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# Generative AI Structured Output and JSON Schema Prompting Skills Test

Evaluate the ability to prompt generative AI systems for reliable, machine-readable responses. Focus on schema design, validation, constraints, and recovery from malformed output.

[Start the test](#test-start)

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## Choose the level that matches your current experience.

*  01 Basic Available
*  02 Intermediate Available
*  03 Advanced Available

Instant result Review your score after submission 20–30 Questions per assessment 15–45 min Estimated completion time 3 levels Choose your difficulty Generative AI & Prompting [View category](https://jobicy.com/test-category/generative-ai-prompting.md) Start assessment

## Choose your level and begin.

Answer without outside help so the result reflects your current knowledge. You will see your score after completing the selected assessment.

Structured AI output enables applications to turn model responses into dependable records, workflows, and interface elements. This test examines how to define response contracts, express field requirements, constrain values, and handle outputs that fail validation without relying on fragile text parsing.

Basic   Intermediate   Advanced

This is a demo version of the test. You may attempt up to 3 questions.

To take the full test and save your results, please [log in](https://jobicy.com/dashboard-page.md) or [create an account](https://jobicy.com/dashboard-page#tab2.md).

This is a demo version of the test. You may attempt up to 3 questions.

To take the full test and save your results, please [log in](https://jobicy.com/dashboard-page.md) or [create an account](https://jobicy.com/dashboard-page#tab2.md).

This is a demo version of the test. You may attempt up to 3 questions.

To take the full test and save your results, please [log in](https://jobicy.com/dashboard-page.md) or [create an account](https://jobicy.com/dashboard-page#tab2.md).

Test details

## Know what to expect.

Review the instructions, covered skills, example question themes, and intended audience before beginning.

01

### Instructions and covered skills

Read each prompt, schema, and scenario carefully before selecting a response. Stay focused on the required output contract rather than the conversational quality of the model response. Turn off notifications and avoid switching between unrelated tasks while completing the test. Choose the option that best preserves valid, usable structured data. Pay attention to field types, required properties, allowed values, and nested relationships. Do not assume that valid-looking prose is valid machine-readable output. Review your choice before moving on.

### Key Areas

This test covers the design and use of structured output contracts for generative AI applications. Candidates interpret JSON Schema concepts such as object, array, string, number, boolean, null, required, properties, items, enum, minimum, maximum, pattern, and additionalProperties. They assess how these constraints shape a model response and how they support consistent downstream handling.

The test also addresses prompt construction for machine-readable responses. Effective prompts identify the requested data, define missing-data behavior, state whether explanations belong outside the response, and align instructions with a formal schema. Candidates should recognize the value of explicit field names, bounded categories, stable date formats, and clear rules for nested objects and arrays.

Validation and recovery are central themes. Candidates evaluate responses for parse failures, invalid types, omitted required fields, unsupported enum values, unwanted keys, and schema violations inside nested records. They consider suitable repair workflows that retain the original task context while asking the model to return a corrected response that conforms to the contract.

### Recommended Preparation

Review JSON syntax and practice distinguishing objects from arrays, strings from numbers, and absent values from explicit null values. Read JSON Schema documentation for common validation keywords and create small schemas for realistic tasks such as invoice extraction, support-ticket classification, meeting summaries, and product catalogs.

Practice writing prompts that pair a clear task description with a response contract. Test how a model behaves when source material lacks a requested value, contains conflicting values, or includes information that does not fit the schema. Examine validation errors and revise either the schema or the instructions according to the actual application need. Familiarity with API responses, data serialization, and automated validation tools is helpful for applying these practices in production workflows.

02

### Examples of questions

1. What purpose does a required property serve in a JSON Schema?
2. Which schema keyword restricts a string to a fixed list of allowed values?
3. Why should a prompt specify whether additional object properties are allowed?
4. What is the appropriate JSON type for an ordered list of extracted entities?
5. How can a schema require each item in an array to include a source field?
6. What response format should a model return when an application needs direct JSON parsing?
7. Which validation outcome should trigger a repair request to the model?
8. How does an enum reduce variation in a classification field?
9. When should a nullable field be used in a response contract?
10. What information should a schema include for a nested address object? 03

### Who this test is best for

AI product builders, automation specialists, software developers, data analysts, and operations professionals who integrate generative AI responses into systems and workflows.

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