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Microsoft DP-800 Sample Question Answers
Question # 1
You have an Azure SQL database That contains a table named dbo.Products, dbo.Products contains three columns named Embedding Category, and Price. The Embedding column is defined as VECTOR(1536).You use Ai_GENERME_EMBEDOINGS and VECTOR_SEARCH to support semanticsearch and apply additional filters on two columns named Category and Price.You plan to change the embedding model from text-embedding-ada-002 to text- embedding-3-smalL Existing rows already contain embeddings in the Embedding column.You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.What should you do first?
A. Regenerate embeddings for the existing rows. B. Normalize the vector lengths before storing new embeddings. C. Convert the Embedding column to nvacchar(mex). D. Create a vector index on dbo.Products.Embedding.
Answer: A
Correct option: A. Regenerate embeddings for the existing rows.
Explanation: Changing from text-embedding-ada-002 to text-embedding-3-small changes the embedding space, because each model maps text to vectors differently. Even with the same 1,536 dimensions, vectors from the two models are not comparable, so mixing new query embeddings with old stored embeddings would make VECTOR_SEARCH distances meaningless. The first step is therefore to regenerate embeddings for all existing rows with the new model, for example in batches with AI_GENERATE_EMBEDDINGS. Because the dimensions stay the same, the VECTOR(1536) column remains valid, and applications can keep using VECTOR_SEARCH with Category and Price filters without runtime errors.
Key points: • Different models produce incompatible vector spaces. • All stored embeddings must come from the same model as queries. • Regenerating in batches limits the impact on the database. • The column definition stays valid when dimensions match.
In practice: The developer registers the new external model, re-embeds rows in batches into the Embedding column or a new column that is swapped in, rebuilds any vector index and then switches query embedding generation to the new model.
Why this matters: Consistent embeddings are essential for accurate semantic search after a model change.
Key takeaway: When switching embedding models, regenerate all existing embeddings before using the new model for queries.
Question # 2
You have an Azure SQL database that contains a table named Customer. Customer contains the following columns:NationalIDNumber is unique per customer.InvestigationNotes contains free-form text.You have a stored procedure that performs point lookups by NationalIDNumber and returns InvestigationNotes in the query results without filtering on InvestigationNotes.You need to encrypt both columns by using Always Encrypted and the highest security possible.Which type of Always Encrypted encryption should you use for each column?
A. randomized for NationalIDNumber and deterministic for InvestigationNotes B. randomized for NationalIDNumber and randomized for InvestigationNotes C. deterministic for NationalIDNumber and randomized for InvestigationNotes D. deterministic for NationalIDNumber and deterministic for InvestigationNotes
Answer: C
Correct option: C. deterministic for NationalIDNumber and randomized for InvestigationNotes
Explanation: Always Encrypted offers deterministic and randomized encryption. Randomized encryption is more secure because the same value produces different ciphertext each time, but it does not support equality comparisons. Deterministic encryption produces the same ciphertext for the same value, which enables equality lookups. The stored procedure performs point lookups on NationalIDNumber, so that column needs deterministic encryption; because each value is unique, it reveals almost nothing about repetitions. InvestigationNotes is only returned, never filtered, joined or indexed, so it should use randomized encryption for the highest security.
Key points: • Deterministic encryption supports equality lookups. • Unique values limit what deterministic encryption can reveal. • Randomized encryption provides the strongest protection. • Columns that are only returned do not need deterministic encryption. • A BIN2 collation is required on deterministically encrypted character columns.
In practice: The developer encrypts NationalIDNumber with ENCRYPTION_TYPE = DETERMINISTIC and a BIN2 collation, encrypts InvestigationNotes with RANDOMIZED, and enables Always Encrypted in the application connection string.
Why this matters: Choosing the encryption type per column balances security with the queries the application needs.
Key takeaway: Use deterministic encryption only for columns used in equality lookups and randomized encryption for everything else.
Question # 3
You have a GitHub Codespaces environment that has GitHub Copilot Chat installed and is connected to a SQL database in Microsoft Fabric named DB1 DB1 contains tables named Sales.Orders and Sales.Customers.You use GitHub Copilot Chat in the context of DB1 .A company policy prohibits sharing customer Personally Identifiable Information (Pll), secrets, and query result sets with any Al service.You need to use GitHub Copilot Chat to write and review Transact-SQL code for a new stored procedure that will join Sales.Orders to sales .Customers and return customer names and email addresses. The solution must NOT share the actual data in the tables with GitHub Copilot Chat.What should you do?
A. From Sales.Customers, paste several rows that include email addresses into a chat, so that GitHub Copilot Chat can infer edge cases. B. Run a select statement that returns customer names and email addresses and provide the result set to GitHub Copilot Chat so that GitHub Copilot Chat can generate the stored procedure. C. Provide the database connection string to GitHub Copilot Chat so that GitHub Copilot Chat can validate the stored procedure. D. Ask GitHub Copilot Chat to generate the stored procedure by using schema details only.
