Microsoft · DP-800
Microsoft Certified: SQL AI Developer Associate (DP-800) Practice Tests
Validates expertise in designing and developing AI-enabled database solutions across Microsoft SQL platforms including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. Covers T-SQL development, CI/CD practices, security, performance optimization, and implementing AI capabilities such as vector search and RAG.
Exam Details
Practice Questions
600
≈ 12 practice exams
Duration
100 minutes
Passing Score
700/1000
Difficulty
AssociateLast Updated
May 2026
Topics Covered
Design and Develop Database SolutionsImplement Programmability ObjectsWrite Advanced T-SQL CodeAI-Assisted SQL Development ToolsData Security and ComplianceDatabase Performance OptimizationCI/CD with SQL Database ProjectsAzure Services IntegrationModels, Embeddings, and Intelligent SearchRetrieval-Augmented Generation (RAG)
Microsoft Certified: SQL AI Developer Associate (DP-800) Practice Exam Preparation
Use this DP-800 practice exam to prepare for Microsoft Certified: SQL AI Developer Associate (DP-800) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for Microsoft DP-800, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Design and Develop Database Solutions, Implement Programmability Objects, Write Advanced T-SQL Code, AI-Assisted SQL Development Tools, and Data Security and Compliance. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Design and develop database solutions38%
Secure, optimize, and deploy database solutions38%
Implement AI capabilities in database solutions28%
Exam Overview
The Microsoft Certified: SQL AI Developer Associate certification, earned by passing Exam DP-800 (Developing AI-Enabled Database Solutions), validates expertise in designing and building AI-enabled database solutions across the full breadth of Microsoft SQL platforms—Microsoft SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. The credential covers the complete lifecycle of modern database development: schema design, advanced T-SQL programming, performance optimization, data security, CI/CD automation via SQL Database Projects, and deep integration with Azure services such as Data API builder (DAB), Azure Monitor, and Azure Functions.
Released in early 2026, the certification reflects the industry shift toward embedding AI directly inside the database tier rather than relying solely on external AI services. Candidates must demonstrate practical knowledge of AI-assisted development tooling (GitHub Copilot, Microsoft Copilot in Fabric, Model Context Protocol), as well as core AI concepts—vector embeddings, semantic search, hybrid search, and Retrieval-Augmented Generation (RAG) using native T-SQL functions and the sp_invoke_external_rest_endpoint stored procedure.
Who Should Take This Exam
This certification targets mid-level database developers—typically with two or more years of hands-on T-SQL experience—who are expanding into AI-integrated and cloud-scale architectures. Ideal candidates hold roles such as SQL/database developer, Azure SQL developer, data engineer, or backend application developer who owns the database layer. Professionals working with Microsoft Fabric, Azure SQL Database, or SQL Server who need to expose intelligent search or natural-language interfaces to applications are a natural fit.
Candidates collaborate daily with application developers, DBAs, architects, AI engineers, DevSecOps engineers, and security administrators. Some exposure to GitHub-based CI/CD workflows and foundational AI concepts (embeddings, vectors, language models) is expected before attempting the exam.
Prerequisites
Microsoft does not enforce formal prerequisites for DP-800, but the exam assumes solid practical experience. Candidates should be comfortable writing complex T-SQL including CTEs, window functions, JSON functions, and stored procedures before studying the AI-specific content. Familiarity with database design fundamentals—indexes, constraints, partitioning, row-level security, Always Encrypted, and Dynamic Data Masking—is assumed throughout.
On the tooling side, experience with GitHub (branching, pull requests, Actions) and a working knowledge of Azure services such as Azure Functions, Logic Apps, and Azure Monitor will reduce the learning curve significantly. Exposure to AI/ML concepts—particularly what embeddings are, how vector similarity works, and what a language model prompt looks like—is recommended. Microsoft's free self-paced learning path 'Become a SQL AI Developer: Prepare for Certification Exam DP-800' on Microsoft Learn can fill gaps in any of these areas.
Exam Format
Exam DP-800 is a proctored assessment delivered through Pearson VUE, available in English. Candidates have 120 minutes to complete the exam (the official certification page specifies 120 minutes; allow approximately 100 minutes of active test time). The exam may include interactive lab or scenario-based components in addition to standard multiple-choice and multi-select question types. Candidates can explore the user interface in advance using Microsoft's free Exam Sandbox at aka.ms/examdemo.
