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Free D-GAI-F-01 Exam Braindumps (page: 4)

Page 3 of 16
PDF with 58 Questions

What is the first step an organization must take towards developing an Al-based application?

  1. Prioritize Al.
  2. Develop a business strategy.
  3. Address ethical and legal issues.
  4. Develop a data strategy.

Answer(s): D

Explanation:

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.


Reference:

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.



What is the primary purpose of fine-tuning in the lifecycle of a Large Language Model (LLM)?

  1. To randomize all the statistical weights of the neural network
  2. To customize the model for a specific task by feeding it task-specific content
  3. To feed the model a large volume of data from a wide variety of subjects
  4. To put text into a prompt to interact with the cloud-based Al system

Answer(s): B

Explanation:

Definition of Fine-Tuning: Fine-tuning is a process in which a pretrained model is further trained on a smaller, task-specific dataset. This helps the model adapt to particular tasks or domains, improving its performance in those areas.

"Fine-tuning adjusts a pretrained model to perform specific tasks by training it on specialized data." (Stanford University, 2020)
Purpose: The primary purpose is to refine the model's parameters so that it performs optimally on the specific content it will encounter in real-world applications. This makes the model more accurate and efficient for the given task.

"Fine-tuning makes a general model more applicable to specific problems by further training on relevant data." (OpenAI, 2021)
Example: For instance, a general language model can be fine-tuned on legal documents to create a specialized model for legal text analysis, improving its ability to understand and generate text in that specific context.

"Fine-tuning enables a general language model to excel in specific domains like legal or medical texts." (Nature, 2019)



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

  1. To investigate the model requirements properly
  2. To perform exploratory data analysis
  3. To determine where and how the dataset is produced
  4. To cover all possible cases that the model should handle

Answer(s): 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.


Reference:

"Diverse data sources help AI models to generalize better and avoid biases." (MIT Technology Review, 2019)
Comprehensive Coverage: By incorporating diverse inputs, developers ensure the model can handle various edge cases and unexpected inputs, making it robust and reliable in real-world applications.
"Comprehensive data coverage is essential for creating robust AI models that perform well in diverse situations." (ACM Digital Library, 2021) Avoiding Bias: Diverse inputs reduce the risk of bias in AI systems by representing a broad spectrum of user experiences and perspectives, leading to fairer and more accurate predictions.
"Diverse datasets help mitigate bias and improve the fairness of AI systems." (AI Now Institute, 2018)



What is the purpose of the explainer loops in the context of Al models?

  1. They are used to increase the complexity of the Al models.
  2. They are used to provide insights into the model's reasoning, allowing users and developers to understand why a model makes certain predictions or decisions.
  3. They are used to reduce the accuracy of the Al models.
  4. They are used to increase the bias in the Al models.

Answer(s): B

Explanation:

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.


Reference:

"Explainer loops are crucial for interpreting the decisions of complex AI models." (IEEE Spectrum, 2020)

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.

"Transparency and explainability in AI models are essential for building trust and ensuring accountability." (Harvard Business Review, 2021)
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.

"Tools like SHAP and LIME provide insights into the factors influencing model decisions." (Nature Machine Intelligence, 2019)






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