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.