A scaled score of 700 or higher (on a 1–1000 scale) is required to pass. If a candidate fails the first attempt, they must wait 24 hours before retaking; subsequent retake intervals vary per Microsoft's standard retake policy. The certification is valid for one year and can be renewed at no cost via an online assessment on Microsoft Learn. Exam price varies by country/region as set by Pearson VUE.
Skills Measured
- 1.Design and develop database solutions (35–40%): Covers designing and implementing tables (including in-memory, temporal, external, ledger, graph, and JSON columns), database constraints, sequences, and partitioning. Includes creating programmability objects (views, scalar functions, table-valued functions, stored procedures, triggers), writing advanced T-SQL (CTEs, window functions, JSON functions, regex, fuzzy string matching, MATCH graph queries, error handling), and designing SQL solutions using AI-assisted tools such as GitHub Copilot, Microsoft Copilot in Fabric, and Model Context Protocol (MCP) server integration.
- 2.Secure, optimize, and deploy database solutions (35–40%): Encompasses data security (Always Encrypted, column-level encryption, Dynamic Data Masking, Row-Level Security, object-level permissions, passwordless access, auditing, securing GraphQL/REST/MCP endpoints), query performance optimization (execution plans, DMVs, Query Store, Query Performance Insight, blocking/deadlock resolution), CI/CD using SQL Database Projects (unit/integration testing, branching, schema drift detection, deployment pipelines), and integration with Azure services including Data API builder (DAB), change event streaming (CES), CDC, Change Tracking, Azure Functions with SQL trigger binding, Azure Logic Apps, and Azure Monitor.
- 3.Implement AI capabilities in database solutions (25–30%): Focuses on designing and managing external AI models (multimodal, multilanguage, structured output), generating and maintaining vector embeddings (using triggers, Change Tracking, Azure Functions, CDC, CES, or Microsoft Foundry), implementing full-text, semantic vector, and hybrid search (VECTOR_DISTANCE, VECTOR_SEARCH, VECTOR_NORMALIZE, ANN vs. ENN, reciprocal rank fusion), and building RAG workflows using sp_invoke_external_rest_endpoint to send structured data to language models and extract responses.
Study Tips
- Start with the official DP-800 Study Guide (learn.microsoft.com/credentials/certifications/resources/study-guides/dp-800), which provides the full domain breakdown with bullet-level objectives updated as of March 2026. Use it as your primary checklist—check off each bullet only after you can demonstrate the skill hands-on.
- Complete Microsoft's free self-paced learning path 'Become a SQL AI Developer: Prepare for Certification Exam DP-800' on Microsoft Learn. The modules map directly to exam domains and include sandboxed lab environments for vector search, RAG, and SQL Database Projects.
- Build a personal lab environment: create a free Azure SQL Database or use a SQL Server 2022+ instance to practice native vector functions (VECTOR_DISTANCE, VECTOR_SEARCH), generate embeddings via an Azure OpenAI endpoint, and wire up a simple RAG pipeline using sp_invoke_external_rest_endpoint. Hands-on practice with these features is critical because the exam includes interactive scenario components.
- Practice the CI/CD domain by creating a real SQL Database Project in Visual Studio Code with the MSSQL extension, committing it to a GitHub repo, and configuring a GitHub Actions workflow for build validation and deployment. Schema drift detection and branching strategies are heavily tested in the 35–40% deployment domain.
- Take the free official Practice Assessment linked from the DP-800 study guide page once it becomes available (usually within eight weeks of general availability). Also visit the Exam Readiness Zone (learn.microsoft.com/shows/exam-readiness-zone) for domain-specific video walkthroughs aligned to DP-800 objectives.
- Dedicate focused time to the security subdomain, specifically Always Encrypted, Dynamic Data Masking, Row-Level Security, and securing MCP/GraphQL endpoints—this area spans both the 'design' and 'deploy' domains and questions often test the difference between configuration options rather than basic definitions.
- Use the Exam Sandbox (go.microsoft.com/fwlink/?linkid=2226877) before exam day to familiarize yourself with the interface, especially any interactive lab question types. Navigating an unfamiliar UI under time pressure is a common source of lost points.
