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Generative AI Leader Practice Question: A data science team wants to compare semantic…
A data science team wants to compare semantic similarity between thousands of customer reviews to identify emerging themes. Which Google Cloud service and approach should they use?
⚠ Common exam trap
A common pitfall on Google exams is assuming that pairwise cosine similarity on embeddings is sufficient for large-scale semantic search, but for thousands of reviews, using Vertex AI Vector Search for approximate nearest neighbor (ANN) indexing and querying is necessary for efficiency and scalability.
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 Vertex AI Embeddings API to generate embeddings, index them in Vertex AI Vector Search, and query for nearest neighbors
Vertex AI Embeddings API generates dense vector representations of text, which can be indexed in Vertex AI Vector Search for efficient approximate nearest neighbor (ANN) search. This approach scales to thousands of reviews and allows the team to query for the most semantically similar reviews, enabling theme discovery without pairwise computation.
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 Vertex AI Embeddings API to generate embeddings, index them in Vertex AI Vector Search, and query for nearest neighbors
Why this is correct
Embeddings convert each review into a dense vector capturing semantics; Vertex AI Vector Search indexes them and returns nearest neighbours efficiently at scale. This satisfies the requirement to compare semantic similarity across thousands of reviews and surface emerging themes.
- ✗
Use BigQuery ML to train a custom similarity model on the reviews
Why it's wrong here
BigQuery ML is for SQL-based ML, but semantic similarity typically requires embeddings and vector search.
- ✗
Use the Gemini API to compute semantic similarity directly
Why it's wrong here
Calling the Gemini API per review pair scales poorly across thousands of reviews and returns generative text rather than a comparable numeric similarity score. Gemini suits summarisation or generation tasks; for bulk semantic similarity, Vertex AI text embeddings with vector search produce efficient, deterministic similarity rankings.
- ✗
Use Vertex AI Embeddings API to generate embeddings and then compute cosine similarity pairwise
Why it's wrong here
Pairwise comparison does not scale to thousands; vector search is needed.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.