Machine learning test for hiring
Check how candidates choose, tune and interpret classical machine learning methods.
- 10 分钟
- 15 道题
- Intermediate
- Multiple choice
- Questions in 英语
- Phone and desktop
简短回答
The Machine Learning test is an intermediate, 10-minute check of working knowledge across classical methods. Multiple-choice questions use short scenarios, small results tables and brief Python snippets to assess regression, classification, trees and unsupervised methods. It is designed for hiring machine learning engineers, data scientists, data analysts, data engineers and AI engineers.
测量内容
Working knowledge of classical machine learning methods: when to use each one, how to tune it and how to read its results.
Regression and regularization
Questions check how candidates fit and regularize regression models and interpret their results.
Classification and support vector machines
Candidates choose and assess classifiers, including support vector machines, in short scenarios.
Decision trees and ensembles
Questions cover the use and interpretation of decision trees and ensembles.
Clustering, anomalies and dimensionality reduction
Candidates consider clustering, anomaly detection and dimensionality reduction to find structure in unlabeled data.
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, data scientists, data analysts, data engineers and AI engineers, and technical leads or product people whose work calls for intermediate machine learning knowledge.
A focused view of practical knowledge
See choices in context
Short scenarios show how candidates select a method for a problem, beyond what a resume lists.
Check result reading
Small results tables and code snippets prompt candidates to interpret an outcome or spot a mistake.
Cover several method families
One 10-minute check spans regression, classifiers, trees and ensembles, plus unsupervised methods.
Focus follow-up
Use the overall and sub-skill scores to focus a work sample and interview on the candidate's demonstrated knowledge.
What you get for each candidate
- 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.
Test conditions and review signals
Each candidate receives a different set of questions from large pools, in a different order, with shuffled answer options. 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 copy or select question text, and blocked copy attempts are recorded. Personal invitation links expire, and a finished test cannot be taken again. HireValid records signals such as time away from the test tab, pastes into answers and answers faster than a question can be read. An Integrity Score and evidence timeline help the employer review signals; neither makes the decision. With candidate consent, an employer can add webcam photos at random moments. See how we build our tests for more about the testing approach.
我们如何防范 AI 作弊 · Integrity signals
Use the score as evidence
Review the score as one signal, not as a hiring decision. Pair it with a job-relevant work sample that asks the candidate to choose or interpret a method, then use a structured interview to explore their reasoning. The Artificial Intelligence test and Software Engineer test are related options when you need to consider adjacent skills.
结构化面试问题
Describe how you would choose and assess a classifier for a work problem. What result would change your next step?
Listen for a reasoned method choice and attention to interpreting results, rather than a definition.
Talk me through how you would review a regression result before using the model.
Listen for relevant checks around fitting, regularization and what the result means for the problem.
How would you investigate a clustering or anomaly result that did not fit the scenario?
Listen for a clear approach to examining the result and considering whether the method fits the data.
Understand scoring and benchmarks
Fairness and accommodations
Questions test concepts rather than the defaults or function names of any one library, so candidates who learned on different tools start even. The code snippets are short, written in plain Python, and need no library knowledge. Scenarios use neutral settings such as sensors, deliveries, website visits and equipment, with no money symbols, idioms or local knowledge, and names from many backgrounds. The numbers in tables are chosen so that scratch paper is enough and no calculator is needed.
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.
Test details
A ten-minute check of working knowledge across classical machine learning: fitting and regularizing regression models, building classifiers including support vector machines, using decision trees and ensembles, and finding structure in unlabeled data through clustering, anomaly detection and dimensionality reduction. Questions show short scenarios, small results tables and brief code snippets, and ask the candidate to pick the right method, read a result or spot a mistake.
- Questions are in English.
- Aids allowed: Scratch paper.
- Each question is answered once, in order, with no going back.
- 4 practice questions, 4 minutes, before the timed test.
Python is a trademark of its owner. HireValid is not affiliated with or endorsed by it.
常见问题
What does the Machine Learning test measure?+
It measures working knowledge of classical machine learning methods: when to use them, how to tune them and how to read their results. Topics include regularized regression, support vector machines, trees and ensembles, clustering, anomaly detection and dimensionality reduction.
What question formats appear in the Machine Learning test?+
The 15 multiple-choice questions use short scenarios, small results tables and brief Python code snippets. Candidates choose a method, read a result or spot a mistake; the snippets require no library knowledge.
Who should use the Machine Learning test in hiring?+
It is for hiring machine learning engineers, data scientists, data analysts, data engineers and AI engineers. It can also suit technical leads or product people whose work calls for intermediate machine learning knowledge.
Can candidates go back in the Machine Learning test?+
No. The 10-minute test does not allow candidates to return to an earlier question. Candidates may use scratch paper, and a four-question practice set takes four minutes.
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