Career Benefits
The SQL AI Developer Associate credential positions holders at the intersection of two high-demand skill sets—enterprise SQL development and applied AI engineering—making them valuable to organizations adopting Microsoft Fabric, Azure SQL, or SQL Server 2022+ for intelligent application backends. Roles commonly associated with this certification include SQL/Database Developer, Azure Data Engineer, Backend Developer, and AI Integration Engineer. Because the certification is new (2026), early adopters gain a differentiation advantage as enterprises ramp up AI-enabled data architectures across regulated industries such as finance, healthcare, and retail.
While Microsoft does not publish salary data tied to specific certifications, data engineers and database developers with Azure AI skills command salaries in the $110,000–$145,000 range in the US market (2025–2026 surveys), with premiums for Fabric and AI integration experience. The DP-800 complements adjacent certifications such as DP-300 (Azure Database Administrator Associate) and DP-700 (Fabric Data Engineer Associate), and serves as a natural progression for SQL professionals who have outgrown purely administrative or ETL-focused roles and want to build AI-powered data products.
Sample Questions
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 12 full-length practice exams.
Preview — answers shown1. A developer at Proseware Inc. is building a fraud detection system in SQL Server 2025. The system uses a SQL Graph database with an Account node table and a Transaction edge table representing money movement between accounts. The developer needs to identify all indirect fund flows from a known fraudulent account to any other account, traversing an arbitrary number of hops, and return the full account path as a concatenated string. Which two query elements are required to implement this? (Select two!)
Multiple correct answersAUse SHORTEST_PATH with the + quantifier inside MATCH to enable variable-length graph traversal
BUse a recursive CTE referencing the Transaction table to iteratively walk account relationships
CUse STRING_AGG with WITHIN GROUP (GRAPH PATH) to aggregate account names along the discovered path
DUse CROSS APPLY on Account to enumerate all possible next-hop accounts at each traversal step
EUse FOR XML PATH to concatenate node names into a path string after joining Account and Transaction
Explanation
SHORTEST_PATH with the + quantifier inside MATCH enables variable-length traversal through the graph, finding paths of any depth between nodes without specifying a fixed hop count at query time. STRING_AGG with the WITHIN GROUP (GRAPH PATH) clause is specifically designed for SQL Graph path queries and aggregates node values along the traversal result. Recursive CTEs can traverse relationships but do not use the graph-optimized SHORTEST_PATH algorithm, resulting in worse performance on large graphs. CROSS APPLY with account enumeration is a standard relational approach that bypasses SQL Graph optimizations entirely. FOR XML PATH is a legacy string concatenation method that cannot integrate with graph path result sets.
2. A compliance developer at Adatum Financial needs to implement a tamper-evident log for wire transfers in Azure SQL Database. Every transfer event must be permanently recorded as it occurs. Individual log entries must never be modified or deleted after insertion. Auditors must be able to cryptographically verify that no historical records have been altered or removed. Which table type should the developer create for the wire transfer log? (Select one!)
AA system-versioned temporal table with HISTORY_RETENTION_PERIOD = INFINITE
BAn updatable ledger table with SYSTEM_VERSIONING = ON
CA standard table protected by an INSTEAD OF trigger that blocks UPDATE and DELETE
DAn append-only ledger table
Explanation
Append-only ledger tables permit only INSERT operations and generate cryptographic hash chains over the stored rows, allowing auditors to detect any tampering, deletion, or modification after the fact. This directly satisfies both requirements: immutable entries and cryptographic tamper detection. Updatable ledger tables also provide cryptographic verification but still allow row modifications and deletions, which violates the immutability requirement. System-versioned temporal tables record full history and prevent accidental data loss but do not provide cryptographic verification and can be manipulated by privileged users. INSTEAD OF triggers enforced by application logic do not produce cryptographic proof of integrity and can be bypassed by database administrators with sufficient permissions.
3. A security architect at Woodgrove Bank stores customer net-worth values in an Azure SQL Database column encrypted with Always Encrypted using randomized encryption. An analyst team now requires the ability to run the query SELECT * FROM Customers WHERE NetWorth BETWEEN 500000 AND 2000000 while keeping the data encrypted at rest and in transit. The query currently fails because the database engine cannot compare ciphertext values. What is the correct approach to support this requirement? (Select one!)
