Courseiva
Implement Generative AI And Agentic SolutionsmediumMultiple SelectObjective-mapped

AI-103 Implement Generative AI And Agentic Solutions Practice Question

When implementing Retrieval-Augmented Generation (RAG) using Azure AI Search and Azure OpenAI, which THREE strategies help optimize chunk retrieval relevance and reduce noise? (Choose three)

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

Configure hybrid search combining vector embeddings and keyword full-text search.

Hybrid search (combining vectors and keywords), semantic ranking, and appropriate chunk sizing are proven strategies for optimizing RAG relevance.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Configure hybrid search combining vector embeddings and keyword full-text search.

    Why this is correct

    Hybrid search leverages the strengths of both dense vector search and sparse keyword search to retrieve the most relevant chunks.

  • Store all corporate documents in a single unindexed raw text blob without metadata fields.

    Why it's wrong here

    Unindexed raw text blobs without metadata prevent efficient filtering and targeted retrieval.

  • Set the embedding model dimensions to the lowest possible setting to maximize retrieval speed.

    Why it's wrong here

    Lower dimensions reduce semantic expressiveness and typically degrade retrieval accuracy in vector search.

  • Implement document chunking strategies with appropriate overlap to preserve context across boundaries.

    Why this is correct

    Chunking with overlap ensures that sentences spanning boundary lines are not split apart, retaining contextual coherence.

  • Enable semantic ranking to re-rank search results using deep learning models.

    Why this is correct

    Semantic ranking significantly improves precision by re-ranking initial search results based on deep semantic meaning.

About these practice questions

This AI-103 question is part of Courseiva's 510-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed August 2026 · checked against the official Microsoft exam blueprint

This AI-103 practice question is part of Courseiva's free Microsoft 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 AI-103 exam.