PROGRAMMEERVAARDIGHEDEN

Neural networks test for hiring

See how candidates reason about building, training, and debugging neural networks.

10 minuten
15 vragen
Intermediate
Multiple choice
Questions in Engels
Phone and desktop

Use this test free See the question formats

Het korte antwoord

The neural networks test is an intermediate hiring assessment for machine learning engineers, deep learning and AI specialists, data scientists who build models, and related roles. It measures practical judgment in network design, training and tuning, reading training logs, spotting bugs in model code, and applying techniques such as attention and parameter-efficient fine-tuning.

Wat het meet

Designing, training, debugging and applying neural networks, including attention-based and other current techniques.

Network architecture and design

Questions cover how neural networks are built and how to reason about their architecture and design.

Training and optimization

Candidates consider how networks are trained and tuned, including how to read training logs.

Diagnosing and fixing models

Questions ask candidates to spot bugs in model code and reason through diagnosing and fixing models.

Modern deep learning techniques

Candidates consider how current techniques such as attention and parameter-efficient fine-tuning work.

Question formats

Multiple choice

The candidate: Reads a short situation, table or statement and picks the right answer, or every right answer.

You see: Whether the candidate knows the material well enough to apply it, not just recognize it.

15 questions in 10 minutes, served one at a time.

Roles this test fits

Machine learning engineers, deep learning and AI specialists, data scientists who build models, computer vision and NLP engineers, applied research scientists and AI architects.

What this test can show

Reasoning about model design
Short Python questions show how candidates approach network architecture without tying answers to one deep learning library.
Reading training evidence
Questions about training logs show how candidates interpret model behavior and think about tuning.
Finding code issues
Model-code questions let you review how candidates diagnose a bug, rather than relying only on a description of their experience.
Applying current techniques
Questions on attention and parameter-efficient fine-tuning show how candidates reason about modern deep learning approaches.

What you get for each candidate

Example report (fictional data)Neural Networks

73%Percentile 51. Benchmark: HireValid candidates, n = 1.873

Network architecture and design56%

Training and optimization87%

Diagnosing and fixing models82%

Modern deep learning techniques65%

Integrity ScoreHoge betrouwbaarheid

Stayed on the test tab throughout
No pasted answers
Answered at a steady pace

Scores, profiles and integrity signals are evidence for you to review alongside an interview, never an automatic decision.

Use this test free

Test integrity details

Each candidate receives a different question set from large pools, with question order and answer options shuffled. The browser shows one question at a time, and answer keys stay on HireValid’s servers. Timing runs on the server; reloading restores the same question with only the time left, and candidates cannot go back. Copying or selecting question text is blocked, and a blocked copy attempt is recorded in the timeline. Personal invitation links expire, and a finished test cannot be taken again. HireValid summarizes signals such as time away from the test tab and pastes into answers in an Integrity Score with an evidence timeline for employer review. It does not decide on its own. With candidate consent, employers can add webcam photos at random moments. Read how we build our tests for more about the test design.

Hoe we fraude met AI tegengaan · Integrity signals

Use the score as evidence

Review the score alongside a relevant work sample and a structured interview. For example, ask candidates to explain a model-design choice or debug a short code example, then compare their reasoning with the test evidence. The score should inform a person’s review, not decide by itself. You can also compare the assessment with the Deep Learning test or Machine Learning test when choosing related skills to assess.

Vragen voor gestructureerde sollicitatiegesprekken

How would you investigate a training log that suggests a model is not learning as expected?
Listen for a clear approach to reading the log, forming possible explanations, and deciding what to inspect next.
What would you consider when choosing an architecture for a model task?
Listen for relevant design tradeoffs and a reasoned connection between the task and the proposed network.
How would you track down a bug in a short piece of model code?
Listen for a systematic way to inspect the code and test likely causes rather than guessing.
Describe a model problem where you would consider parameter-efficient fine-tuning instead of full fine-tuning. What would you check before deciding?
Listen for a link between the constraints of the problem and the choice, and how the candidate would confirm that it worked.

Understand scoring and benchmarks

Fairness and accommodations

Questions are written to be framework-neutral: code is short plain Python, every layer, shape convention, default and setting a question relies on is stated in the question, and no answer depends on one library's function names or defaults. Questions ask how techniques work and when to use them, never who published them or when. Wording is plain technical English, and the arithmetic is small enough for scratch paper, which every candidate may use.

You can give any candidate extra time as an accommodation. Results are evidence for a person to review, never an automatic decision.

Provisional: reliability is reported once 200 candidates have completed it.

Lees hoe we met eerlijkheid omgaan

Test details

A timed check of practical deep learning judgment. Candidates work through short questions on how networks are built, how they are trained and tuned, how to read training logs and spot bugs in model code, and how current techniques such as attention and parameter-efficient fine-tuning work. Code is short, written in plain Python, and does not depend on any one deep learning library.

Questions are in English.
Aids allowed: Scratch paper.
Each question is answered once, in order, with no going back.
4 practice questions, 3 minutes, before the timed test.

Python is a trademark of its owner. HireValid is not affiliated with or endorsed by it.

Veelgestelde vragen

How does the neural networks test keep its questions fair?+

Questions use short plain Python and do not depend on one deep learning library. Each question states the layers, shape conventions, defaults, and settings it relies on. Questions focus on how techniques work and when to use them, not who published them or when. The arithmetic is small enough for scratch paper.

Can candidates use scratch paper on the neural networks test?+

Yes. Scratch paper is allowed for every candidate. It can be useful when working through the small arithmetic in questions about network design, training, or model behavior.

What practice set comes with the neural networks test?+

Candidates can try a practice set with 4 questions and a 3-minute limit. It offers a chance to see the question style before the 10-minute assessment, which contains 15 multiple-choice questions.

Which candidates should take the intermediate neural networks test?+

It is intended for machine learning engineers, deep learning and AI specialists, data scientists who build models, computer vision and NLP engineers, applied research scientists, and AI architects, at an intermediate level.