Answer: D
Correct option: D. Ask GitHub Copilot Chat to generate the stored procedure by using schema details only.
Explanation: The company policy prohibits sharing PII, secrets and query result sets with AI services. To write a stored procedure that joins Sales.Orders to Sales.Customers and returns names and email addresses, GitHub Copilot Chat only needs the structure of the tables: table and column names, data types, keys and the relationship between them. Providing schema details, such as CREATE TABLE definitions, lets Copilot generate and review accurate Transact-SQL without seeing any real customer data. Edge cases can be described in words or with clearly synthetic examples, so no PII or secrets leave the environment.
Key points: • Copilot needs schema information, not actual data, to write code. • Table definitions describe columns, types and relationships. • Synthetic examples can illustrate edge cases safely. • No PII, secrets or result sets are shared.
In practice: The developer copies the CREATE TABLE scripts or describes the columns in the chat, asks Copilot to generate the procedure, reviews the code and tests it locally against the database.
Why this matters: Schema-only prompting follows data minimization while still getting useful help from AI tools.
Key takeaway: When using AI coding assistants with sensitive databases, share schema details only, never real data or secrets.
Question # 4
You have an Azure SQL database named AdventureWorksDB that contains a table named dbo.Employee.You have a C# Azure Functions app that uses an HTTP-triggered function with an Azure SQL input binding to query dbo.Employee.You are adding a second function that will react to row changes in dbo.Employee and write structured logs.You need to configure AdventureWorksDB and the app to meet the following requirements:• Changes to dbo.Employee must trigger the new function within five seconds.• Each invocation must processes no more than 100 changes.Which two database configurations should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A. Create an AFTER trigger on dbo.Employee for Data Manipulation Language (DML). B. SetSql Trigger MaxBatchSize to 100. C. Enable change tracking on the dbo. Employee table. D. Enable change tracking at the database level. E. Set Sql_Trigger_PollingIntervalMs to 5000. F. Enable change data capture (CDC) for dbo.Employee table changes
Answer: C,D
Correct option: C. Enable change tracking on the dbo. Employee table. D. Enable change tracking at the database level.
Explanation: The new function reacts to row changes using the Azure SQL trigger binding, which is built on SQL change tracking. The binding polls change tracking functions to find rows changed since the last processed version and invokes the function with batches of changes. Change tracking must be enabled at the database level, for example ALTER DATABASE AdventureWorksDB SET CHANGE_TRACKING = ON, and on the monitored table, ALTER TABLE dbo.Employee ENABLE CHANGE_TRACKING. These are the two required database configurations. The other requirements are met by the binding's default app settings, which poll about every second and process at most 100 changes per invocation.
Key points: • The SQL trigger binding relies on change tracking. • Change tracking must be on for the database. • Change tracking must also be on for dbo.Employee. • Default polling and batch settings meet the timing and batch limits.
In practice: The developer enables change tracking on the database with suitable retention and on dbo.Employee, deploys the function with the SQL trigger binding and confirms that changes trigger structured logging within seconds.
Why this matters: Correct database configuration enables reliable, near real-time change processing with Azure Functions.
Key takeaway: Enable change tracking on both the database and the table for the Azure SQL trigger binding.
Question # 5
You have an SDK-style SQL database project stored in a Git repository. The project targets an Azure SQL database.The CI build fails with unresolved reference errors when the project ieferences system objects.You need to update the SQL database project to ensure that dotnet build validates successfully by including the correct system objects in the database model for Azure SQL Database.Solution: Add the Microsoft.SqlServer.Dacpacs.Mastet NuGet package to the project.Does this meet the goal?
A. Yes B. No
Answer: B
Correct option: B. No
Explanation: The proposed solution adds Microsoft.SqlServer.Dacpacs.Master, which contains the master database model for on-premises SQL Server. The project targets Azure SQL Database, whose system objects differ from SQL Server: Azure exposes some objects that SQL Server lacks and omits some server-level objects. A SQL Server master model therefore does not accurately represent the target platform and can leave references unresolved or cause platform mismatch errors, so the answer is No. The correct package for this project is Microsoft.SqlServer.Dacpacs.Azure.Master, which provides the Azure SQL Database system object model and resolves references correctly during dotnet build.
Key points: • System object packages are specific to each target platform. • The Master package models on-premises SQL Server. • Azure SQL Database has a different set of system objects. • The Azure.Master package matches an Azure SQL target. • Pinning the package version keeps CI builds reproducible. • Using the wrong platform package can also hide errors that would appear in Azure.
