AM2 Flashcards
Describe a problem that you solved using applied research and data modelling techniques to design and refine the database and storage architectures. How did you ensure the security, stability, and scalability of the data products delivered to the business?
Explain how you interpreted the organisational policies, standards and guidelines in relation to AI and data to ensure compliance and adherence to ethical, legal and regulatory frameworks.
How does your role fit with and support the organisational strategy and objectives related to AI and data science? Can you provide examples of how you have contributed to achieving these objectives?
What do you think are the most significant roles and impacts of AI, data science, and data engineering in industry and society? How can businesses harness these technologies to create value and benefit society?
Describe your experience with high-performance computer architectures and how you have made effective use of these in your work. Can you provide examples of how this has led to improved business solutions?
How do you stay updated with current industry trends across AI and data science, and how do you apply these to your work? Can you provide examples of how you have implemented these trends to improve business outcomes?
Discuss the programming languages and techniques that you have used for data engineering, and how you have applied them to create commercially beneficial scientific analysis and simulation.
Explain the principles and properties behind statistical and machine learning methods, and how you have used these techniques to solve complex business problems. Can you provide examples of how you have evaluated and improved these models?
Describe how AI and data science techniques have supported and enhanced the work of other members of the analytical team, and how you have collaborated with software engineers to ensure suitable testing and documentation processes are implemented.
How do you identify and consider the associated regulatory, legal, ethical, and governance issues when evaluating choices at each stage of the data process? Can you provide examples of how you have addressed these issues in your work?
Can you describe a problem you solved using data modelling, and how you evaluated the solution using test data and results from research, feasibility, acceptance and usability testing?
How do you ensure that your work in AI and data science adheres to organisational policies, standards and guidelines? Can you give an example?
How do you ensure that your role in AI and data science aligns with and supports the overall objectives and strategy of the organisation? Can you give an example?
In your opinion, what are some of the most significant impacts that AI, data science, and data engineering are having on industry and society?
Can you explain high-performance computer architectures and how you have made effective use of them in your work in AI and data science?
How do you stay up-to-date with current industry trends in AI and data science, and how have you applied them in your work?
Can you describe a programming language or technique that you have used in data engineering, and how it was applicable to your work?
What principles and properties do you consider when selecting statistical and machine learning methods for your work in AI and data science?
How do you ensure that AI and data science techniques support and enhance the work of other members of the analytical team? Can you give an example?
Can you explain the relationship between mathematical principles and core techniques in AI and data science within the context of your organisation?
What programming languages and modern machine learning libraries have you
used to meet business needs? (K25)
How do you make effective use of high-performance computer architectures? (K16)
What are the principles and properties behind statistical and machine learning methods? (K19)
What is the relationship between mathematical principles and core techniques in AI and data science within the organizational context? (K22)
How do you evaluate software solutions via analysis of test data and results from research, feasibility, acceptance, and usability testing? (K7)
How does your role fit with and support organizational strategy and objectives? (K10)
How do AI and data science techniques support and enhance the work of other members of the analytical team? (K21)
How do you provide direction and technical guidance for the business with regard to AI and data science opportunities? (S6)
How do you coordinate, negotiate with, and manage expectations of diverse stakeholders, suppliers with conflicting priorities, interests, and timescales? (S8)
How do you disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice? (S23)
What are some recent trends in ML/AI and relative ethical and social concerns in a business context? (K11)
How do you identify current industry trends across AI and data science and how to apply these? (K17)
How do you interpret organizational policies, standards, and guidelines in relation to AI and data? (K8)
How do you consider the associated regulatory, legal, ethical, and governance issues when evaluating choices at each stage of the data process? (S12)
How do you select and apply the most effective/appropriate AI and data science techniques to solve complex business problems? (S26)
How do you maintain awareness of trends and innovations in the subject area, utilizing a range of academic literature, online sources, community interaction, conference attendance, and other methods? (B8)
How do you commit to continuous professional development, maintaining your knowledge and skills in relation to AI developments that influence your work?
