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Your hands-on investigation

Run each lab's verified baseline first. Then use the activity below to build your own explanation and artifact. The runner proves a baseline works; your investigation shows whether you can apply the concept to a changed situation.

Before independent work, use the guided-to-independent progression for your lab. It provides a worked starting point and a supported intermediate task.

Execute your own SQL without managing passwords

From the repository root:

.venv/bin/python scripts/query.py labs/practice/inspect_register.sql

The helper uses the same private .env and non-administrator builder. It executes SQL you supply, so read the file first and keep experiments inside this disposable database. {{schema}} is replaced with your safely quoted configured schema. It prints the final statement's rows as JSON. SQL errors show SQLSTATE codes without printing connection details. Put personal drafts under ignored .local/. For temporary experiments use BEGIN; ... ROLLBACK; in the same file.

Lab 1: Explain the boundary

Follow the canonical lab page for the concept, investigation and completion requirements.

Lab 2: Classify an additional field

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Lab 3: Trace grain and test a hypothesis

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Lab 4: Use lineage to make a decision

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Lab 5: Predict, authenticate, compare

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Lab 6: Investigate evidence, not just alerts

Follow the canonical lab page for the concept, investigation and completion requirements.

Lab 7: Make an accountable AI decision

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Lab 8: Challenge retention behavior

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Lab 9: Discuss deployment implications

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Author: Isaac K. Nti.