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This assessment centers on the basic principles of artificial intelligence (AI) and machine learning (ML). Successfully completing this Achievement Assessment not only confirms your expertise and understanding of these principles but also showcases your knowledge of deep learning methods and the broader implications of AI. Additionally, it underscores the relevance and importance of AI in the current business environment. The assessment questions are crafted to evaluate your understanding of how AI can facilitate human progress, foster innovation, and transform various business operations. By answering these questions, you will demonstrate your ability to grasp the significant impact of AI technologies in modern enterprises and their potential to transform industries.

● The impact and scope of artificial intelligence

● Concepts of artificial intelligence and machine learning

● Challenges and the application of artificial intelligence

● Concepts of machine learning, deep learning, and neural network

● Concepts of large language models (LLMs) and the role they play in generative AI.

● Concepts of building AI ecosystem.

● How AI is implemented in a business model and the impact of AI in business.

● Ethics in AI; various ethical issues principles, different types of biases and their impacts and what culture should be developed to reduce bias and increase the trust of humans over machines.

1. Understand the Basics: Make sure you have a solid understanding of the fundamental concepts of AI and ML. This will form the foundation upon which more advanced topics are built.

2. Review the Exam Topics: Familiarize yourself with the exam topics listed above. Ensure you understand each topic and can explain it clearly.

3. Use Multiple Resources: Study from a variety of resources such as textbooks, online courses, and research papers. Different perspectives can provide a more comprehensive understanding.

4. Practice with Real-World Examples: Apply AI and ML concepts to real-world problems. This can help you understand the practical applications and implications of these technologies.

5. Join Study Groups: Connect with others preparing for the same exam. Study groups can provide support, share resources, and offer different insights.

6. Take Practice Tests: Practice exams can help you get used to the format of the test and identify areas where you need more study.

1. What is a principle thatguides organizations, government, and developers towards the ethical use of Al?

A. Only regulatory agencies should be held accountable for the accuracy, fairness, and use of Al models

B. The value of Al models must only be measured in financial gain.

C. Al models must ensure data privacy and confidentiality.

D. Al models must always agree with the user's point of view.

Answer: C

2. What role does human feedback play in Reinforcement Learning for LLMs?

A. It is used to provide real-time corrections to the model's output.

B. It helps in identifying the model's architecture for optimization.

C. It assists in the physical hardware improvement of the model.

D. It rewards good output and penalizes bad output to improve the model.

Answer: D

3. What is the significance ofparameters in Large Language Models (LLMs)?

A. Parameters are used to parse image, audio, and video data in LLMs.

B. Parameters are used to decrease the size of the LLMs.

C. Parameters are used to increase the size of the LLMs.

D. Parameters are statistical weights inside of the neural network of LLMs.

Answer: D

4. What are the three key patrons involved in supporting the successful progress and formation ofany Al-based application?

A. Customer facing teams, executive team, and facilities team

B. Marketing team, executive team, and data science team

C. Customer facing teams, HR team, and data science team

D. Customer facing teams, executive team, and data science team

Answer: D

5. What is the purpose of adversarial training in the lifecycle of a Large Language Model (LLM)?

A. To make the model more resistant to attacks like prompt injections when it is deployed in production

B. To feed the model a large volume of data from a wide variety of subjects

C. To customize the model for a specific task by feeding it task-specific content

D. To randomize all the statistical weights of the neural network

Answer: A

6. What strategy can an Al-based company use to develop a continuous improvement culture?

A. Limit the involvement of humans in decision-making processes.

B. Focus on the improvement of human-driven processes.

C. Discourage the use of Al in education systems.

D. Build a small Al community with people of similar backgrounds.

Answer: B

7. What is one of the objectives of Al in the context of digital transformation?

A. To become essential to the success of the digital economy

B. To reduce the need for Internet connectivity

C. To replace all human tasks with automation

D. To eliminate the need for data privacy

Answer: A

8. What impact does bias have in Al training data?

A. It enhances the model's performance uniformly across tasks.

B. It simplifies the algorithm's complexity.

C. It can lead to unfair or incorrect outcomes.

D. It ensures faster processing of data by the model.

Answer: C

9. Whatis the role of a decoder in a GPT model?

A. It takes the output and determines the input.

B. It is used to deploy the model in a production or test environment.

C. It takes the input and determines the appropriate output.

D. It is used to fine-tune the model.

Answer: C

10. What are common misconceptions people have about Al? (Select two)

A. Al can learn from mistakes.

B. Al can produce biased results.

C. Al is not prone to generate errors.

D. Al can think like humans.

Answer: D