AI-102 Implement an agentic solution Practice Question
You are designing an agentic solution using Azure AI Agent Service. The agent needs to be able to both read and write data to an Azure SQL database. Which TWO tools should you configure?
⚠ Common exam trap
A common mix-up: candidates confuse KQL with SQL or assume a knowledge base can handle database transactions, but KQL is specific to Azure Data Explorer and knowledge bases are read-only retrieval systems, not transactional data stores.
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
✓
Function calling
Function calling is correct because it allows the agent to define custom functions that can execute SQL queries against Azure SQL Database, enabling both read and write operations through a structured API call pattern. Code Interpreter is correct because it can run Python code that uses libraries like pyodbc or SQLAlchemy to connect to Azure SQL Database and perform data manipulation, providing a sandboxed execution environment for dynamic SQL operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Function calling
Why this is correct
Function calling can be used to define custom functions that interact with the database.
- ✗
KQL
Why it's wrong here
KQL is for Azure Data Explorer, not Azure SQL.
- ✗
Grounding with Bing
Why it's wrong here
Grounding with Bing is for web search.
- ✓
Code Interpreter
Why this is correct
Code Interpreter can run Python code that reads and writes to SQL databases.
- ✗
Knowledge base
Why it's wrong here
Knowledge base is for search indexes, not direct database read/write.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.