Python Coding-and-Design Exercise in Google Colab
You are given an open-ended coding-and-design exercise to complete in Python, and the interviewer recommends working in Google Colab. The prompt is deliberately generic: there is no fixed spec handed to you, and part of what is being evaluated is how you turn an ambiguous task into working, well-engineered, reproducible code in a notebook environment.
Walk through your end-to-end approach for executing such an exercise in Colab. Your answer should make clear how you would move from an ambiguous prompt to a deliverable a reviewer can re-run, touching on: how you scope requirements; how you choose components, algorithms, and data structures; how you structure the code and notebook; how you handle I/O, configuration, logging, and error handling; how you test; how you reason about complexity and performance; how you manage dependencies and the environment; and how you ensure reproducibility and documentation.
Provide concrete steps, small illustrative code snippets where they add value, and best practices tailored specifically to Colab's environment and constraints (ephemeral VM, pre-installed-but-drifting libraries, out-of-order cell execution, limited and reclaimable RAM/accelerators).
Constraints & Assumptions
-
Environment:
Google Colab — a shared, ephemeral VM. Installs and files in
/content
do
not
survive a disconnect or factory reset; persistence requires Drive or a repo. Many libraries are pre-installed but at versions you do not control and that drift over time. Free-tier RAM is on the order of ~12 GB with a single modest CPU; GPU/TPU/high-RAM are not guaranteed and can be reclaimed mid-session.
-
Acceptance bar (assume unless told otherwise):
"Runtime → Restart and run all" completes top-to-bottom with no manual steps and produces the expected result deterministically.
-
Time-boxed:
treat this as a ~60–90 minute exercise, so favor a small correct contract and a working baseline over speculative scale-out.
-
Out of scope unless asked:
building a UI, distributed/cluster execution, production deployment, or persistence beyond writing artifacts to Drive.
Clarifying Questions to Ask
-
Input:
What is the format (CSV/JSON/text/array), the schema, the
expected size
, and the source (uploaded file, Drive mount, URL, generated)?
-
Output:
What exact return type/schema is required, and what does "correct" mean — can you pin down one or two concrete
input → expected output
acceptance examples?
-
Performance & scale:
What input scale and latency target should the solution handle (
n≈103
vs.
108
changes everything)?
-
Environment constraints:
Is internet access allowed during the run? Any PII/security concerns? Must it run offline after setup?
-
Deliverables:
What exactly is reviewed — just the notebook, or notebook +
src/
modules + tests + README? Is "Restart and run all passes" the acceptance test?
What a Strong Answer Covers
A strong answer walks the full lifecycle of the exercise and addresses each of the following dimensions with concrete, Colab-aware reasoning (and small snippets where they help):
-
Requirements scoping:
turning the ambiguous prompt into a written contract — functional requirements (inputs, outputs, operations, edge cases) and non-functional ones (performance target, resource limits, constraints, deliverables/repro bar). Captures assumptions and acceptance examples up front.
-
Components, algorithms & data structures:
choosing structures by access pattern (e.g.
dict
for keyed lookup,
set
for membership,
Counter
/
heapq
for counting/top-k,
deque
for windows, pandas/numpy for tabular/numeric), letting the input-scale answer drive algorithm complexity, and consciously avoiding accidental
O(n2)
; preferring streaming/generators when data may not fit in memory; baseline-first then optimize.
-
Code & notebook structure:
keeping graded
logic
in importable
.py
modules (pure, testable) and using the notebook as narrative + driver; sensible module boundaries (config / io / core / pipeline); functions-first, classes only when there is real state; idempotent, in-order cells;
%autoreload
to wire modules in.
-
I/O, configuration, logging, error handling:
isolating side effects from a pure core; a single typed config source of truth; the
logging
module over bare
print
; validation at the boundary with fail-fast, actionable, narrowly-caught errors.
-
Testing in Colab:
treating tests as a primary signal; running
pytest
/
unittest
inline so the reviewer sees green; covering happy path, empty/degenerate, boundary, and malformed-input-raises; optionally property-based tests and mocked I/O.
-
Complexity & performance:
stating time/space complexity for core functions and justifying it;
measuring
(
%timeit
,
cProfile
, memory profiling) rather than guessing; the correct → measured → optimized ordering; re-running tests after optimizing.
-
Dependencies & environment:
pinning versions against Colab's drifting pre-installs, keeping the install cell in the notebook, handling the "restart runtime after upgrading a core lib" gotcha, and printing the environment for reproducibility.
-
Reproducibility & documentation:
seeding determinism (and the
PYTHONHASHSEED
-only-takes-effect-at-startup subtlety), the "Restart and run all" gold standard, writing artifacts alongside the config/env that produced them, and reviewer-facing docs (top-of-notebook overview, section headers, docstrings, README with limitations and next steps).
Follow-up Questions
-
The notebook needs data that is too large to hold in RAM. Concretely, how would you change your I/O and core-compute design to stream it, and how does that affect your tests and complexity analysis?
-
A reviewer reports that "Restart and run all" fails on their machine even though it works on yours. What are the most likely Colab-specific causes, and how would you make the notebook robust to them?
-
The exercise turns out to need a third-party library that is
not
pre-installed and whose install requires a runtime restart. How do you structure the notebook so a single "Restart and run all" still works end-to-end?
-
Suppose mid-exercise the requirements change (e.g. the output schema gains a field, or the input grows 100×). Which parts of your structure absorb that change cheaply, and which would you have to rework — and what does that say about your original boundaries?