In practice: The developer replaces the package reference with Microsoft.SqlServer.Dacpacs.Azure.Master and runs dotnet build in CI to confirm the unresolved references are resolved.
Why this matters: Matching system references to the target platform keeps database builds accurate.
Key takeaway: For projects targeting Azure SQL Database, reference Microsoft.SqlServer.Dacpacs.Azure.Master, not the SQL Server package.
Question # 6
You are developing an Azure SQL solution by using Microsoft Visual Studio 2026. The solution uses a GitHub repository.You plan to use GitHub Copilot Chat to access the GitHub repository tools by connecting to the GitHub MCP Server.You need to configure Visual Studio to support the planned configuration. The solution must rely on OAuth to access the MCP server.What should you create?
A. the <SOLUTIONDIR>\.vscode\settings.json file that contains a URL of [https://api.githubcopilot.com/mcp/](https://api.githubcopilot.com/mcp/) B. the <SOLUTIONDIR>\.vs\settings.json file that contains the personal access token (PAT) that has the repo scope C. the <SOLUTIONDIR>\.github\mcp.json file that contains the personal access token (PAT) that has the repo scope D. the <SOLUTIONDIR>\.mcp.json file that contains a URL of[https://api.githubcopilot.com/mcp/](https://api.githubcopilot.com/mcp/)
Answer: D
Correct option: D. the <SOLUTIONDIR>\.mcp.json file that contains a URL of [https://api.githubcopilot.com/mcp/](https://api.githubcopilot.com/mcp/)
Explanation: Visual Studio 2022 version 17.14 and later, including Visual Studio 2026, support MCP servers in GitHub Copilot Chat agent mode and read server configurations from defined locations. A .mcp.json file in the solution directory is the standard way to configure servers for a solution, and it can be committed so the team shares the configuration. Pointing it at the hosted GitHub MCP Server URL, https://api.githubcopilot.com/mcp/, as an HTTP server lets Visual Studio handle OAuth: the user signs in with GitHub and authorizes access, and the IDE stores and refreshes the token securely, with no secret written to any file.
Key points: • Visual Studio reads MCP servers from .mcp.json in the solution directory. • The hosted GitHub MCP Server is reached by its HTTP URL. • OAuth sign-in replaces personal access tokens. • The file can be shared through source control.
In practice: The developer creates <SOLUTIONDIR>\.mcp.json with an HTTP server entry for the GitHub MCP Server URL, opens Copilot Chat in agent mode, completes the OAuth sign-in and confirms the GitHub tools are available.
Why this matters: Solution-level MCP configuration with OAuth gives secure, team-wide access to repository tools.
Key takeaway: In Visual Studio, configure the GitHub MCP Server URL in the solution's .mcp.json file to use OAuth.
Question # 7
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders, dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.orders.Solution: Run the following Transact-SQL statement.DELETE FROM dbo.OrdersWHERE OrderDate < DATEADD(nonth, -36, SYSUTCDATETIME());Does this meet the goal?
A. Yes B. No
Answer: B
Correct option: B. No
Explanation: The proposed DELETE statement does not meet the goal, so the answer is No. DELETE is a fully logged, row-by-row operation: removing a whole month of orders writes log records for every row and index entry, uses significant I/O and CPU and takes many locks that may escalate to a table lock, blocking other queries. The predicate also depends on SYSUTCDATETIME(), so it may remove part of a month or miss part of the oldest month instead of aligning with partition boundaries. Because the table is already partitioned by month, the right approach is a partition-level operation such as TRUNCATE TABLE dbo.Orders WITH (PARTITIONS (n)) or SWITCH PARTITION, which removes the month as a fast metadata operation.
Key points: • DELETE logs every row and can run for a long time. • Many locks can escalate and block other queries. • A time-based predicate does not align with partition boundaries. • Partition truncation or switching minimizes impact.
In practice: The developer replaces the DELETE with a partition truncation for the oldest partition, scheduled during a quiet period, and maintains the partition function for the sliding window.
Why this matters: Choosing partition operations over large deletes protects performance on busy tables.
Key takeaway: Avoid large DELETE statements on partitioned tables; truncate or switch out whole partitions instead.
Question # 8
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders. dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.Orders.Solution: Identify the partition scheme for the oldest month, and then run the following Transact-SQL statement:SQLALTER TABLE dbo.OrdersDROP PARTITION SCHEME {partition_scheme_name};Does this meet the goal?
A. Yes B. No
Answer: B
Correct option: B. No
Explanation: The proposed statement, ALTER TABLE dbo.Orders DROP PARTITION SCHEME {partition_scheme_name}, does not exist in Transact-SQL, so it fails and removes no rows, and the answer is No. A partition scheme maps all partitions of the table to filegroups; there is one scheme for the whole table, not one per month, and dropping a scheme is only possible when nothing uses it. Removing the oldest month efficiently requires working with that month's partition, for example TRUNCATE TABLE dbo.Orders WITH (PARTITIONS (n)) or switching the partition out to a staging table. These metadata operations complete almost instantly with minimal logging and brief locks, so other queries are barely affected.
