Who should take the Artificial Intelligence Test?

This test fits candidates who need working knowledge of AI but are not being hired as specialist researchers. It is useful for product, project, consulting, analyst, and client-facing roles that interact with AI concepts, tools, or vendors.

Does this test require coding skills?

No, the test focuses on conceptual understanding rather than programming. It checks whether candidates can reason about AI terms, model types, and practical risks in a work setting.

What does a strong score tell me?

A strong score suggests the candidate can explain AI clearly, distinguish common approaches, and identify limitations that affect business decisions. It is a good sign for roles that need sound judgment around AI without requiring deep technical research expertise.

Can this test replace an interview?

No, it should be used as an early screening tool, not as the only hiring step. It helps you identify who has a reliable baseline of AI knowledge before moving candidates into interviews or role-specific exercises.

Why are questions randomised for each candidate?

Randomisation helps reduce overexposure to the same questions and supports a fairer screening process across applicants. Each candidate still receives questions aligned to the same skills and difficulty level.

IT & security

Artificial Intelligence Test

The Artificial Intelligence Test helps hiring teams screen candidates who need practical AI understanding, not research depth. It is suited to intermediate roles where people must explain core concepts, distinguish model types, and spot limits and risks in business use.

10 mins20 questionsIntermediateMultiple choice

Summary of the Artificial Intelligence Test

Core conceptsModel typesLimitations & risk

What it measures

3 dimensions, scored separately

Core concepts

This measures whether a candidate understands the basic ideas behind AI and can use key terms correctly. It matters at work because people need to explain AI clearly, challenge loose claims, and make sound decisions with technical and non-technical teams.

Model types

This measures whether a candidate can distinguish between major model categories and understand what each is generally used for. It matters because choosing the wrong model approach can lead to poor requirements, bad vendor evaluation, or unrealistic project plans.

Limitations & risk

This measures whether a candidate recognizes where AI can fail, what it cannot do, and what risks need attention. It matters because teams need people who can identify bias, overclaiming, misuse, and operational risk before they become business problems.

The complete guide

Everything about the Artificial Intelligence Test

01What is the Artificial Intelligence Test?

This test measures whether candidates understand artificial intelligence at a practical working level. It covers core concepts, common model types, and the limitations and risks that affect real decisions. Questions check whether a candidate can separate AI from machine learning and deep learning, correct common misunderstandings, and reason about where AI fits and where it does not. The test is built for hiring teams that need signal on applied knowledge, especially for roles that work with technical teams, vendors, or AI-enabled products. On Springhire, it gives a structured way to compare candidates on the basics that matter before interviews.

02How does the Artificial Intelligence Test work?

Candidates answer 20 multiple choice questions in 10 minutes. Questions are drawn from a 60-question bank, so each candidate sees a randomised set. The test is set at an intermediate level, is auto-scored, and is designed to check practical understanding rather than advanced theory or coding depth.

03Why is the Artificial Intelligence Test important to employers?

Many candidates can speak confidently about AI without understanding the basics. That creates hiring risk when a role needs someone to explain model choices, challenge vague claims, or spot limits before a team makes a poor product, vendor, or compliance decision. This test helps reduce that risk early in screening.

Reading the report

How to interpret Artificial Intelligence Test results

Every candidate report scores each dimension separately. Here is what high and lower scores typically look like at work, and how to use them in your hiring decision.

Core concepts

High scorers: A high score indicates the candidate can accurately explain foundational AI ideas and correct common misconceptions.

Lower scorers: A low score suggests the candidate may confuse basic terms or rely on vague, inaccurate descriptions of AI.

Model types

High scorers: A high score shows the candidate can tell model types apart and connect them to appropriate uses at a practical level.

Lower scorers: A low score indicates the candidate may not understand the differences between common model categories or when they fit.

Limitations & risk

High scorers: A high score means the candidate is likely to recognize important constraints, risks, and unrealistic expectations around AI use.

Lower scorers: A low score suggests the candidate may overlook key limitations or fail to question risky or exaggerated AI claims.

What job roles can you hire with the Artificial Intelligence Test?

Product Manager
Business Analyst
Solutions Consultant
Technical Account Manager
IT Project Manager

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