Design an algorithmic E-E-A-T scoring system

WHAT THIS TESTS: This question tests whether you can translate a qualitative content guideline into an algorithmic feature system. Interviewers want to see that you understand ranking as a multi-signal inference problem, not just an NLP exercise. They are looking for feature engineering discipline, data architecture choices, and awareness of ground truth limitations.

A GOOD ANSWER COVERS: First, dimensional decomposition. Do not build one E-E-A-T score. Build separate models or feature groups for Experience, Expertise, Authoritativeness, and Trustworthiness because each has different evidentiary requirements. Second, structured data ingestion. Collect Schema.org author and reviewedBy markup, medical license numbers, publication dates, organization BBB ratings, HTTPS usage, and contact page presence. Third, unstructured signals. Use named entity recognition to link authors to knowledge graph entries, extract first-hand experience phrases like I tested or in my clinic, analyze citation graphs from academic or news domains, and measure semantic depth against known high-quality corpora. Fourth, cross-domain reputation. Scrape or ingest independent reviews, Wikipedia mentions, and journalist citations of the author or publisher. Fifth, ground truth calibration. Explicitly state that algorithmic scores must be trained against human Search Quality Rater labels rather than user clicks, because clicks reward sensationalism while raters measure trust.

COMMON WRONG ANSWERS: Proposing a single opaque E-E-A-T score without explaining how dimensions differ. Using only on-page text and ignoring off-page reputation. Treating click-through rate, dwell time, or bounce rate as proxies for trustworthiness. Suggesting that LLM inference alone can score expertise without external verification. Ignoring adversarial gaming, such as fake author profiles or purchased credentials.

LIKELY FOLLOW-UPS: How do you handle entities with no knowledge graph entry, such as niche hobbyists? How do you prevent circularity where a site ranks well simply because it already ranks well? What latency constraints would you impose if these features run at query time versus index time? How do you update scores when an author later publishes misinformation?

ONE CONCRETE EXAMPLE: For a medical article, structured signals would include the authors medical license ID, hospital affiliation in Schema.org markup, and a last-reviewed date. Unstructured signals would include whether the article cites peer-reviewed journals, whether the authors name appears on other high-trust health domains, and whether the text uses hedged clinical language rather than absolute cures. These features feed into a classifier trained on rater-labeled YMYL data, outputting separate expertise and trustworthiness logits that the ranking mixer consumes independently.

Source: Google Search Central documentation