Original practice scenario inspired by Gilead Sciences, Inc.'s public business context; this is not a reported interview question or a description of its internal systems. A fictional research operations database stores participants, visits, measurements, and protocol amendments. Explain a query for one prespecified endpoint per participant at a defined visit, including missing measurements and repeated attempts. This is data-engineering practice, not clinical interpretation.
Read model answer +
Define the eligible participant population and endpoint-selection rule before joining measurements. Repeated attempts and protocol versions can otherwise produce several conflicting rows for one participant.
Work through it
Select the applicable protocol version and target visit window using recorded eligibility rules. Preserve eligible participants who have no qualifying measurement.
Rank qualifying measurements by the prespecified rule, such as accepted status then acquisition order, with a complete tie-break. Keep one endpoint row per participant.
Report observed and missing counts separately and retain exclusion reasons. Test multiple attempts, a boundary visit time, a withdrawn measurement, and a participant with no visit.
Tradeoffs to discuss
Different defensible selection rules answer different questions. The software should implement the approved analysis definition rather than choose a convenient favorable value.
Common pitfalls
An inner join hides missing endpoints.
MAX(measurement) selects a value by magnitude, which is not a valid substitute for a documented acquisition-selection rule.
Original practice scenario inspired by Gilead Sciences, Inc.'s public business context; this is not a reported interview question or a description of its internal systems. A synthetic internal research assistant retrieves passages from a permissioned document collection and drafts answers. Describe an evaluation that separates retrieval, evidence use, and unsupported claims without assuming the assistant can make scientific decisions.
Read model answer +
Evaluate the assistant as several linked components. A fluent answer can still cite an irrelevant passage, omit a critical qualification, or reveal material the requester is not authorized to access.
Work through it
Create expert-reviewed questions with evidence references, including unanswerable and conflicting-evidence cases. Keep evaluation documents and questions separated from prompt development where practical.
Measure retrieval recall at the context cutoff, then assess whether each material answer claim is supported by the supplied passages. Record abstentions and citation correctness separately.
Test permission filtering before retrieval and after document changes. Review failures by document version, question type, and missing-context cause rather than combining everything into one score.
Tradeoffs to discuss
Expert claim review is expensive but necessary for nuanced evidence judgments. Automated checks can triage failures without replacing that review.
Common pitfalls
A citation marker does not establish support.
High retrieval recall cannot compensate for unauthorized documents or invented conclusions in the generated answer.
