Level 5
Alongside skill creation and AI design explanations, explain commands to install, configure and update tools.
Who can apply
No age limit. Students under 12 can sit this level.
Screen-sharing interview
The examiner checks the computer before asking questions. Respond immediately and complete the requested tasks promptly.
- In Zoom, share your entire screen, not just a window or tab.
- Give the examiner of the shared computer. They first check the ’s display settings to confirm that no additional monitor is connected.
- After the check, answer questions and perform tasks yourself on the shared screen. Using the tools being tested is part of the interview; secretly outsourcing answers to or another person is cheating.
Level 5: example interview tasks
- When asked to create a , start typing immediately and finish within 30 seconds. Then verify that it actually works within 60 seconds after creation. To prevent AI-assisted cheating, skill creation is limited to 30 seconds, with an additional 60 seconds to demonstrate that it works.
- Questions cover commands for installing, configuring and updating tools such as , , , , and . Without hesitation, enter the commands yourself to perform installation and configuration.
- When asked how you would create an AI for a specific topic, start answering without hesitation and finish explaining the entire process within 3 minutes.
The 60-second verification period starts after creation is complete; it is separate from the 30-second creation period.
Several tasks of this difficulty are given. This is the entry-level standard for level 5.
Required programming language
Required at every level, from Level 5 to Level 1.
Shell Scripting
The base language for running the computers that build and run .
This certificate requires at every level, from 5 to 1. Building an AI from scratch — fetching , running , moving results and putting a on a — usually happens on the , and whatever language the model is written in, the runs it, joins the pieces and the work. (Some models can also be built with visual no-code tools, and just using AI in a or app needs no . This certificate asks for the shell because it tests building and automating.)
Learn shell programmingExample design question
How would you build an AI for a specified topic? Explain its purpose, data, tools and how you would verify the result.
This is one design-question example. The interview includes several questions and tasks appropriate to each level, not this question alone.
What to learn
- Start typing immediately and create the requested within 30 seconds.
- Verify that the skill works within 60 seconds after creation.
- Start explaining a topic-specific AI design immediately and cover the entire creation process within 3 minutes.
How you are marked
- Was the skill completed within 30 seconds?
- Was its actual operation verified within 60 seconds after creation?
- Were several questions at this level answered promptly and independently?
Exam format
There is no written exam and no practical exam. A single Zoom interview with the entire decides the level. The examiner asks questions suited to the level, you explain right away in your own words, and follow-up questions check how deep your understanding goes.
Practice interview questions
These 20 questions are examples of what the examiner may ask in the interview. Do not try to hit a single correct word — explain your answer out loud. Open each one to see a sample explanation and a follow-up question. They are not official exam questions.
Basic AI words
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1
Examiner Explain what stands for and what it means.
Show a sample explanation
Sample explanation AI stands for Artificial Intelligence. Artificial means "made by people" and Intelligence means "the ability to think and learn".
Follow-up question Name one AI you used today and say what it does for you.
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2
Examiner Explain what an is, with an example from everyday life.
Show a sample explanation
Sample explanation An algorithm is a procedure that writes down the steps for solving a problem in order. Like the steps for cooking instant noodles, the steps are fixed, and a computer follows the algorithm people made exactly. Most algorithms are designed to give the same result for the same input, but some deliberately use randomness, so their results can differ from run to run.
Follow-up question How could the result change if you swapped one step of the algorithm?
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3
Examiner Explain what is and how it differs from people writing the rules.
Show a sample explanation
Sample explanation Machine learning lets the computer find the rules by itself from many examples, instead of people writing each rule. For example, if you show it lots of spam and normal mail, the computer finds the of spam by itself.
Follow-up question Give an example of a task whose rules are hard for people to write down.
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4
Examiner Explain what is and why good matters.
Show a sample explanation
Sample explanation Training data is the many examples (photos, text, sounds and so on) shown to an so it can learn. An AI's answers resemble its training data, so if it learns from wrong data it gives wrong answers.
Follow-up question If you were building an AI that recognises cats, what photos would you collect?
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5
Examiner Explain what a is, using photo as an example.
Show a sample explanation
Sample explanation A label is the answer tag attached to each piece of , such as "cat" on a cat photo and "dog" on a dog photo. Learning to give correct answers from labelled data is called .
Follow-up question What happens to the if many photos have the wrong label?
Models, neural networks and vectors
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6
Examiner Explain what a is in and how it relates to .
Show a sample explanation
Sample explanation A model is the result of : what it learned is stored as numbers, and when it gets an input it calculates and gives an output. , and are all names of , and actually running a trained model to get answers is called inference.
Follow-up question Which usually needs more computation, training or inference?
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7
Examiner Explain what a is and how it is like and unlike the human brain.
