Free Machine Learning Engineer Practice Assessment (2026)

Cohesyve · Practice for candidates

Going for a Machine Learning Engineer role? Find out how you'd actually score.

Run a Machine Learning Engineer simulation built the same way employers build theirs, and get a full report showing exactly where you lost marks — before it counts.

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Overview

Machine learning assessments have shifted away from deriving algorithms on a whiteboard. What employers now set is closer to the job: a dataset with leakage in it, a model that performs well offline and badly in production, or a service that has to return a prediction within a latency budget. The marking rewards evaluation discipline and engineering judgement more than model choice. This page covers what to expect and how it is scored.

Why employers assess this role

A model that looks excellent in a notebook and fails in production is the single most common outcome of a weak hire, and it is almost always caused by evaluation error rather than by modelling error. Employers assess practically because leakage, a badly chosen split, and a metric that ignores class imbalance are invisible in a conversation and obvious in submitted work.

What gets tested

Framing a business problem as a learning taskData exploration and leakage detectionFeature engineering and preprocessing disciplineValidation strategy and split designMetric selection appropriate to the problemModel training, tuning and baselinesServing, latency and reproducibilityMonitoring for drift and degradation

The format

Duration

90–180 minutes

Question types

End-to-end modelling task on a supplied dataset
Critique of an existing notebook or evaluation setup
Design a serving or retraining approach in writing
Debug a model whose live performance dropped

Levels

Entry · Mid · Senior

What you'll be asked to do

Frame the problem before modelling

Given a business objective, define the target, the unit of prediction and the metric. Candidates who start training immediately usually lose marks here.

•Turn "reduce churn" into a supervised target with a defined prediction window
•Choose a metric for a heavily imbalanced classification task and justify it
•Decide what a sensible baseline is before any model is trained

Build a defensible evaluation

The highest-signal section. Assessors plant leakage and temporal structure to see who designs the split around the real prediction setting.

•Design a split for data where users appear many times over two years
•Identify which supplied features would not exist at prediction time
•Explain why a randomly shuffled split overstates performance here

Train and improve a model

Marked on the sequence of decisions rather than the final number. A modest model with a clear improvement path beats a strong number nobody can account for.

•Establish a baseline and show what each subsequent change bought
•Handle categorical features with high cardinality without leaking the target
•Decide when to stop tuning and say what you would try next

Take it to production

Frequently written rather than built. Serving, reproducibility and monitoring are where engineering ability separates from modelling ability.

•Describe how features are computed identically at training and serving time
•Meet a stated latency budget for a model that is currently too slow
•Define what you would monitor to detect that the model has degraded

Cohesyve for candidates

Practise a Machine Learning Engineer assessment before the real one

Run the same AI job simulations companies use to evaluate applicants. You get a scored report showing where you're strong and where you're not, plus what to work on.

Sample tasks — and what strong looks like

Given a customer dataset, build a model that predicts churn within the next thirty days and report how well it performs.

Mid

What strong looks like: Defines the label and the observation window explicitly, splits by time so the model never sees the future, checks for features that are only populated after the outcome, and reports a metric appropriate to the imbalance alongside a baseline. Weak submissions shuffle the rows, report a high score, and never notice the leaked cancellation field.

Review a supplied notebook that reports strong results and say whether you would trust it.

Entry

What strong looks like: Finds the specific defect — normally fitting the preprocessing on the full dataset before splitting, or a feature derived from the target — explains the mechanism by which it inflates the score, and proposes the corrected procedure. Weak answers comment on code style and accept the number.

A model that performed well at launch has degraded over three months. Explain how you would investigate.

Senior

What strong looks like: Distinguishes input drift from label drift from an upstream pipeline change, checks whether the feature distributions at serving still match training, and proposes a retraining trigger rather than a one-off retrain. Weak answers retrain immediately and never establish what changed.

A model meets its accuracy target but takes far longer per prediction than the stated budget allows. Bring it within budget.

Mid

What strong looks like: Measures where the time actually goes, distinguishing feature computation from inference, then chooses deliberately between a smaller model, precomputed features or batching, and reports the accuracy given up for the latency gained. Weak answers change the model first without profiling.

How to prepare

#1
Practise designing the evaluation before touching a model, because split design and leakage detection carry more marks than anything you do afterwards.
#2
Train yourself to audit every feature by asking whether it would exist at prediction time, since planted leakage is the most common trap in these assessments.
#3
Always establish a trivial baseline first and quote your model against it, as an unanchored score reads as inexperience.
#4
Rehearse justifying a metric out loud for imbalanced and cost-asymmetric problems, because a default choice of accuracy is a reliable way to lose points.
#5
Get comfortable writing the production section — feature parity, reproducibility and monitoring — as it is where engineering candidates separate from analysis candidates.
#6
Sit a full timed mock, since these tasks run long and candidates routinely spend the budget on tuning and leave the reasoning unwritten.

Common mistakes

Shuffling a temporally ordered dataset and reporting a score that could never hold in production.
Fitting scalers, encoders or imputers on the full dataset before splitting.
Including a feature that is only populated once the outcome has already occurred.
Reporting accuracy on a heavily imbalanced problem without a baseline for comparison.
Tuning at length for a marginal gain while the evaluation itself remains unsound.
Delivering a notebook that cannot be re-run end to end because of hidden state and out-of-order cells.

How it's scored

Criterion	What strong looks like
Problem framing	The target, the prediction window and the metric are defined explicitly and tie back to the stated business objective.
Evaluation soundness	The split reflects the real prediction setting, preprocessing is fitted inside the training fold, and no feature encodes the outcome.
Baseline and iteration	A simple baseline exists and each subsequent change is attributed to a measurable effect rather than tried at random.
Production thinking	Features are computed the same way at training and serving, the work is reproducible, and monitoring is specified rather than assumed.
Communication	Limitations are stated honestly, the result is expressed in terms the business asked about, and the next step is named.

Frequently Asked Questions

Do I need a state-of-the-art model to score well?

No, and reaching for one often hurts. A well-evaluated gradient-boosted baseline with clearly documented decisions scores above a larger model whose performance nobody can account for.

How much of the marking is engineering rather than modelling?

More than most candidates expect for an engineering title. Reproducibility, feature parity between training and serving, and monitoring frequently carry as much weight as the model result itself.

Can I use standard libraries and pretrained models?

Almost always yes, since the assessment is meant to resemble the job. If AI assistance or a specific library is restricted, that is stated explicitly, so read the brief before you begin.

What fails candidates most often?

Leakage. A strong reported score with a split that could not exist in production is the most common submission, and it is treated as a serious error rather than an oversight.

Is it worth doing a practice run first?

It is, particularly for pacing. Cohesyve provides five free scored assessments each month with a full report, so you can find out whether your evaluation discipline holds up when someone else marks it.

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Cohesyve · Practice for candidates

Practise a Machine Learning Engineer assessment now — free.

Five scored assessments a month, a full report on every run, and a learning pathway built from what you got wrong. No card required.