Key points: • ALTER TABLE has no DROP PARTITION SCHEME clause. • A partition scheme covers the whole table, not one month. • Partition truncation or switching removes a single month. • Metadata operations minimize impact on concurrent queries.
In practice: The developer finds the oldest partition number with $PARTITION, truncates or switches out that partition and then merges the empty boundary with ALTER PARTITION FUNCTION ... MERGE RANGE.
Why this matters: Using partition-level operations is the right way to manage sliding windows of data.
Key takeaway: Remove old months by truncating or switching out their partitions, not by dropping the partition scheme.
Question # 9
You have a GitHub Enterprise subscription.Your team is developing an Azure SQL dataset solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.A mix of GitHub Copilot instructions is configured at different levels, including organization- wide, repository-wide, agent-specific, and personal.Based on the GitHub Copilot instruction precedence rules, which instructions will take precedence over the others?
A. organization-wide B. agent-specific C. repository-wide D. personal
Answer: D
Correct option: D. personal
Explanation: GitHub Copilot supports custom instructions at several levels, and when several apply, they are combined in a defined priority order. Personal instructions have the highest priority, followed by repository instructions, and organization instructions have the lowest priority. When instructions conflict, Copilot follows the higher-priority one. Personal instructions reflect the explicit preferences of the user who is actually working with Copilot, such as response language or level of detail, so they take precedence over repository-wide and agent-specific guidance stored in the repository and over organization-wide baselines.
Key points: • Instructions from several levels are combined for each request. • Personal instructions have the highest priority. • Repository and agent-specific instructions come next. • Organization instructions provide the lowest-priority baseline. • When instructions do not conflict, Copilot uses guidance from all levels together. • Organization owners can still enforce baseline standards that repositories and individuals build on. • Clear, non-conflicting instructions at each level give the most predictable results.
In practice: Teams put broad standards in organization instructions, project conventions in repository and agent files, and let individuals tailor behavior with personal instructions, knowing those win in conflicts.
Why this matters: Understanding precedence helps teams design instructions that work together predictably.
Key takeaway: In GitHub Copilot, personal instructions take precedence over repository and organization instructions.
Question # 10
You have an Azure SQL database named ProductsDB.You deploy Data API builder (DAB) to Azure Container Apps by using the [mcr.microsoft.com/azure-databases/data-api-builder:latest](https://mcr.microsoft.com/azure-databases/data-api-builder:latest) image.The container app has the following configurations:Secrets: mssql-connection-string, dab-config-base64Environment variables:MSSQL_CONNECTION_STRING=secretref:mssql-connection-stringDAB_CONFIG_BASE64=secretref:dab-config-base64Ingress: External on port 5000Users report that the /health endpoint returns a healthy response, but all requests that query an entity named Products fail and generate a connection error.You confirm that the SQL login in the connection string is correct and the database exists.You need to ensure that the container app can establish connections to the Azure SQL logical server without changing the container app deployment settings or the DAB configuration file.What should you do on the Azure SQL logical server?
A. Create a firewall rule that allows a start and end IP address of 0.0.0.0. B. Enable FORCE_LAST_GOOD_PLAN automatic tuning for ProductsDB. C. Create an auto-failover group for ProductsDB. D. Run DBCC CHECKDB on ProductsDB.
Answer: A
Correct option: A. Create a firewall rule that allows a start and end IP address of 0.0.0.0.
Explanation: The DAB container runs correctly, because the /health endpoint responds and the configuration and secrets load, but queries fail with connection errors even though the login and database are correct. This indicates that the Azure SQL logical server's firewall is blocking the container app. Creating a server firewall rule with a start and end IP address of 0.0.0.0, equivalent to allowing Azure services and resources to access the server, permits connections that originate from Azure services such as Container Apps, whose outbound IPs can change. The rule is configured on the logical server, so no changes are needed to the container app or the DAB configuration.
Key points: • Healthy DAB but failed queries point to a network block. • The Azure SQL firewall blocks connections by default. • The 0.0.0.0 rule allows connections from Azure services. • No container or configuration changes are required.
In practice: The administrator adds the firewall rule on the logical server, retests the Products endpoint and, for stronger isolation later, plans a private endpoint with virtual network integration.
Why this matters: Diagnosing which layer fails, application or network, leads quickly to the right fix.
Key takeaway: Allow Azure services with a 0.0.0.0 firewall rule when an Azure-hosted app cannot reach Azure SQL.
