KETERAMPILAN PEMROGRAMAN

Computer vision test for hiring

See how experienced practitioners reason through practical image and video problems.

10 menit
15 pertanyaan
Advanced
Multiple choice
Questions in Inggris
Phone and desktop

Use this test free See the question formats

Jawaban singkat

The Computer Vision test is an advanced hiring assessment for experienced practitioners who work with images or video. It measures practical judgment across image operations, object detection, pixel-level labeling, camera geometry, and deployment. Candidates trace short Python snippets using NumPy, OpenCV, and PyTorch, solve small calculations by hand, and choose methods for concrete problems.

Hal yang diukur

Practical computer vision judgment: image operations, detection, pixel-level labeling, camera geometry and deployment.

Filtering, transforms and preprocessing

Candidates work with image filtering, transformations, and preprocessing operations.

Recognition and localization

This area covers detecting objects and identifying where they appear in an image.

Pixel labeling and region masks

Candidates consider assigning labels to pixels and forming masks for image regions.

Camera geometry, motion and deployment

This area covers camera geometry, motion, and practical choices for running models in production.

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 and deep learning engineers who build models on images or video, computer vision and perception engineers in robotics, inspection, mapping or medical imaging, and data scientists whose work centers on visual data.

Evidence from practical vision tasks

See reasoning in code context
Short Python snippets using NumPy, OpenCV, and PyTorch show how candidates interpret image operations, rather than relying only on a list of tools on a resume.
Check visual prediction judgment
Questions cover both object detection and pixel-level labeling, giving you evidence about distinct ways candidates approach image predictions.
Keep the task representation accessible
Images are described through arrays, tables, shapes, and short code, not photographs; answers do not depend on distinguishing colors by sight.

What you get for each candidate

Example report (fictional data)Computer Vision

78%Percentile 62. Benchmark: HireValid candidates, n = 2.265

Filtering, transforms and preprocessing79%

Recognition and localization84%

Pixel labeling and region masks57%

Camera geometry, motion and deployment90%

Integrity ScoreTingkat keyakinan tinggi

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

How the assessment is delivered

Each candidate receives a different set of questions from large pools. Questions appear in a different order on each attempt, and answer options are shuffled. The browser shows one question at a time, and answer keys stay on HireValid’s servers. The 10-minute timer runs on the server; reloading restores the same question with only the remaining time. Candidates cannot return to earlier questions. Question text cannot be copied or selected, and a blocked copy attempt is recorded in the timeline. HireValid records review signals, such as time away from the test tab, and summarizes them in an Integrity Score and evidence timeline for the employer. These signals do not decide on their own. With candidate consent, webcam photos can be added at random moments.

Cara kami mencegah kecurangan dengan AI · Integrity signals

Use the result as evidence

Review the score alongside a work sample and a structured interview. Ask candidates to explain choices relevant to your image or video work; do not use the score alone or as a pass mark. No norms exist yet, and reliability is reported once 200 candidates have completed the test. You can also compare it with the Machine Learning test or the Artificial Intelligence test if the role calls for broader model knowledge.

Pertanyaan wawancara terstruktur

How would you decide whether to apply a filter or transformation before running a model on a new image stream?
Listen for a reasoned choice tied to the image operations and preprocessing involved, rather than a recited definition.
How would you investigate whether an object detection approach is locating objects well in a particular visual task?
Listen for practical reasoning about detection and localization and how the approach relates to the task.
What would you consider when choosing how to label image regions and run a vision model in production?
Listen for connections between pixel labels or masks, camera geometry, and deployment considerations.

Understand scoring and benchmarks

Fairness and accommodations

Questions describe images through arrays, tables, shapes and short code rather than photographs, so no question depends on recognizing scenes, people or places from any culture, and no answer relies on telling colors apart by sight. Code is kept to short, plain Python with the libraries named, the arithmetic is small enough for scratch paper, and the wording is direct technical English without idioms.

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

Provisional: no norms exist yet, and reliability is reported once 200 candidates have completed it.

Baca pendekatan kami terhadap keadilan

Test details

A ten-minute check of working knowledge in computer vision for experienced practitioners. Candidates trace short Python snippets that use NumPy, OpenCV and PyTorch, work small calculations by hand, and choose the right method for concrete problems in filtering, detecting objects, labeling pixels, camera geometry and running models in production.

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, NumPy, OpenCV, PyTorch are trademarks of their owners. HireValid is not affiliated with or endorsed by them.

Pertanyaan yang sering diajukan

Which roles fit the Computer Vision test?+

It is designed for experienced machine learning and deep learning engineers working with images or video, computer vision and perception engineers, and data scientists whose work centers on visual data. It is an advanced test, not aimed at junior roles. See the related [Deep Learning test](/tests/role-specific-skills/deep-learning).

How does the Computer Vision test handle image-based fairness?+

Questions describe images through arrays, tables, shapes, and short code rather than photographs. They do not depend on recognizing scenes, people, or places from a culture, or on telling colors apart by sight.

Can candidates use scratch paper on the Computer Vision test?+

Yes. Scratch paper is allowed, and the arithmetic is small enough for it. Questions use short, plain Python with the named libraries and direct technical English without idioms.

What does the Computer Vision practice set include?+

Candidates can try a practice set with 4 questions in 3 minutes. It offers a chance to encounter the test’s question format before the 15-question assessment, which uses multiple-choice questions.