- A
Use Vertex AI Embeddings API to generate embeddings, index them in Vertex AI Vector Search, and query for nearest neighbors
This scalable approach allows efficient similarity search across large datasets.
- B
Use BigQuery ML to train a custom similarity model on the reviews
Why wrong: BigQuery ML is for SQL-based ML, but semantic similarity typically requires embeddings and vector search.
- C
Use the Gemini API to compute semantic similarity directly
Why wrong: Gemini does not provide a direct similarity computation service; embeddings are required.
- D
Use Vertex AI Embeddings API to generate embeddings and then compute cosine similarity pairwise
Why wrong: Pairwise comparison does not scale to thousands; vector search is needed.
Question 91 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
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?
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
Option A is correct because 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.
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 Vertex AI Embeddings API to generate embeddings, index them in Vertex AI Vector Search, and query for nearest neighbors
Why this is correct
This scalable approach allows efficient similarity search across large datasets.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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
Gemini does not provide a direct similarity computation service; embeddings are required.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between generating embeddings and efficiently querying them at scale; the trap here is that candidates may think pairwise cosine similarity is sufficient, ignoring the scalability requirement implied by 'thousands of customer reviews.'
Trap categories for this question
Similar concept trap
BigQuery ML is for SQL-based ML, but semantic similarity typically requires embeddings and vector search.
Detailed technical explanation
How to think about this question
Vertex AI Embeddings API uses transformer-based models (e.g., textembedding-gecko) to output 768-dimensional vectors. Vertex AI Vector Search implements ScaNN (Scalable Nearest Neighbors) for ANN search, leveraging techniques like product quantization and tree-based partitioning to achieve sub-linear query time. In a real-world scenario, a team analyzing 10,000 reviews would use Vector Search to find the top-k similar reviews for a given query in milliseconds, whereas pairwise cosine similarity would require ~50 million comparisons.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Generative AI Concepts and Technologies — study guide chapter
Learn the concepts, then practise the questions
- →
Generative AI Concepts and Technologies practice questions
Targeted practice on this topic area only
- →
All Generative AI Leader questions
997 questions across all exam domains
- →
Google Cloud Generative AI Leader Generative AI Leader study guide
Full concept coverage aligned to exam objectives
- →
Generative AI Leader practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related Generative AI Leader practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Fundamentals of Generative AI practice questions
Practise Generative AI Leader questions linked to Fundamentals of Generative AI.
Business Strategies for Generative AI Solutions practice questions
Practise Generative AI Leader questions linked to Business Strategies for Generative AI Solutions.
Generative AI Concepts and Technologies practice questions
Practise Generative AI Leader questions linked to Generative AI Concepts and Technologies.
Google AI Ecosystem and Strategy practice questions
Practise Generative AI Leader questions linked to Google AI Ecosystem and Strategy.
Responsible AI and Data Governance practice questions
Practise Generative AI Leader questions linked to Responsible AI and Data Governance.
Google Cloud's Generative AI Offerings practice questions
Practise Generative AI Leader questions linked to Google Cloud's Generative AI Offerings.
Techniques to Improve Generative AI Model Output practice questions
Practise Generative AI Leader questions linked to Techniques to Improve Generative AI Model Output.
Applying Generative AI in Business practice questions
Practise Generative AI Leader questions linked to Applying Generative AI in Business.
Generative AI Leader fundamentals practice questions
Practise Generative AI Leader questions linked to Generative AI Leader fundamentals.
Generative AI Leader scenario practice questions
Practise Generative AI Leader questions linked to Generative AI Leader scenario.
Generative AI Leader troubleshooting practice questions
Practise Generative AI Leader questions linked to Generative AI Leader troubleshooting.
Practice this exam
Start a free Generative AI Leader practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Vertex AI Embeddings API to generate embeddings, index them in Vertex AI Vector Search, and query for nearest neighbors — Option A is correct because 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.
What should I do if I get this Generative AI Leader 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.