Microsoft DP-800 Cheat Sheet: SQL AI Developer

May 1, 2026

Review Microsoft SQL AI Developer Associate (DP-800) SQL development, AI-enabled database design, vectors, retrieval, security, modernization, performance, and DevOps traps before using the DP-800 practice page.

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DP-800 is a SQL developer route with AI-enabled database design. Use this cheat sheet to separate normal database-development decisions from vector, retrieval, model-endpoint, security, and modernization decisions before trying the DP-800 sample questions.

Use this with practice. Review the SQL AI checklist, then open the DP-800 page for sample questions, current-exam notes, and related IT Mastery practice paths.

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Exam snapshot

Field	Detail
Issuer	Microsoft
Route name	Developing AI-Enabled Database Solutions
Exam code	DP-800
Product family	Microsoft SQL
Status in IT Mastery	Sample questions with Notify me form

Topic map

Area	What to know	Common trap
Database development	Schema design, T-SQL, programmability, indexing, constraints, and data integrity	Treating AI features as a substitute for sound relational design
AI-enabled SQL	Embeddings, vector search, retrieval patterns, model endpoints, and semantic search	Adding vectors before defining retrieval quality and security boundaries
Security and access	Least privilege, identities, secrets, data exposure, and application access patterns	Giving the app broad database rights because AI features are involved
Performance and operations	Indexing, query plans, workload behavior, monitoring, deployment, and drift	Optimizing model calls while ignoring slow SQL access paths
Modernization	SQL Server, Azure SQL, migration, compatibility, and app changes	Moving platforms without checking dependencies and performance assumptions

Must-know distinctions

Distinction	How to decide
Relational query vs semantic retrieval	Relational queries match structured predicates; semantic retrieval matches meaning through embeddings.
Embedding generation vs vector search	Embedding generation creates vectors; vector search compares them for similarity.
Stored procedure vs application logic	Keep data-close logic in SQL when consistency and performance benefit; keep orchestration in the app when workflow spans systems.
Indexing vs caching	Indexes improve database access paths; caches avoid repeated reads when freshness rules permit.
Authentication vs authorization	Authentication proves identity; authorization grants allowed actions.
Migration vs modernization	Migration moves workload; modernization changes architecture, performance model, or integration pattern.
Prompt output vs database truth	AI output may summarize or infer; database records remain the source for persisted facts.

High-yield checklist

Start with the data model and query pattern before adding AI features.
Define how embeddings are created, stored, refreshed, and secured.
Check whether vector retrieval, full-text search, or relational filtering best fits the question.
Keep sensitive data boundaries clear when sending text to model endpoints.
Use least privilege for application identities and database roles.
Review query plans, indexes, statistics, and parameter behavior for performance issues.
Treat AI answers as generated output that may require grounding and validation.
Separate database deployment, schema migration, and application rollout concerns.
Monitor database workload, model call latency, errors, and cost signals.
Verify compatibility and dependency risks before a modernization move.

Common traps

Using semantic search when exact filtering is required.
Sending more data to a model than the application needs.
Ignoring indexing because the scenario includes AI.
Confusing generated summaries with authoritative database state.
Hard-coding database credentials in application code or pipeline settings.
Treating vector storage as useful without a retrieval and evaluation plan.

Practice strategy

Use the DP-800 page to classify misses as SQL design, AI retrieval, security, performance, or modernization. If AI retrieval misses dominate, review embeddings, vector similarity, grounding, and result validation. If SQL misses dominate, return to schema, indexing, T-SQL, and transaction basics.

Official source

Microsoft Learn study guide for Exam DP-800

Revised on Monday, May 25, 2026