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Tune Vertex Ai Vector Search Recall | professional-cloud-data-engineer | StudyCerts

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Professional Cloud Data Engineer
Preparing unstructured data for embeddings and retrieval-augmented generation (RAG)
A data engineering team is implementing a Retrieval-Augmente…

Professional Cloud Data Engineer

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Tune Vertex Ai Vector Search Recall

Section: Preparing unstructured data for embeddings and retrieval-augmented generation (RAG)

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A data engineering team is implementing a Retrieval-Augmented Generation (RAG) system on Google Cloud. They use Vertex AI text embedding models to generate high-dimensional vector embeddings of unstructured technical articles and index them using Vertex AI Vector Search with the Tree-AH algorithm.

During evaluation, the team discovers that while query response latency is extremely low, the index's Approximate Nearest Neighbor (ANN) search recall is insufficient, causing relevant context to be missed during generative AI grounding.

Which configuration change should the team make to improve search recall?

Decrease the leaf_nodes_to_search_percent parameter in the Tree-AH algorithm configuration

Switch the index update method from Stream update to Batch update

Deploy the index to an endpoint configured with a higher minReplicaCount and autoscaling enabled

Increase the leaf_nodes_to_search_percent parameter in the Tree-AH algorithm configuration

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Professional Cloud Data Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Designing data processing systems
Ingesting and processing the data
Storing the data
Preparing and using data for analysis
Maintaining and automating data workloads

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Preparing unstructured data for embeddings and retrieval-augmented generation (RAG)

Networking fundamentals

CI/CD (Continuous Integration and Continuous Deployment)

Capacity management (e.g., BigQuery Editions and reservations)

Configuring security controls within cloud environments to support compliance requirements (e.g., Assured Workloads, organizational policies, Access Transparency, Access Approval, regionalization of data and services).