Microsoft DP-800 Dumps - Pass Developing AI-Enabled Database Solutions Exam in First Attempt 2026

Because the SQL Server 2025 instance has no outbound network connectivity, the embedding model cannot rely on a remote REST endpoint such as Azure AI Foundry or Azure OpenAI. Microsoft's CREATE EXTERNAL MODEL documentation includes a local deployment pattern using ONNX Runtime running locally with local runtime/model paths. That is the right design when embeddings must be generated inside the SQL Server instance without external network access. Microsoft explicitly documents a local ONNX Runtime example for SQL Server 2025 and notes the required local runtime setup and model path configuration.

The permission requirement is handled by granting the application user access to use the external embeddings model. Microsoft's AI_GENERATE_EMBEDDINGS documentation states that, as a prerequisite, you must create an external model of type EMBEDDINGS that is accessible via the correct grants, roles, and/or permissions. Among the choices, the exam-appropriate action is to grant execute permission on the external model project to AlApplicationUser so only that database user can run embedding generation through the model.

For an SDK-style SQL database project targeting Azure SQL Database, Microsoft recommends using the Azure SQL system DACPAC as a NuGet package reference rather than adding a direct artifact reference to master.dacpac for new SDK-style development. Microsoft's SQL Database Projects documentation says direct .dacpac artifact references are not recommended for new development in SDK-style projects; instead, use NuGet package references.

Because the goal is specifically to make dotnet build validate successfully with the correct Azure SQL system objects, adding an artifact reference to master.dacpac is not the recommended SDK-style solution. It can work in some project styles, but it does not meet the stated goal as the proper approach for SDK-style Azure SQL projects.

GitHub documents that repository custom instructions for Copilot Chat can be disabled for your own use in the editor settings, and that doing so does not affect other users. In VS Code, this is controlled through settings related to instruction files, where you can disable the use of repository instruction files for your own environment.

The other options are incorrect:

B is not a documented mechanism for disabling repository-level Copilot instructions.

C would remove the repository instruction file itself and therefore affect everyone using that repository, which violates the requirement.

The correct answer is C because the email must be normalized by removing only the subaddressing portion between the plus sign and the @, while preserving the domain. JSON_VALUE is the correct function to extract the scalar email value from the JSON document. Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.

Then REGEXP_REPLACE should remove the pattern \+.*@ and replace it with a single @. For example:

user1+promo@contoso.com user1@contoso.com

Microsoft documents that REGEXP_REPLACE returns the source string with text matching the regular expression replaced by the replacement string, and that an empty or custom replacement string can be used to reshape the result.

Why the other options are wrong:

A removes everything from + to the end of the string, which would leave user1 and lose @contoso.com.

B tries to extract a string that already excludes +..., but it does not reliably reconstruct the normalized address in this pattern.

D also removes everything after +, including the domain, which is incorrect.

So the normalized-email expression is:

REGEXP_REPLACE(JSON_VALUE(Payload, '$.customer_email'), '\+.*@', '@')

The requirement says the stored values and query inputs must both be protected, and users must not be able to infer relationships or repetitions in the data from the encrypted output. Microsoft documents that deterministic encryption always produces the same ciphertext for the same plaintext, which allows equality comparisons but also leaks patterns. By contrast, randomized encryption produces a different encrypted value each time for the same plaintext, which improves security and prevents pattern analysis based on repeated ciphertext values.

That makes randomized encryption the right choice here:

It protects data at rest and in transit/query parameters under Always Encrypted's client-side encryption model.

It prevents attackers from learning that the same plaintext value appears repeatedly, because repeated inputs do not produce repeated ciphertext.

Why the other options are wrong:

A . Always Encrypted with secure enclaves adds richer confidential query support, but the key protection property the question is testing is the encryption type. The requirement to prevent inference from repeated ciphertext points specifically to randomized encryption.

C . RLS controls row access, not value confidentiality.

D . Deterministic encryption allows equality-based operations but leaks repetition patterns, which the question explicitly forbids.