TEST D-GAI-F-01 DUMPS PDF - D-GAI-F-01 VALID MOCK TEST

Test D-GAI-F-01 Dumps Pdf - D-GAI-F-01 Valid Mock Test

Test D-GAI-F-01 Dumps Pdf - D-GAI-F-01 Valid Mock Test

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

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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 4
  • 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 5
  • 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.

EMC Dell GenAI Foundations Achievement Sample Questions (Q10-Q15):

NEW QUESTION # 10
What is a principle that guides organizations, government, and developers towards the ethical use of Al?

  • A. Al models must ensure data privacy and confidentiality.
  • B. Al models must always agree with the user's point of view.
  • C. The value of Al models must only be measured in financial gain.
  • D. Only regulatory agencies should be held accountable for the accuracy, fairness, and use of Al models

Answer: A

Explanation:
One of the guiding principles for the ethical use of AI is ensuring data privacy and confidentiality. Here's a detailed explanation:
* Ethical Principle:
* Explanation: Organizations, governments, and developers are increasingly recognizing the importance of protecting individuals' data. Ensuring data privacy and confidentiality is crucial to maintaining trust and compliance with legal standards.
* Implementation: AI models must be designed to handle data responsibly, employing techniques such as encryption, anonymization, and secure data storage to protect sensitive information.
* Regulatory Compliance: Adhering to regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is essential for legal and ethical AI deployment.
* References:
* Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines.
Nature Machine Intelligence, 1(9), 389-399.
* Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360.


NEW QUESTION # 11
What are the potential impacts of Al in business? (Select two)

  • A. Limiting the use of data analytics
  • B. Increasing the need for human intervention
  • C. Improving operational efficiency and enhancing customer experiences
  • D. Reducing production and operating costs

Answer: C,D

Explanation:
Reducing Costs: AI can automate repetitive and time-consuming tasks, leading to significant cost savings in production and operations. By optimizing resource allocation and minimizing errors, businesses can lower their operating expenses.


NEW QUESTION # 12
What strategy can an organization implement to mitigate bias and address a lack of diversity in technology?

  • A. Reduce diversity across technology teams and roles.
  • B. Limit partnerships with nonprofits and nongovernmental organizations.
  • C. Partner with nonprofit organizations, customers, and peer companies on coalitions, advocacy groups, and public policy initiatives.
  • D. Ignore the issue and hope it resolves itself over time.

Answer: C

Explanation:
Partnerships with Nonprofits: Collaborating with nonprofit organizations can provide valuable insights and resources to address diversity and bias in technology. Nonprofits often have expertise in advocacy and community engagement, which can help drive meaningful change.


NEW QUESTION # 13
What are the enablers that contribute towards the growth of artificial intelligence and its related technologies?

  • A. The development of blockchain technology and quantum computing
  • B. The abundance of data, lower cost high-performance compute, and improved algorithms
  • C. The creation of the Internet and the widespread use of cloud computing
  • D. The introduction of 5G networks and the expansion of internet service provider coverage

Answer: B

Explanation:
Several key enablers have contributed to the rapid growth of artificial intelligence (AI) and its related technologies. Here's a comprehensive breakdown:
Abundance of Data:The exponential increase in data from various sources (social media, IoT devices, etc.) provides the raw material needed for training complex AI models.
High-Performance Compute:Advances in hardware, such as GPUs and TPUs, have significantly lowered the cost and increased the availability of high-performance computing power required to train large AI models.
Improved Algorithms:Continuous innovations in algorithms and techniques (e.g., deep learning, reinforcement learning) have enhanced the capabilities and efficiency of AI systems.
References:
LeCun, Y., Bengio, Y., & Hinton, G. (2015).Deep Learning. Nature, 521(7553), 436-444.
Dean, J. (2020). AI and Compute. Google Research Blog.


NEW QUESTION # 14
A machine learning engineer is working on a project that involves training a model using labeled data.
What type of learning is he using?

  • A. Unsupervised learning
  • B. Self-supervised learning
  • C. Supervised learning
  • D. Reinforcement learning

Answer: C

Explanation:
When a machine learning engineer is training a model using labeled data, the type of learning being employed is supervised learning. In supervised learning, the model is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns to predict the output from the input data, and the goal is to minimize the difference between the predicted and actual outputs.
The Official Dell GenAI Foundations Achievement document likely covers the fundamental concepts of machine learning, including supervised learning, as it is one of the primary categories of machine learning. It would explain that supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs12. The data is known as training data, and it consists of a set of training examples. Each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). The supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
Self-supervised learning (Option OA) is a type of unsupervised learning where the system learns to predict part of its input from other parts. Unsupervised learning (Option OB) involves training a model on data that does not have labeled responses. Reinforcement learning (Option OD) is a type of learning where an agent learns to make decisions by performing actions and receiving rewards or penalties. Therefore, the correct answer is C. Supervised learning, as it directly involves the use of labeled data for training models.


NEW QUESTION # 15
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