AI and Python Foundations

Distinguish rules, machine learning and generative AI; classify six education and business tasks.

Evidence: A justified task map with at least five correct classifications.

Run cells in order, locate files and restart the environment; inspect a supplied dataset.

Evidence: A notebook that runs from a clean kernel without manual fixes.

Use strings, numbers, booleans and conversions to represent course enquiries.

Evidence: Five type and conversion tests pass, including a missing value.

Route enquiries with explicit conditions and process a list without duplicate outputs.

Evidence: Correct output for normal, empty and repeated input cases.

Write reusable validation functions and catch expected input errors.

Evidence: Three functions with clear inputs, return values and negative tests.

Transform lists and dictionaries; load and save a nested learner record.

Evidence: A round trip preserves required keys and handles an optional field.

Compare array operations with loops; calculate summary measures.

Evidence: Correct mean, range and array shape checks on a fixed fixture.

Load CSV data, inspect missingness and remove duplicate records responsibly.

Evidence: A cleaned file plus counts explaining every removed or altered row.

Create category and trend charts; describe what the data cannot establish.

Evidence: Two labelled charts and a limitation grounded in sample size or quality.

Call a mock course API; validate status codes, JSON and pagination.

Evidence: Tests cover success, empty results and a simulated server error.

Write small automated tests and commit a reproducible project structure.

Evidence: A repository containing tests, requirements and meaningful change history.

Identify privacy risks, human checks and a baseline for the final project.

Evidence: A project brief with synthetic data, acceptance criteria and review points.