WGU | D602 DEPLOYMENT TASK 3 | COMPLETE WORKED EXEMPLAR
WESTERN GOVERNORS UNIVERSITY | D602
Program Deployment
Task 3: Flight Delay Prediction API Implementation
Comprehensive worked exemplar: GitLab organization, FastAPI root and prediction endpoints, request
validation, ten automated API tests, two Dockerfiles, code-development explanation, demo script, evidence
checklist, and full source appendices.
Version: 2026/2027 Updated | Prepared: 27 October 2026 | Reference implementation: Python 3.12 +
FastAPI
Evidence boundary: The attached 2025 student report provides the A-G section framework and example
endpoint contract. The original current WGU rubric, repository history, Docker build, model artifact, and
demonstration video were not supplied. This document is an original independently developed reference,
not verified assignment completion.
Model honesty: The available Task 2 model inputs do not match the four Task 3 API arguments. The
executable example returns a labeled historical-average baseline from supplied data, not invented
machine-learning output. Its sample data are synthetic.
Reproducible reference implementation | 2026/2027 Page 1
,WGU | D602 DEPLOYMENT TASK 3 | COMPLETE WORKED EXEMPLAR
Document map and verification status
Section Content Evidence status
A GitLab repository, folder layout, example commits Repository actions require actual GitLab
B1-B2 Root endpoint; delay endpoint; validation and data Working source; tests passed locally
policy
C API automated tests covering success and failure 10 pytest tests passed locally
D Dockerfiles V1 and V2; build/run procedure Docker commands given; build not run here
E Development process, architecture, challenges and Design explanation; no invented build evidence
controls
F Screen-recording demonstration plan and request Script exists; no video recorded
matrix
G References Uploaded report plus external official
documentation
Appendices Full source code, test suite, Dockerfiles, demo script Files also provided separately
Executive summary
The project demonstrates development of a cross-platform Python FastAPI web service for analyzing airline
departure delays. The application exposes a root operational endpoint and a parameterized GET endpoint for
estimated delay information. It validates 30 common US airport identifiers, verifies four required request
values, obtains delay observations from a processed flight-history CSV, and returns structured JSON with the
estimate and its provenance.
The project includes an introductory API implementation and an enhanced version, automated tests written
with pytest and FastAPI TestClient, a basic Dockerfile and a non-root Dockerfile with a health check, as well
as a repeatable demonstration script. The enhanced design follows modular software development
principles and uses source-code organization compatible with Windows, macOS, and Linux.
Unlike a fabricated mock predictor, the reference returns an explicitly identified historical-average baseline
computed from provided observations. A genuine trained regression model can only be deployed when its
expected features, input schema, and supporting preprocessing metadata have been reconciled with the API
request contract.
A. GitLab Repository
A1. Project setup and objective
A dedicated GitLab project should be created within the designated WGU subgroup following the course
GitLab instructions. GitLab serves as the authoritative source-control location for application logic, tests,
dependency specifications, Docker configurations, a demonstration utility, and the project README. Its
branching and commit history support reproducibility and evidence of progressive implementation.
Reproducible reference implementation | 2026/2027 Page 2
, WGU | D602 DEPLOYMENT TASK 3 | COMPLETE WORKED EXEMPLAR
A2. Project layout
D602-Flight-Delay-Prediction-API/
app/
__init__.py
main.py
tests/
test_api.py
examples/
synthetic_cleaned_data.csv
api_v1.py
Dockerfile.v1
Dockerfile.v2
requirements.txt
demo_script.py
README.md
.dockerignore
.gitignore
Production airline datasets, local environment files, credentials, and generated caches should be excluded
from Git where appropriate. The included .dockerignore prevents accidental inclusion of the project data
directory inside Docker images.
A3. Development and commit evidence
Milestone Proposed commit description
1 Initialize D602 Flight Delay API
2 Add root endpoint with JSON health response
3 Add route-delay estimator and four query parameters
4 Validate airports and local time strings
5 Add tests for valid and invalid requests
6 Create Dockerfile Version 1
7 Add hardened Dockerfile and runtime health check
8 Document execution and demonstration
These are example commits, not assertions that a repository has been created. After committing and
pushing the actual work, insert the precise GitLab URL and real dated commit/branch evidence into
submission comments.