Show a sample explanation
Sample explanation A neural network (artificial neural network) is a inspired by the nerve cells () of the human brain. It connects simple calculating units in layers: each unit multiplies its inputs by , adds them up and passes the result through an . adjusts those weights from so the learns patterns. It is inspired by the brain, but it does not work the way a real brain does.
Follow-up question What does a "layer" of a neural network mean?
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8
Examiner Explain what is and what "deep" means.
Show a sample explanation
Sample explanation Deep learning is the method that stacks many layers of a deep and trains them. Deep means there are many layers, and most today is based on deep learning.
Follow-up question How are machine learning and deep learning related?
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9
Examiner Explain what a is and why a computer turns text, pictures and sound into vectors.
Show a sample explanation
Sample explanation A vector is a list of several numbers in a fixed order, such as [0.2, -1.5, 3.0]; with 3 numbers it is a 3-dimensional vector. A computer cannot understand text, pictures or sound directly, so it turns them into lists of numbers like this to calculate.
Follow-up question If you turned a photo into a list of numbers, what would you write down as numbers?
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10
Examiner Explain what an is, using "puppy" and "dog" as an example.
Show a sample explanation
Sample explanation An embedding turns the meaning of a word or sentence into a (a list of numbers), so words with similar meanings such as "puppy" and "dog" get numbers close to each other. The closer the meaning, the closer the vectors, so search, recommendation and use embeddings to compare meaning.
Follow-up question Why can embeddings find texts with the same meaning even when the words differ?
Language models and conversation
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11
Examiner Explain what stands for and what kind of it is.
Show a sample explanation
Sample explanation An LLM (Large Language Model) is a huge trained on an enormous amount of text so it understands human language and continues sentences. Chat AIs such as , and are all based on LLMs.
Follow-up question What does an LLM not know, and what should you do then?
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12
Examiner Explain what a is and how a uses tokens to write text.
Show a sample explanation
Sample explanation A token is the basic unit a language model splits text into — a whole word or a piece of a word. A language model looks at the earlier tokens, scores what the next token should be, and writes the text one piece at a time.
Follow-up question Why can the same sentence have a different number of tokens in different languages?
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13
Examiner Explain what a is and what changes when you write a good one.
Show a sample explanation
Sample explanation A prompt is the question or instruction you type to get an such as a to do something. The clearer the prompt, the easier it is to get the answer you want — but even a good prompt does not guarantee a correct answer.
Follow-up question Say how you would improve the prompt "write something".
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14
Examiner Explain what an is and how to guard against it.
Show a sample explanation
Sample explanation A hallucination is when an AI makes up untrue or unsupported content and says it convincingly. It does not mean the machine really hallucinates; it is a name for output that sounds right but is wrong, so always check important content against other sources.
Follow-up question How would you check whether a book title an AI told you really exists?
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15
Examiner Explain what the — the last T in 's — is.
Show a sample explanation
Sample explanation GPT is short for Generative Pre-trained Transformer, and Transformer is the name of the design behind most of today's . The Transformer was first presented in 2017 in the paper " Is All You Need" by Google researchers.
Follow-up question What do the G (Generative) and P (Pre-trained) in GPT each mean?
AI history, names and tools
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16
Examiner Explain the question Alan Turing asked in 1950 and the test he proposed.
Show a sample explanation
Sample explanation The British mathematician Alan Turing asked "Can machines think?" in 1950 and proposed a test (the ) of whether a machine can hold a conversation like a person. It is called the Turing test, and talking like a person does not mean a machine has a mind.
Follow-up question Do you think today's have passed the Turing test? Why?
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17
Examiner Explain what is and how its 2016 match against Lee Sedol ended.
Show a sample explanation
Sample explanation AlphaGo is a Go-playing made by . In March 2016 it played five games against Lee Sedol 9-dan and won 4 to 1; Lee Sedol won the fourth game.
Follow-up question Why was Go thought to be harder for AI than chess?
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18
Examiner Explain , the company that made , and why ChatGPT's release in 2022 mattered.
Show a sample explanation
Sample explanation ChatGPT runs on the family of made by OpenAI. Released in November 2022, it made chat widely known around the world.
Follow-up question Name one good thing and one worrying thing that came with chat AI becoming widely used.
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19
Examiner Explain , the company that made , and show with examples that different come from different companies.
Show a sample explanation
Sample explanation Claude is an AI model made by Anthropic. Every AI model has a company behind it — for example, was made by Google and by .
Follow-up question Why can different AIs give different answers to the same question?
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20
Examiner Explain mkdir, the () command that makes a new folder, with an example.
Show a sample explanation
Sample explanation mkdir is short for make and is the command that makes a new folder (directory). For example, mkdir my-ai makes a folder called my-ai.
Follow-up question Which commands would you use to go into that folder and see the list of files inside it?