Candidates who become Microsoft DP-800 certified demonstrate their worth in the Microsoft field. DP-800 certification is proof of their competence and skills. This is a highly sought after credential and it makes career advancement easier for the candidate. To become Microsoft DP-800 Certified, you must pass the Developing AI-Enabled Database Solutions (DP-800) Exam. For this task, you need actual and updated DP-800 Questions.
| Section | Weight | Objectives |
|---|---|---|
| Secure, optimize, and deploy database solutions | 35-40% | - Deploy and maintain solutions
|
| Design and develop database solutions | 35-40% | - Design database solutions
|
| Implement AI capabilities in database solutions | 25-30% | - Use AI-assisted development tools
|
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NEW QUESTION # 83
You need to design a generative Al solution that uses a Microsoft SOL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
* Ait ' grounded In the latest transactional and reference data stored in D61
* Do NOT require retraining or fine-tuning the language model when the data changes
* Can include citations or references to the source data used in the response Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern?
More than one answer choice may achieve the goal. Select the BEST answer
Answer: C
Explanation:
The best use case for RAG is answering user questions based on company-specific knowledge. Microsoft defines RAG as a pattern that augments a language model with a retrieval system that provides grounding data at inference time, which is exactly what you need when responses must be based on the latest transactional and reference data, must avoid retraining/fine-tuning, and should be able to include citations or references to source data.
The other options do not fit as well:
summarizing free-form user input does not inherently require retrieval from DB1, training a custom model contradicts the requirement to avoid retraining/fine-tuning, generating marketing slogans is a creative generation task, not a grounding-and-citation scenario. RAG is specifically strong when answers must come from your organization's own changing knowledge.
NEW QUESTION # 84
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 references 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.Azure.Master NuGet package to the project.
Does this meet the goal?
Answer: A
Explanation:
This does meet the goal. Microsoft documents that SDK-style SQL projects can add the master.dacpac database reference as a package reference , and for Azure SQL Database the correct package is the Azure- specific master DACPAC package. The Azure SQL system DACPACs are available through NuGet, and this is the recommended way to include the right system objects in the database model for dotnet build validation.
So for an SDK-style SQL database project that targets Azure SQL Database, adding Microsoft.SqlServer.
Dacpacs.Azure.Master is the correct fix for unresolved references to system objects.
NEW QUESTION # 85
You need to meet the development requirements for the FeedbackJson column How should you complete the Transact SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText
CONTAINS(FeedbackJson, @Keyword)
SimilarityScore
These three selections are the correct way to complete the query because they align exactly with the stated requirements for the FeedbackJson column.
First, to extract the customer feedback text from the JSON document , the correct expression is JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText . Microsoft documents that JSON_VALUE is used to extract a scalar value from JSON, while JSON_QUERY is used for returning an object or array .
Since $.text is the textual feedback string, JSON_VALUE is the correct function.
Second, to filter rows where the JSON text contains a keyword , the best choice is CONTAINS (FeedbackJson, @Keyword) . The scenario explicitly states that FeedbackJson already has a full-text index
, and Microsoft documents that CONTAINS is the full-text predicate used in the WHERE clause to search full-text indexed character data. That makes it more appropriate than using EDIT_DISTANCE for keyword filtering.
Third, to order the results by similarity score, highest first , the correct item is SimilarityScore in the ORDER BY clause, which would be paired with DESC in the query. This matches the requirement to sort by the computed fuzzy similarity value. The DP-800 study guide specifically includes writing queries that use fuzzy string matching functions such as EDIT_DISTANCE, which supports the earlier computed SimilarityScore expression in the query.
NEW QUESTION # 86
You have an Azure SQL database that contains a column named Notes.
A security review discovers that Notes contains sensitive data.
You need to ensure that the data is protected so that neither the stored values nor the query inputs reveal information about the actual data. The solution must prevent a user from inferring relationships or repetitions in the data based on the encrypted output Which should you use?
Answer: B
Explanation:
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.
NEW QUESTION # 87
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.
Answer: B,E
Explanation:
Azure Functions' Azure SQL trigger requires change tracking to be enabled on the source table. Microsoft's SQL trigger documentation states that setting up change tracking for the Azure SQL trigger requires two steps: enable change tracking on the database and enable change tracking on the table being monitored. Since the question asks specifically which database configurations you should perform, enabling change tracking on dbo.Employee is one of the required database-side steps.
To meet the latency requirement that changes trigger the function within five seconds, the relevant trigger setting is Sql_Trigger_PollingIntervalMs. Microsoft documents this setting as the delay, in milliseconds, between processing each batch of changes, and a value of 5000 means the trigger polls every 5 seconds.
A few clarifications about the other options:
B is not the documented setting name. The documented app setting is Sql_Trigger_BatchSize or host setting MaxBatchSize, not "SetSql Trigger MaxBatchSize". The screenshot wording suggests a distractor.
D is also required in practice for the trigger to work, but the question asks for two answers and includes the polling setting plus the table-level CT setting as the actionable choices presented.
F is wrong because the Azure SQL trigger uses change tracking, not CDC.
NEW QUESTION # 88
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