ACreate a filtered nonclustered index on the NetWorth column with a WHERE NetWorth > 0 predicate to support range scans over the ciphertext
BGrant the analyst team the db_datareader role along with a column-level DECRYPT permission on the NetWorth column
CChange the NetWorth column encryption type from randomized to deterministic encryption
DEnable Always Encrypted with Secure Enclaves, configure a VBS enclave, and re-encrypt the column with an enclave-enabled column encryption key
Explanation
Always Encrypted with Secure Enclaves extends standard Always Encrypted by allowing computations such as range comparisons (BETWEEN, >, <, >=, <=), LIKE pattern matching, ORDER BY, and in-place encryption to execute inside a trusted execution environment called a Virtualization-Based Security (VBS) enclave. The column must retain randomized encryption and be protected by an enclave-enabled column encryption key. This preserves the confidentiality guarantee — the plaintext never leaves the enclave boundary — while enabling server-side range predicate evaluation that was impossible with standard Always Encrypted. Switching to deterministic encryption allows equality comparisons only, not range comparisons, because deterministic encryption produces the same ciphertext for identical plaintext but does not impose any order-preserving property that could support BETWEEN. Column-level DECRYPT permissions do not exist in SQL Server; Always Encrypted is a client-side encryption technology and the database engine never processes plaintext under any permission model. Filtered indexes on Always Encrypted columns cannot be created because the database engine evaluates index predicates against stored ciphertext values, which it cannot interpret.
4. A developer at Adatum Retail wants to register an Azure OpenAI text embedding model in Azure SQL Database so that T-SQL queries can call AI_GENERATE_EMBEDDINGS() to generate vector embeddings directly from stored procedures. Which step must be completed FIRST before executing the CREATE EXTERNAL MODEL statement? (Select one!)
ACreate a VECTOR INDEX using the DiskANN algorithm on the target table to support approximate nearest neighbor search
BCreate a DATABASE SCOPED CREDENTIAL containing the Azure OpenAI API key to authenticate requests to the external endpoint
CEnable the VECTOR data type on the target table columns that will store the resulting embeddings
DConfigure sp_invoke_external_rest_endpoint with the Azure OpenAI model endpoint URL and a maximum timeout value
Explanation
CREATE EXTERNAL MODEL requires an existing DATABASE SCOPED CREDENTIAL that securely stores the API key or managed identity credentials used to authenticate calls to the Azure OpenAI endpoint. The EXTERNAL MODEL statement references this credential by name, and the engine uses it to authorize every subsequent AI_GENERATE_EMBEDDINGS() invocation at runtime. Without the credential, the CREATE EXTERNAL MODEL statement cannot compile successfully. Enabling the VECTOR data type is a separate concern that applies to column definitions in tables that will store the resulting embedding values, not a prerequisite for registering the model itself. Creating a VECTOR INDEX is an optimization step performed after embeddings are populated and is entirely independent of model registration. sp_invoke_external_rest_endpoint is an alternative mechanism for calling arbitrary REST endpoints and is not involved in the CREATE EXTERNAL MODEL or AI_GENERATE_EMBEDDINGS() workflow.
5. A database administrator at Litware Inc. has applied Dynamic Data Masking with a partial() function to the SocialSecurityNumber column in the Employees table. A non-privileged analyst with only SELECT permission reports being able to determine the actual unmasked SSN values without holding the UNMASK permission. The DBA needs to understand the root cause of this vulnerability. Which statement correctly identifies why the analyst can recover the unmasked values? (Select one!)
ADynamic Data Masking requires the database collation to be set to a BIN2 collation on masked columns to prevent value inference
BDynamic Data Masking is not a true security boundary; users with SELECT permission can infer actual column values by issuing queries with varying WHERE clause filter predicates
CThe analyst was implicitly granted the UNMASK permission through membership in the db_datareader fixed database role
DThe partial() mask function does not protect numeric data types; only email() or random() masks prevent inference on SSN columns
Explanation
Dynamic Data Masking is explicitly documented as not being a true security boundary. Although the displayed output of masked columns is obfuscated, WHERE clause predicates are evaluated against the actual unmasked data before masking is applied to the result set. A user with SELECT permission can iteratively filter on the masked column with different predicate ranges and observe which predicates return rows, progressively narrowing down the actual values through an inference attack. This limitation is not specific to any mask function type since all DDM masks operate only on the display layer. BIN2 collation is a requirement for Always Encrypted deterministic encryption, not for Dynamic Data Masking, and does not affect DDM inference behavior. The db_datareader role grants SELECT permission on all tables but does not confer the UNMASK permission, which must be explicitly granted at the database, schema, table, or column level.
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