- A
Specify embedding dimension size in the index config.
Why wrong: Dimension is derived from the model; not set manually.
- B
Deploy the index to the IndexEndpoint.
Deployment makes the index available for querying.
- C
Train a custom embedding model.
Why wrong: You can use pre-trained models; training is optional.
- D
Download the query embeddings to local storage.
Why wrong: Query is sent as an API call, not downloaded.
- E
Create an IndexEndpoint resource.
IndexEndpoint is the serving infrastructure.
Question 435 of 997
Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
An organization is building a search application using Vertex AI Vector Search. They have encoded their documents into embeddings and want to retrieve the most similar documents for a query. Which TWO actions are required to set up a Vector Search index?
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
✓
Deploy the index to the IndexEndpoint.
Option B is correct because deploying the index to an IndexEndpoint is a required step to make the Vector Search index available for online queries. Without deployment, the index exists only as a metadata resource and cannot serve similarity search requests. The deployment binds the index to a specific endpoint, allocating machine resources for real-time inference.
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.
- ✗
Specify embedding dimension size in the index config.
Why it's wrong here
Dimension is derived from the model; not set manually.
- ✓
Deploy the index to the IndexEndpoint.
Why this is correct
Deployment makes the index available for querying.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Train a custom embedding model.
Why it's wrong here
You can use pre-trained models; training is optional.
- ✗
Download the query embeddings to local storage.
Why it's wrong here
Query is sent as an API call, not downloaded.
- ✓
Create an IndexEndpoint resource.
Why this is correct
IndexEndpoint is the serving infrastructure.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the index creation steps with the deployment steps, thinking that merely creating the index resource is sufficient for querying, when in fact deployment to an IndexEndpoint is mandatory for online serving.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Vector Search uses a ScaNN (Scalable Nearest Neighbors) algorithm for approximate nearest neighbor (ANN) search. The index is built from the provided embeddings and stored in a distributed manner. When deployed, the IndexEndpoint creates a gRPC endpoint that accepts query embeddings and returns the top-k nearest neighbors, with latency typically under 10 ms for high-dimensional vectors.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy the index to the IndexEndpoint. — Option B is correct because deploying the index to an IndexEndpoint is a required step to make the Vector Search index available for online queries. Without deployment, the index exists only as a metadata resource and cannot serve similarity search requests. The deployment binds the index to a specific endpoint, allocating machine resources for real-time inference.
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.
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Last reviewed: Jul 4, 2026
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