D-GAI-F-01 EXAM QUESTIONS: DELL GENAI FOUNDATIONS ACHIEVEMENT & D-GAI-F-01 EXAM PREPARATION

D-GAI-F-01 Exam Questions: Dell GenAI Foundations Achievement & D-GAI-F-01 Exam Preparation

D-GAI-F-01 Exam Questions: Dell GenAI Foundations Achievement & D-GAI-F-01 Exam Preparation

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EMC Dell GenAI Foundations Achievement Sample Questions (Q24-Q29):

NEW QUESTION # 24
A company is implementing governance in its Generative Al.
What is a key aspect of this governance?

  • A. Transparency
  • B. Speed of deployment
  • C. User interface design
  • D. Cost efficiency

Answer: A

Explanation:
Governance in Generative AI involves several key aspects, among which transparency is crucial.
Transparency in AI governance refers to the clarity and openness regarding how AI systems operate, the data they use, the decision-making processes they employ, and the way they are developed and deployed. It ensures that stakeholders understand AI processes and can trust the outcomes produced by AI systems.
The Official Dell GenAI Foundations Achievement document likely emphasizes the importance of transparency as part of ethical AI governance. It would discuss the need for clear communication about AI operations to build trust and ensure accountability1. Additionally, transparency is a foundational element in addressing ethical considerations, reducing bias, and ensuring that AI systems are used responsibly2.
User interface design (Option OB), speed of deployment (Option OC), and cost efficiency (Option OD) are important factors in the development and implementation of AI systems but are not specifically governance aspects. Governance focuses on the overarching principles and practices that guide the ethical and responsible use of AI, making transparency the key aspect in this context.


NEW QUESTION # 25
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. Enhanced customer experience
  • C. Higher operational costs
  • D. Increased manual labor

Answer: B

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 # 26
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. Confirmation Bias
  • B. Systemic Bias
  • C. Linguistic Bias
  • D. Data Bias

Answer: B

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 # 27
Why should artificial intelligence developers always take inputs from diverse sources?

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

Answer: C

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 # 28
Whatis the role of a decoder in a GPT model?

  • A. It takes the input and determines the appropriate output.
  • B. It takes the output and determines the input.
  • C. It is used to fine-tune the model.
  • D. It is used to deploy the model in a production or test environment.

Answer: A

Explanation:
In the context of GPT (Generative Pre-trained Transformer) models, the decoder plays a crucial role. Here's a detailed explanation:
Decoder Function:The decoder in a GPT model is responsible for taking the input (often a sequence of text) and generating the appropriate output (such as a continuation of the text or an answer to a query).
Architecture:GPT models are based on the transformer architecture, where the decoder consists of multiple layers of self-attention and feed-forward neural networks.
Self-Attention Mechanism:This mechanism allows the model to weigh the importance of different words in the input sequence, enabling it to generate coherent and contextually relevant output.
Generation Process:During generation, the decoder processes the input through these layers to produce the next word in the sequence, iteratively constructing the complete output.
References:
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.
(2017). Attention is All You Need. In Advances in Neural Information Processing Systems.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Blog.


NEW QUESTION # 29
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