Dual-system order event ingestion
Build one reliable event layer from two feeds and explain which records must not enter analysis.
Open task and complete outputRead complete output firstCold-chain fulfilment operations practical
Complete five connected assignments, from reliable event ingestion to fulfilment analysis, access investigation, service review and a delivery proposal. Open every input, complete output and reference answer on this site.
197 mature orders, 90 late orders and 10 open overdue orders. The access investigation identifies six unauthorized exports and one expired-session read.
216 orders · 640 source records · 69 access requests
Every material and full answer can be read here. Checkpoints let you start each task independently.
Build one reliable event layer from two feeds and explain which records must not enter analysis.
Open task and complete outputRead complete output firstIdentify priority orders and hubs using a valid historical window, and deliver a readable decision report.
Open task and complete outputRead complete output firstUse policy, session and request evidence to identify control gaps and propose verifiable actions.
Open task and complete outputRead complete output firstControl for service mix and sample size before setting hub review priorities.
Open task and complete outputRead complete output firstTurn the preceding evidence into a scoped, estimated and testable delivery proposal.
Open task and complete outputRead complete output firstThe five tasks connect event ingestion, operations analysis, access investigation, service review and a delivery proposal. Open the completed workbench and full reports first, then produce your own outputs. Suggested task effort is 18–24 hours, with 2–4 more for review and revision.
learner/task-01.md through task-05.md contain instructions. learner/materials contains raw data, the dictionary and stakeholder brief. learner/checkpoints provides complete intermediate results so each task can be started independently. instructor/answers contains full reference outputs, and instructor/scripts contains runnable Python and tests.
Use Python 3.10 or later. No third-party Python packages are required. Keep CSV input as UTF-8 and preserve timezone text when opening it in a spreadsheet application. The reference calculation does not depend on spreadsheet date parsing.
From the extracted pack directory run:
python3 --version
python3 instructor/scripts/solution.py learner/materials my-results
python3 instructor/scripts/test_lab.py
The reference command writes a new my-results directory without modifying the raw inputs. If Python is absent, install it through your operating system's official channel. Implement learner/starter.py and run python3 smoke_test.py from learner. Its unfinished functions identify the work to complete. Explain your own decisions and revisions rather than representing a reference run as independent work.
Five tasks are worth 20 points each. Run the learner's code, trace conclusions to raw records, and require critical rule corrections even when the score exceeds 14. Actual practitioner allocation remains a delivery decision; do not pre-fill a practitioner's identity or claim that a review has already happened.
Ask the learner to open the output and give a conclusion in three minutes, trace one piece of evidence in five, recalculate a boundary in five, and record one revision plus its acceptance check. Retain the original, revised output and test record.
Record finding → evidence → affected conclusion → correction → re-test → learner explanation. Complete at least one revision per task. Different visualizations, wording and architectures can pass when their rules, evidence, assumptions and acceptance are sound. A reference answer is not a script to memorize. Do not invent employment, client endorsement or outcome claims.