KETERAMPILAN PEMROGRAMAN

Machine learning in Amazon Web Services test

See how candidates judge the practical work of building and running machine learning workloads on AWS.

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

Use this test free See the question formats

Jawaban singkat

The Machine Learning in Amazon Web Services test is an intermediate hiring assessment for machine learning engineers, data scientists who deploy their own models, MLOps engineers, and cloud architects planning ML platforms. Short scenarios measure practical judgment in data and permissions, SageMaker training and serving, live-model care, and managed ML services.

Hal yang diukur

Practical judgment in building, serving and running machine learning workloads on Amazon Web Services.

Data, compute and access setup for ML

Candidates judge how to prepare data, compute, and permissions for a machine learning workload on AWS.

Training, tuning and serving models

Scenarios cover decisions involved in training, tuning, and serving models with Amazon SageMaker.

Keeping live models healthy and affordable

Candidates consider how to keep deployed models accurate, fast, and affordable.

Prebuilt and visual ML services

Questions ask when a prebuilt or visual AI service is a better fit than a custom model.

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 who deploy their own models, MLOps engineers, and cloud architects who plan ML platforms.

Evidence of practical AWS ML judgment

Reading policies and code
Some questions show a short JSON policy or Python snippet to read and judge, so you see whether a candidate can tell what a permission or call will actually allow on AWS.
Amazon SageMaker delivery choices
Scenarios about training, tuning and serving in Amazon SageMaker show how a candidate weighs the options for getting a model into use.
Prebuilt service or custom model
Questions ask when a prebuilt AI service beats a custom model, a build-or-use call that also shapes what a live model costs to run.

What you get for each candidate

Example report (fictional data)Machine Learning in Amazon Web Services (AWS)

78%Percentile 62. Benchmark: HireValid candidates, n = 1.640

Data, compute and access setup for ML76%

Training, tuning and serving models79%

Keeping live models healthy and affordable86%

Prebuilt and visual ML services69%

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

Assessment integrity signals

Each candidate receives a different set of questions from large question pools, with question order and answer options shuffled. The browser receives 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 time left. Candidates cannot copy or select question text, and a blocked copy attempt is recorded. A personal invitation link expires, and a completed test cannot be taken again. HireValid records signals such as time away from the test tab and summarizes them 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.

Cara kami mencegah kecurangan dengan AI · Integrity signals

Use the score as evidence

Review the score alongside a work sample that reflects the role, such as asking a candidate to reason through a model-serving decision. Use a structured interview to explore their choices in data and permissions, Amazon SageMaker, and live-model care. The score is one signal for a person to review, not a hiring decision. For adjacent skill areas, see Computer Vision and Natural Language Processing (NLP).

Pertanyaan wawancara terstruktur

How would you prepare data and permissions for a model workload on AWS?
Listen for a clear explanation of setup decisions and how they relate to the planned workload.
Tell me about a model you served with Amazon SageMaker. What tradeoffs shaped your approach?
Look for reasoning about training, tuning, and serving, with choices tied to the situation.
How would you investigate a live model that became slower or less accurate?
Listen for a practical way to consider model health, performance, and affordability.

Understand scoring and benchmarks

Fairness and accommodations

Questions test working judgment in realistic scenarios rather than memory of prices, quotas, regional availability or recent product launches, which change often. Scenarios come from many industries, use names from many backgrounds, avoid money symbols and local knowledge, and keep the English plain so the test measures cloud ML skill rather than reading speed. Code is short, commented only where needed, and uses common library calls.

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.

Baca pendekatan kami terhadap keadilan

Test details

A timed set of short scenarios about running machine learning on Amazon Web Services: preparing data and permissions, training and serving models in Amazon SageMaker, keeping live models accurate, fast and affordable, and knowing when a prebuilt AI service beats a custom model. Some questions show a short Python snippet or a JSON policy to read and judge.

Questions are in English.
No aids are needed.
Each question is answered once, in order, with no going back.
4 practice questions, 4 minutes, before the timed test.

Amazon Web Services, Amazon SageMaker, Amazon S3, Amazon CloudWatch, AWS Lambda, AWS Identity and Access Management, AWS Glue, Amazon Bedrock, Amazon Comprehend, Amazon Rekognition are trademarks of their owners. HireValid is not affiliated with or endorsed by them.

Pertanyaan yang sering diajukan

Does the test depend on knowing current AWS prices or recent launches?+

No. Questions test working judgment in realistic scenarios rather than memory of prices, quotas, regional availability or recent product launches, which change often. Scenarios come from many industries, use names from many backgrounds and plain English, and code is short and uses common library calls.

Which roles and level fit the Machine Learning in Amazon Web Services test?+

It is an intermediate test for machine learning engineers, data scientists who deploy their own models, MLOps engineers, and cloud architects who plan ML platforms. Choose it when the role makes practical ML decisions on AWS.

What format and aids does the Machine Learning in Amazon Web Services test use?+

The test has 15 multiple-choice questions in English and takes 10 minutes. Some questions include a short Python snippet or JSON policy to read and judge. No aids are allowed, and candidates cannot go back to an earlier question.

What practice is available for the Machine Learning in Amazon Web Services test?+

Candidates can try a practice set of 4 questions in 4 minutes before the 15 timed questions. It lets them get used to the single-answer and select-all formats, and to reading a scenario closely, before the scored questions start.