Build a small project plan you can verify

Before you begin

• Open entry; inspect the worked fixture before beginning.
• A text editor/spreadsheet for the manual route. Hosted Python/Jupyter for code exercises; applicable dependencies are in requirements.txt.
• For actual AI calls, a legitimate available cloud service and authorised learner credentials; no particular provider or country is mandatory.

Free original, non-accredited practice. Self-review is not independent assessment. No particular provider, payment platform or country is mandatory; use a legitimate available route or explicitly labelled manual practice.

Map deliverables and dependencies

A plan needs dependency logic and capacity limits, not just attractive dates. Calculate the earliest possible finish under explicit assumptions. A change in duration must propagate to dependent work rather than remain hidden in a cached report. Write each predecessor before drawing dates. Inspect the supplied input before using any tool. Write the decision rules in your own words and calculate one small result independently. The reference output is an answer key for this deliberately bounded fixture, not proof of general capability. Policy for this project: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

SOURCE (authored fixture):
{
  "duration": {
    "A": 2,
    "B": 3,
    "C": 1
  },
  "depends": {
    "A": [],
    "B": [
      "A"
    ],
    "C": [
      "A"
    ]
  }
}
INDEPENDENT FIXTURE TARGET:
{
  "finish": {
    "A": 2,
    "B": 5,
    "C": 3
  },
  "project_days": 5
}

Your practical task

1. Write each predecessor before drawing dates.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record a manual prediction and the relevant rule before consulting the answer key.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Map deliverables and dependencies — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Add a dependency A after B: detect a cycle.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Calculate the earliest finish

A plan needs dependency logic and capacity limits, not just attractive dates. Calculate the earliest possible finish under explicit assumptions. A change in duration must propagate to dependent work rather than remain hidden in a cached report. Use a small dependency graph and hand-check the critical chain. Establish a baseline before changing prompts or code. Keep the original input immutable. Record expected values, observed values and whether each comparison passed. If the expected file conflicts with a justified policy, investigate rather than forcing a convenient match. Policy for this project: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated.

Worked example

MANUAL BASELINE / compare every named field
{
  "finish": {
    "A": 2,
    "B": 5,
    "C": 3
  },
  "project_days": 5
}
Keep a separate observed-result column; the answer key is not a model response.

Your practical task

1. Use a small dependency graph and hand-check the critical chain.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Calculate the earliest finish — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Increase A to 4 days: project duration becomes 7.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Build a change-aware plan

A plan needs dependency logic and capacity limits, not just attractive dates. Calculate the earliest possible finish under explicit assumptions. A change in duration must propagate to dependent work rather than remain hidden in a cached report. Compute finish times from input durations rather than typing static dates. Build one inspectable transformation or bounded AI prompt. Keep calculation and validation deterministic where possible. If you make a permitted hosted call, save the actual response separately from the authored examples and verify its material claims; the supplied reference never simulates a live model. Policy for this project: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated.

Worked example

PROJECT CONTRACT:
Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated.
HOSTED REFERENCE: open hosted-lab.ipynb or run reference.py with all fixture files together.
EXPECTED CONSTRUCTED RESULT:
{
  "finish": {
    "A": 2,
    "B": 5,
    "C": 3
  },
  "project_days": 5
}

Your practical task

1. Compute finish times from input durations rather than typing static dates.
2. Open hosted-lab.ipynb and the reference code from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Only one worker available: parallel execution is no longer a valid assumption.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Build a change-aware plan — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Only one worker available: parallel execution is no longer a valid assumption.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Test a cycle and capacity change

A plan needs dependency logic and capacity limits, not just attractive dates. Calculate the earliest possible finish under explicit assumptions. A change in duration must propagate to dependent work rather than remain hidden in a cached report. Reject invalid dependency graphs; document resource assumptions. Change one assumption at a time and predict the result first. Retain the failure, the revision and the rerun evidence. A failure is useful information: hiding a bad output or editing a test target after seeing the answer destroys the value of the experiment. Policy for this project: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated.

