EXAM EMC D-GAI-F-01 REVIEWS - D-GAI-F-01 SAMPLE QUESTIONS PDF

Exam EMC D-GAI-F-01 Reviews - D-GAI-F-01 Sample Questions Pdf

Exam EMC D-GAI-F-01 Reviews - D-GAI-F-01 Sample Questions Pdf

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EMC Dell GenAI Foundations Achievement Sample Questions (Q27-Q32):

NEW QUESTION # 27
A team is analyzing the performance of their Al models and noticed that the models are reinforcing existing flawed ideas.
What type of bias is this?

  • A. Systemic Bias
  • B. Data Bias
  • C. Linguistic Bias
  • D. Confirmation Bias

Answer: A

Explanation:
When AI models reinforce existing flawed ideas, it is typically indicative of systemic bias. This type of bias occurs when the underlying system, including the data, algorithms, and other structural factors, inherently favors certain outcomes or perspectives. Systemic bias can lead to the perpetuation of stereotypes, inequalities, or unfair practices that are present in the data or processes used to train the model.
The Official Dell GenAI Foundations Achievement document likely covers various types of biases and their impacts on AI systems. It would discuss how systemic bias affects the performance and fairness of AI models and the importance of identifying and mitigating such biases to increase the trust of humans over machines123. The document would emphasize the need for a culture that actively seeks to reduce bias and ensure ethical AI practices.
Confirmation Bias (Option OB) refers to the tendency to process information by looking for, or interpreting, information that is consistent with one's existing beliefs. Linguistic Bias (Option OC) involves bias that arises from the nuances of language used in the data. Data Bias (Option OD) is a broader term that could encompass various types of biases in the data but does not specifically refer to the reinforcement of flawed ideas as systemic bias does. Therefore, the correct answer is A. Systemic Bias.


NEW QUESTION # 28
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. Encoder
  • B. Discerner of real from fake data
  • C. Creator of random noise
  • D. Generator

Answer: A

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 # 29
A tech startup is developing a chatbot that can generate human-like text to interact with its users.
What is the primary function of the Large Language Models (LLMs) they might use?

  • A. To store data
  • B. To generate human-like text
  • C. To manage databases
  • D. To encrypt information

Answer: B

Explanation:
Large Language Models (LLMs), such as GPT-4, are designed to understand and generate human-like text.
They are trained on vast amounts of text data, which enables them to produce responses that can mimic human writing styles and conversation patterns. The primary function of LLMs in the context of a chatbot is to interact with users by generating text that is coherent, contextually relevant, and engaging.
The Dell GenAI Foundations Achievement document outlines the role of LLMs in generative AI, which includes their ability to generate text that resembles human language1. This is essential for chatbots, as they are intended to provide a conversational experience that is as natural and seamless as possible.
Storing data (Option OA), encrypting information (Option OB), and managing databases (Option OD) are not the primary functions of LLMs. While LLMs may be used in conjunction with systems that perform these tasks, their core capability lies in text generation, making Option OC the correct answer.


NEW QUESTION # 30
A company is considering using Generative Al in its operations.
Which of the following is a benefit of using Generative Al?

  • A. Decreased innovation
  • B. Higher operational costs
  • C. Enhanced customer experience
  • D. Increased manual labor

Answer: C

Explanation:
Generative AI has the potential to significantly enhance the customer experience. It can be used to personalize interactions, automate responses, and provide more engaging content, which can lead to a more satisfying and tailored experience for customers.
The Official Dell GenAI Foundations Achievement document would likely highlight the importance of customer experience in the context of AI. It would discuss how Generative AI can be leveraged to create more personalized and engaging interactions, which are key components of a positive customer experience1.
Additionally, Generative AI can help businesses understand and predict customer needs and preferences, enabling them to offer better service and support23.
Decreased innovation (Option OA), higher operational costs (Option OB), and increased manual labor (Option OD) are not benefits of using Generative AI. In fact, Generative AI is often associated with fostering greater innovation, reducing operational costs, and automating tasks that would otherwise require manual effort.
Therefore, the correct answer is C. Enhanced customer experience, as it is a recognized benefit of implementing Generative AI in business operations.


NEW QUESTION # 31
Why should artificial intelligence developers always take inputs from diverse sources?

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

Answer: D

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 # 32
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