible cases that the model should handle
C. To investigate the model requirements properly
D. To determine where and how the dataset is produced

Answer: B

Explanation:
Diverse Data Sources: Utilizing inputs from diverse sources ensures the AI model is exposed to a wide range of scenarios, dialects, and contexts. This diversity helps the model generalize better and avoid biases that could occur if the data were too homogeneous.

NEW QUESTION # 53
Whatrole does human feedback play in Reinforcement Learning for LLMs?

A. It helps in identifying the model's architecture for optimization.
B. It assists in the physical hardware improvement of the model.
C. It rewards good output and penalizes bad output to improve the model.
D. It is used to provide real-time corrections to the model's output.

Answer: C

Explanation:
Role of Human Feedback: In reinforcement learning for LLMs, human feedback is used to fine-tune the model by providing rewards for correct outputs and penalties for incorrect ones. This feedback loop helps the model learn more effectively.

NEW QUESTION # 54
A team of researchers is developing a neural network where one part of the network compresses input data.
What is this part of the network called?

A. Generator
B. Encoder
C. Creator of random noise
D. Discerner of real from fake data

Answer: B

Explanation:
In the context of neural networks, particularly those involved in unsupervised learning like autoencoders, the part of the network that compresses the input data is called the encoder. This component of the network takes the high-dimensional input data and encodes it into a lower-dimensional latent space. The encoder's role is crucial as it learns to preserve as much relevant information as possible in this compressed form.
The term "encoder" is standard in the field of machine learning and is used in various architectures, including Variational Autoencoders (VAEs) and other types of autoencoders. The encoder works in tandem with a decoder, which attempts to reconstruct the input data from the compressed form, allowing the network to learn a compact representation of the data.
The options "Creator of random noise" and "Discerner of real from fake data" are not standard terms associated with the part of the network that compresses data. The term "Generator" is typically associated with Generative Adversarial Networks (GANs), where it generates new data instances.
The Dell GenAI Foundations Achievement document likely covers the fundamental concepts of neural networks, including the roles of encoders and decoders, which is why the encoder is the correct answer in this context12.

NEW QUESTION # 55
What is one of the positive stereotypes people have about Al?

A. Al is suitable only in manufacturing sectors.
B. Al can help businesses complete tasks around the clock 24/7.
C. Al is unbiased.
D. Al can leave humans behind.

Answer: B

Explanation:
24/7 Availability: AI systems can operate continuously without the need for breaks, which enhances productivity and efficiency. This is particularly beneficial for customer service, where AI chatbots can handle inquiries at any time.

NEW QUESTION # 56
What are the three broad steps in the lifecycle of Al for Large Language Models?

A. Preprocessing, Training, and Postprocessing
B. Initialization, Training, and Deployment
C. Data Collection, Model Building, and Evaluation
D. Training, Customization, and Inferencing

Answer: D

Explanation:
Training: The initial phase where the model learns from a large dataset. This involves feeding the model vast amounts of text data and using techniques like supervised or unsupervised learning to adjust the model's parameters.

NEW QUESTION # 57
......

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