Cover the feedback loop: implicit signals, explicit ratings, click-through data
0 / 5 completed
1 / 5
The interviewer asks: "How does an inverted index work — and what are its fundamental limitations?" Choose the most complete and accurate technical explanation.
Option C is strongest: it defines the inverted index precisely (term → sorted postings list with metadata), explains query execution (intersection/union), and names five specific limitations with explanations — not just the generic "no semantic understanding." It also names how each limitation is addressed (analyzers for vocabulary mismatch, vector retrieval for semantic blindness, Lucene's segment architecture for update scaling). Option D is technically correct but less complete — it doesn't explain how query execution works (intersection/union) and doesn't address the phrase query cost or how limitations are mitigated. Option A and B are too shallow. Data structure explanation format: definition → operation mechanism → list of named limitations → state how each is mitigated in practice.
2 / 5
The interviewer asks: "Our search results feel irrelevant. How would you diagnose and fix this?" Choose the most structured diagnostic approach.
Option B is strongest: it establishes a baseline metric first (NDCG/MRR), then classifies failure modes before prescribing fixes (critical — different failure types need different solutions), and maps each failure type to a specific fix. It also distinguishes retrieval vs. ranking failures (a common interview distinction). Option C is not wrong — semantic search does address vocabulary mismatch — but it's a technology prescription without diagnosis. If the real problem is a misconfigured analyzer or a stale index, adding vector search won't fix it. Option D focuses only on BM25 tuning, which is one signal but doesn't address other failure modes. Relevance bug diagnosis: quantify → classify failure modes → root cause each type → fix by type → offline eval → online A/B.
3 / 5
The interviewer asks: "How would you design the search for an e-commerce site with 100 million SKUs — covering relevance, performance, and freshness?" Which answer best covers the key system design dimensions?
Option A is strongest: it addresses all five system design dimensions (indexing, freshness, relevance, query understanding, performance) with specific numbers and technology choices, and covers edge cases (zero results fallback). It also distinguishes freshness SLAs by update type (price/inventory vs. new product) — a nuance examiners look for. Option D describes a valid two-stage retrieval architecture but only covers the relevance dimension — doesn't address indexing architecture, freshness, or performance. Option B mentions the right components but without specifics (e.g., "auto-scaling" and "real-time indexing" are outcomes, not designs). Search system design checklist: indexing architecture → freshness SLA → relevance signals → query understanding → performance → edge cases (zero results, typos).
4 / 5
The interviewer asks: "How would you add semantic/vector search to an existing keyword search system?" Choose the answer that demonstrates practical integration experience.
Option D is strongest: it specifies a fusion approach (rather than replacement) with a concrete merging algorithm (RRF — Reciprocal Rank Fusion — a commonly tested term), gives a real implementation path using Elasticsearch's native kNN (avoiding a separate vector database), addresses the embedding model quality problem for domain-specific content, includes freshness requirements for the vector index, and specifies an evaluation methodology. Option A is wrong for recommending full replacement — keyword search is still superior for exact/SKU queries. Option C's query routing classifier is a valid approach but complex to build and maintain. Option B describes the right components but doesn't specify how to fuse results (which is the hard part). Hybrid search integration: retrieval fusion method (RRF) → infrastructure choice (built-in kNN vs. external vector DB) → domain-specific embedding fine-tuning → freshness pipeline → evaluation.
5 / 5
The interviewer asks: "How do you measure and continuously improve search quality?" Which answer demonstrates a complete quality measurement framework?
Option B is strongest: it defines three measurement levels (offline labelled eval, online metrics, and behavioural signal loop), names specific metrics with definitions (including CLTR with position-bias correction), identifies the implicit signal biases (position bias, popularity bias) and how to address them (counterfactual LTR), and describes the complete improvement loop. Option D describes a solid offline evaluation approach but misses online metrics and the implicit feedback loop. Option C is a reasonable monitoring setup but lacks offline evaluation and doesn't close the feedback loop. Search quality framework: offline labelled eval (NDCG/MRR) → online metrics (CTR, abandonment, reformulation) → implicit signal collection and debiasing → improvement loop connecting each layer.