AI & Technical question

How would you design an algorithm to source data from the USDA and display on Google nutrition?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical system design for data ingestion, normalization, and quality assurance from a government data source.

Clarify the goal: reliably pull nutrition data from the USDA database and display accurate, up-to-date information on Google nutrition panels.
Design the ingestion pipeline: scheduled polling or webhook-based sync from the USDA's public API, since it's the authoritative source.
Design a normalization layer: map USDA's data schema and units to Google's internal nutrition display format, since field names and units likely differ.
Add data quality checks: flag missing fields, out-of-range values, or duplicate entries before publishing to the live product.
Design a fallback and versioning strategy: keep the last known good data if a sync fails, rather than showing broken or blank entries.
Define success metrics: data freshness (sync lag) and error/discrepancy rate against the USDA source.

Names a specific pipeline architecture, scheduled sync plus normalization plus quality checks, rather than a vague 'connect the API' answer.
Addresses unit and schema mismatches explicitly, a real, common integration challenge with government data sources.
Gives an illustrative guardrail, e.g. rejecting any sync with more than 1% missing required fields, marked as an assumption.
Proposes a fallback to last-known-good data, showing awareness that partial pipeline failures shouldn't break the live product.

Describing only the data pull without addressing normalization or quality validation.
Ignoring what happens to the live product when a sync fails or returns bad data.

How would you handle a discrepancy between USDA data and existing displayed data?
How often would you sync, and why that frequency?