Worked example

CHANGE TESTS — predict before running:
1. Add a dependency A after B: detect a cycle.
2. Increase A to 4 days: project duration becomes 7.
3. Only one worker available: parallel execution is no longer a valid assumption.
Keep the original expected.json unchanged.

Your practical task

1. Reject invalid dependency graphs; document resource assumptions.
2. Open failure-tests.md from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Add a dependency A after B: detect a cycle.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Test a cycle and capacity change — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Add a dependency A after B: detect a cycle.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Rehearse stakeholder communication

A plan needs dependency logic and capacity limits, not just attractive dates. Calculate the earliest possible finish under explicit assumptions. A change in duration must propagate to dependent work rather than remain hidden in a cached report. Name an owner and uncertainty without inventing executive authority. Review what the fixture cannot establish. Explain who owns the decision, where human approval is needed and which access or professional constraints remain. Do not describe a small educational success as production readiness, certification, legal compliance or a guarantee. Policy for this project: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run. Supporting practice — Leadership as delivery and accountability: A useful project owner communicates scope, uncertainty, decisions and responsibility. A plan generated by AI does not give authority over people. Practise a change conversation that names trade-offs and asks for approval rather than promising impossible dates. Produce a stakeholder handoff, owner/decision table and retrospective with one negotiated change.

Worked example

REVIEW BOUNDARY:
Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated.
Produce a stakeholder handoff, owner/decision table and retrospective with one negotiated change.
ACCESS / NOT TESTED: unavailable provider, unsupported language and absent external feedback must remain explicit.

Your practical task

1. Name an owner and uncertainty without inventing executive authority.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.
6. Produce a stakeholder handoff, owner/decision table and retrospective with one negotiated change.

Evidence to keep

• Rehearse stakeholder communication — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.
• Produce a stakeholder handoff, owner/decision table and retrospective with one negotiated change.

Mistakes to watch for

• Increase A to 4 days: project duration becomes 7.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Deliver a plan and retrospective template

A plan needs dependency logic and capacity limits, not just attractive dates. Calculate the earliest possible finish under explicit assumptions. A change in duration must propagate to dependent work rather than remain hidden in a cached report. Track actual work separately from estimates. Deliver a reproducible bundle rather than a screenshot alone. Include inputs, rules, output, failure tests, a short README and residual limitations. Ask another person to follow the instructions if possible; self-review and actual external review must be labelled separately. Policy for this project: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated.

Worked example

HANDOFF MANIFEST:
input.json — immutable source
expected.json — fixture answer key
actual.json — result actually observed
evidence-log.csv — baseline plus three changes
README.md — reproduction and access route
limitations.md — unresolved failures, rights, approvals and scope
Never bundle a credential.

Your practical task

1. Track actual work separately from estimates.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Deliver a plan and retrospective template — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Only one worker available: parallel execution is no longer a valid assumption.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Capstone self-review

Score each criterion 0 (missing), 1 (partial), or 2 (demonstrated). A self-review pass needs at least 8/10 and 2/2 on the critical safety/limits criterion. This is learner practice feedback, not verified assessment.

• Defined task and preserved input: Original input and the project policy are retained; the actual question is clear.
• Checked baseline: The supplied baseline reconciles field by field; unsupported live-run claims are absent.
• Three failure/change tests: All three specified changes have predictions, observed results and explanations.
• Repeatable handoff: Another person can follow the README; supporting practice evidence and unresolved limits are included.
• Safety, rights and access honesty — critical: Task A takes 2 days, B takes 3 after A, and C takes 1 after A. B and C can run in parallel. No staffing or safety-engineering decisions are automated. Regional access route is legitimate; credentials and private data are absent; manual/constructed work is clearly labelled.

Review questions

Practice files

• README.md
• evidence-log.csv
• expected.json
• failure-tests.md
• hosted-lab.ipynb
• input.json
• lab_engine.py
• reference.py
• requirements.txt

References and editorial limits

Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated.

• Python graphlib documentation

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