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Serving and Scaling Models →mediumMultiple ChoiceObjective-mapped

PMLE Serving and Scaling Models Practice Question

This PMLE practice question tests your understanding of serving and scaling models. 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.

You need to query a Vertex AI Vector Search index for nearest neighbours. The index is deployed on an endpoint. Which API method should you use to perform the query?

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

✓

projects.locations.indexEndpoints.findNeighbors

The correct API method to query a deployed Vertex AI Vector Search index for nearest neighbors is `projects.locations.indexEndpoints.findNeighbors`. This method is specifically designed for vector similarity search against an index endpoint, returning the nearest neighbors for a given query vector. The other options either target the wrong resource (indexes instead of indexEndpoints) or use methods intended for different purposes like model prediction.

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.

  • ✓

    projects.locations.indexEndpoints.findNeighbors

    Why this is correct

    Correct. The findNeighbors method is used to query a deployed index endpoint.

    Related concept

    Read the scenario before looking for a memorised answer.

  • ✗

    projects.locations.indexes.match

    Why it's wrong here

    Not the standard name; the correct is 'findNeighbors'.

  • ✗

    projects.locations.indexes.query

    Why it's wrong here

    Not a valid method; the correct method is on the deployed index.

  • ✗

    projects.locations.endpoints.predict

    Why it's wrong here

    This is for model prediction, not vector search.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The exam often tests the distinction between model prediction endpoints and vector search endpoints, so the trap here is confusing the `predict` method (for model inference) with the `findNeighbors` method (for vector similarity search), leading candidates to incorrectly select option D.

Detailed technical explanation

How to think about this question

Under the hood, Vertex AI Vector Search uses a ScaNN (Scalable Nearest Neighbors) algorithm for high-dimensional vector similarity. The `findNeighbors` method accepts a query vector and parameters like `neighborCount` and `approximateNeighborCount`, and it returns the nearest neighbor vectors along with their distances and metadata. In real-world scenarios, this is critical for building recommendation systems or semantic search applications where you need to find similar items based on embedding 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 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

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.

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FAQ

Questions learners often ask

What does this PMLE question test?

Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: projects.locations.indexEndpoints.findNeighbors — The correct API method to query a deployed Vertex AI Vector Search index for nearest neighbors is `projects.locations.indexEndpoints.findNeighbors`. This method is specifically designed for vector similarity search against an index endpoint, returning the nearest neighbors for a given query vector. The other options either target the wrong resource (indexes instead of indexEndpoints) or use methods intended for different purposes like model prediction.

What should I do if I get this PMLE 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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This PMLE 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 PMLE exam.