- A
Use a tsvector column with a GIN index on that column
PostgreSQL full-text search with tsvector/GIN is purpose-built for fast ranked search.
- B
Use a separate Elasticsearch instance
Why wrong: External service; not a Cloud SQL schema design.
- C
Use a LIKE '%term%' query with a B-tree index
Why wrong: LIKE with leading wildcard cannot use indexes efficiently; no ranking.
- D
Use materialized view with trigram indexes
Why wrong: Trigram indexes support similarity search but not full-text ranking.
Question 776 of 1,000
Full-Text Search in Cloud SQL PostgreSQL: tsvector and GIN Index
This PCDE practice question tests your understanding of design and implement database schemas. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are designing a schema for a Cloud SQL for PostgreSQL database that supports full-text search across millions of product descriptions. The application requires fast search results ranked by relevance. Which schema design is most appropriate?
Quick Answer
The answer is to use a tsvector column with a GIN index. This design is correct because PostgreSQL’s built-in full-text search converts text into a tsvector data type, which stores lexemes for efficient linguistic processing, while the GIN (Generalized Inverted Index) accelerates lookups and supports ranking via the ts_rank function. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of native PostgreSQL capabilities versus external services or inefficient pattern matching; a common trap is choosing a trigram index (pg_trgm) for similarity search, which cannot produce relevance-ranked results for full-text queries. Remember that LIKE with wildcards forces a sequential scan and ignores ranking, while external search engines violate the “schema design within Cloud SQL” constraint. Memory tip: think “tsvector for tokens, GIN for gains”—the combination gives you both speed and relevance scoring directly in the database.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a tsvector column with a GIN index on that column
Option A is correct because PostgreSQL's tsvector data type, combined with a GIN index, is specifically designed for full-text search. It preprocesses text into lexemes, supports stemming and ranking, and the GIN index enables fast lookups for millions of rows, meeting the requirement for relevance-ranked results.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a tsvector column with a GIN index on that column
Why this is correct
PostgreSQL full-text search with tsvector/GIN is purpose-built for fast ranked search.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a separate Elasticsearch instance
Why it's wrong here
External service; not a Cloud SQL schema design.
- ✗
Use a LIKE '%term%' query with a B-tree index
Why it's wrong here
LIKE with leading wildcard cannot use indexes efficiently; no ranking.
- ✗
Use materialized view with trigram indexes
Why it's wrong here
Trigram indexes support similarity search but not full-text ranking.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that LIKE queries with B-tree indexes are sufficient for full-text search, but the trap here is that LIKE '%term%' cannot leverage a B-tree index and forces a sequential scan, while tsvector with GIN is purpose-built for this workload.
Trap categories for this question
Similar concept trap
Trigram indexes support similarity search but not full-text ranking.
Detailed technical explanation
How to think about this question
PostgreSQL's tsvector converts text into a sorted list of distinct lexemes (normalized words), and the GIN (Generalized Inverted Index) stores mappings from lexemes to document positions, enabling fast ranked queries via ts_rank(). Under the hood, GIN uses a B-tree of posting lists, which scales well for high-cardinality data. A real-world scenario is an e-commerce catalog where product descriptions vary in length—tsvector with GIN handles stop words, stemming, and ranking by TF-IDF-like algorithms natively.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this PCDE question test?
Design and implement database schemas — This question tests Design and implement database schemas — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a tsvector column with a GIN index on that column — Option A is correct because PostgreSQL's tsvector data type, combined with a GIN index, is specifically designed for full-text search. It preprocesses text into lexemes, supports stemming and ranking, and the GIN index enables fast lookups for millions of rows, meeting the requirement for relevance-ranked results.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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