Example initialization commands
git init
git branch -M main
git add .
git commit -m "Initialize D602 API project"
git remote add origin <YOUR_ACTUAL_GITLAB_URL>
git push -u origin main
Reproducible reference implementation | 2026/2027 Page 3
WESTERN GOVERNORS UNIVERSITY | D602
Program Deployment
Task 3: Flight Delay Prediction API Implementation
Comprehensive worked exemplar: GitLab organization, FastAPI root and prediction endpoints, request
validation, ten automated API tests, two Dockerfiles, code-development explanation, demo script, evidence
checklist, and full source appendices.
Version: 2026/2027 Updated | Prepared: 27 October 2026 | Reference implementation: Python 3.12 +
FastAPI
Evidence boundary: The attached 2025 student report provides the A-G section framework and example
endpoint contract. The original current WGU rubric, repository history, Docker build, model artifact, and
demonstration video were not supplied. This document is an original independently developed reference,
not verified assignment completion.
Model honesty: The available Task 2 model inputs do not match the four Task 3 API arguments. The
executable example returns a labeled historical-average baseline from supplied data, not invented
machine-learning output. Its sample data are synthetic.
Reproducible reference implementation | 2026/2027 Page 1
,WGU | D602 DEPLOYMENT TASK 3 | COMPLETE WORKED EXEMPLAR
Document map and verification status
Section Content Evidence status
A GitLab repository, folder layout, example commits Repository actions require actual GitLab
B1-B2 Root endpoint; delay endpoint; validation and data Working source; tests passed locally
policy
C API automated tests covering success and failure 10 pytest tests passed locally
D Dockerfiles V1 and V2; build/run procedure Docker commands given; build not run here
E Development process, architecture, challenges and Design explanation; no invented build evidence
controls
F Screen-recording demonstration plan and request Script exists; no video recorded
matrix
G References Uploaded report plus external official
documentation
Appendices Full source code, test suite, Dockerfiles, demo script Files also provided separately
Executive summary
The project demonstrates development of a cross-platform Python FastAPI web service for analyzing airline
departure delays. The application exposes a root operational endpoint and a parameterized GET endpoint for
estimated delay information. It validates 30 common US airport identifiers, verifies four required request
values, obtains delay observations from a processed flight-history CSV, and returns structured JSON with the
estimate and its provenance.
The project includes an introductory API implementation and an enhanced version, automated tests written
with pytest and FastAPI TestClient, a basic Dockerfile and a non-root Dockerfile with a health check, as well
as a repeatable demonstration script. The enhanced design follows modular software development
principles and uses source-code organization compatible with Windows, macOS, and Linux.
Unlike a fabricated mock predictor, the reference returns an explicitly identified historical-average baseline
computed from provided observations. A genuine trained regression model can only be deployed when its
expected features, input schema, and supporting preprocessing metadata have been reconciled with the API
request contract.
A. GitLab Repository
A1. Project setup and objective
A dedicated GitLab project should be created within the designated WGU subgroup following the course
GitLab instructions. GitLab serves as the authoritative source-control location for application logic, tests,
dependency specifications, Docker configurations, a demonstration utility, and the project README. Its
branching and commit history support reproducibility and evidence of progressive implementation.
Reproducible reference implementation | 2026/2027 Page 2
, WGU | D602 DEPLOYMENT TASK 3 | COMPLETE WORKED EXEMPLAR
A2. Project layout
D602-Flight-Delay-Prediction-API/
app/
__init__.py
main.py
tests/
test_api.py
examples/
synthetic_cleaned_data.csv
api_v1.py
Dockerfile.v1
Dockerfile.v2
requirements.txt
demo_script.py
README.md
.dockerignore
.gitignore
Production airline datasets, local environment files, credentials, and generated caches should be excluded
from Git where appropriate. The included .dockerignore prevents accidental inclusion of the project data
directory inside Docker images.
A3. Development and commit evidence
Milestone Proposed commit description
1 Initialize D602 Flight Delay API
2 Add root endpoint with JSON health response
3 Add route-delay estimator and four query parameters
4 Validate airports and local time strings
5 Add tests for valid and invalid requests
6 Create Dockerfile Version 1
7 Add hardened Dockerfile and runtime health check
8 Document execution and demonstration
These are example commits, not assertions that a repository has been created. After committing and
pushing the actual work, insert the precise GitLab URL and real dated commit/branch evidence into
submission comments.
Example initialization commands
git init
git branch -M main
git add .
git commit -m "Initialize D602 API project"
git remote add origin <YOUR_ACTUAL_GITLAB_URL>
git push -u origin main
Reproducible reference implementation | 2026/2027 Page 3
