Dell EMC D-GAI-F-01 Dell GenAI Foundations Achievement Exam Practice Test

Answer : B

Artificial intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that would normally require human intelligence. The correct answer is option B, which defines AI as 'the study and design of intelligent agents.' Here's a comprehensive breakdown:

Definition of AI: AI involves the creation of algorithms and systems that can perceive their environment, reason about it, and take actions to achieve specific goals.

Intelligent Agents: An intelligent agent is an entity that perceives its environment and takes actions to maximize its chances of success. This concept is central to AI and encompasses a wide range of systems, from simple rule-based programs to complex neural networks.

Applications: AI is applied in various domains, including natural language processing, computer vision, robotics, and more.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.

Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence: A Logical Approach. Oxford University Press.

Answer : C

Diversity in AI training data is crucial for developing robust and fair AI models. The correct answer is option C. Here's why:

Generalization: A diverse training dataset ensures that the AI model can generalize well across different scenarios and perform accurately in real-world applications.

Bias Reduction: Diverse data helps in mitigating biases that can arise from over-representation or under-representation of certain groups or scenarios.

Fairness and Inclusivity: Ensuring diversity in data helps in creating AI systems that are fair and inclusive, which is essential for ethical AI development.

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org.

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54(6), 1-35.

Answer : D

The first step an organization must take towards developing an AI-based application is to develop a data strategy. The correct answer is option D. Here's an in-depth explanation:

Importance of Data: Data is the foundation of any AI system. Without a well-defined data strategy, AI initiatives are likely to fail because the model's performance heavily depends on the quality and quantity of data.

Components of a Data Strategy: A comprehensive data strategy includes data collection, storage, management, and ensuring data quality. It also involves establishing data governance policies to maintain data integrity and security.

Alignment with Business Goals: The data strategy should align with the organization's business goals to ensure that the AI applications developed are relevant and add value.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

Marr, B. (2017). Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. Kogan Page Publishers.

Answer : C

The first step for a startup planning to leverage Generative AI to enhance its business is to identify opportunities where this technology can be applied to create value. This involves understanding the business's goals and objectives and recognizing how Generative AI can complement existing workflows, enhance creative processes, and drive the company closer to achieving its strategic priorities1.

Identifying opportunities means assessing where Generative AI can have the most significant impact, whether it's in improving customer experiences, optimizing processes, or fostering innovation. It sets the foundation for a successful Generative AI strategy by aligning the technology's capabilities with the business's needs and goals1.

Investing in talent (Option OA), risk management (Option OB), and data management (Option OD) are also important steps in developing a Generative AI strategy. However, these steps typically follow after the opportunities have been identified. A clear understanding of the opportunities will guide the startup in making informed decisions about talent acquisition, risk assessment, and data governance necessary to support the chosen Generative AI applications23. Therefore, the correct first step is C. Identifying opportunities.

Answer : D

Feature-based transfer learning involves leveraging certain features learned by a pre-trained model and adapting them to a new task. Here's a detailed explanation:

Feature Selection: This process involves identifying and selecting specific features or layers from a pre-trained model that are relevant to the new task while discarding others that are not.

Adaptation: The selected features are then fine-tuned or re-trained on the new dataset, allowing the model to adapt to the new task with improved performance.

Efficiency: This approach is computationally efficient because it reuses existing features, reducing the amount of data and time needed for training compared to starting from scratch.

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How Transferable Are Features in Deep Neural Networks? In Advances in Neural Information Processing Systems.

Answer : B

Explainer Loops: These are mechanisms or tools designed to interpret and explain the decisions made by AI models. They help users and developers understand the rationale behind a model's predictions.

Importance: Understanding the model's reasoning is vital for trust and transparency, especially in critical applications like healthcare, finance, and legal decisions. It helps stakeholders ensure the model's decisions are logical and justified.

Methods: Techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are commonly used to create explainer loops that elucidate model behavior.

Answer : C

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.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.

Dean, J. (2020). AI and Compute. Google Research Blog.