DP-800 Exam Dumps and Practice Questions

Yes, this meets the goal. Microsoft documents that on a partitioned table, you can use TRUNCATE TABLE ... WITH (PARTITIONS (...)) to remove data from a specific partition, and that this is an efficient maintenance operation that targets only that data subset rather than the whole table. Microsoft's partitioning guidance explicitly lists truncating a single partition as an example of a fast partition-level maintenance or retention operation.

That matches the requirement to remove the oldest month while minimizing impact on other queries. Because the table is already partitioned by month on OrderDate, identifying the partition number for that oldest month and truncating only that partition is the correct low-impact approach, assuming the table and indexes are aligned as required for partition truncation.

To configure Visual Studio to support connecting to the GitHub MCP Server with OAuth, you need to create an OAuth application in GitHub. This OAuth application will provide the necessary credentials (client ID and client secret) that Visual Studio can use to authenticate with the GitHub MCP Server. The OAuth application registration allows Visual Studio to request and receive OAuth tokens for secure access to GitHub resources and MCP server tools.

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.

The correct choice is Option A because it provides the relevant semantic context the LLM needs while avoiding an unnecessary field that would add tokens without improving answer quality.

For LLM grounding and RAG-style context, Microsoft guidance emphasizes mapping and sending the fields that contain text pertinent to the use case. In this FAQ scenario, the useful context is the ProductName, the Question, and the Answer. Those three fields help the model understand both the subject domain and the actual Q&A pair. By contrast, FaqId is just a technical identifier and generally adds no semantic value for response generation, so including it wastes tokens.

That is why Option A is better than the others:

Option A keeps the meaningful text fields and removes the low-value identifier.

Option B is too minimal because it includes only the answer text as Prompt, which strips away the product and question context the LLM may need for accurate grounding.

Option C keeps FaqId but omits ProductName, which can be important disambiguating context.

Option D includes everything, but that does not minimize token usage because it keeps the unnecessary FaqId.

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.