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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Available Languages: | Spanish, Portuguese (Brazil), German, Chinese (Simplified), English, Japanese, Korean, French |
| Passing Score: | 700 (on a scale of 1-1000) |
| Exam Price: | $165 USD |
| Exam Format: | Multiple select, Drag and drop, Case studies, Multiple choice, Active screen |
| Recommended Training: | Microsoft Learn DP-800 Learning Path |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: T-SQL development, SQL Server/Azure SQL, CI/CD practices, basic AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-800 |
>> Reliable DP-800 Exam Question <<
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NEW QUESTION # 67
You have an Azure SQL database named SalesDB that supports an AI-enabled product search application.
SalesDB has a table named SalesLT.Product that contains the following columns:
* ProductID (int)
* Name (nvarchar(100))
* ListPrice (decimal(18,2))
You need to create an object that returns products priced above a caller-provided threshold. The solution must meet the following requirements:
* Support being used in a join.
* Accept one input parameter.
* Return a table result.
Answer:
Explanation:
Explanation:
NEW QUESTION # 68
You have an Azure SQL database named DB1 that contains two tables named knowledgebase and query_cache. knowledge_base contains support articles and embeddings. query_cache contains chat questions, responses, and embeddings DB1 supports an Al-enabled chat agent.
You need to design a solution that meets the following requirements:
* Serializes the retrieved rows from knowledee_base
* Extracts the answer field from the response
* Extracts the embeddings to store in query_cache
You will call the external large language model (LLM) by using the sp_irwoke_external_re standpoint stored procedure.
Which Transact-SGL commands should you use for each requirement? To answer, drag the appropriate commands to the correct requirements. Each command may be used once, mote than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct mapping is:
* FOR JSON PATH
* JSON_VALUE
* JSON_QUERY
To serialize the retrieved rows from knowledge_base , the correct command is FOR JSON PATH .
Microsoft documents that FOR JSON formats query results as JSON, and PATH mode is the standard way to shape relational rows into JSON for downstream application or AI use.
To extract the answer field from the response , the correct command is JSON_VALUE because answer is a single scalar field . Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.
To extract the embeddings to store in query_cache , the correct command is JSON_QUERY because embeddings are returned as a JSON array , not a scalar. Microsoft states that JSON_QUERY extracts an object or array from JSON text, which is exactly the right behavior for an embeddings payload.
The unused options are not the best fit here:
* OPENJSON is mainly for shredding JSON into rows and columns.
* AI_GENERATE_CHUNKS is for chunking text, not extracting fields from a response payload.
* VECTOR_DISTANCE computes similarity between vectors and is unrelated to JSON extraction.
* FOR XML PATH produces XML, not JSON.
NEW QUESTION # 69
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?
Answer: B
NEW QUESTION # 70
Drag and Drop Question
You have an Azure SQL database that supports an AI-driven product search API.
You need to identify the top CPU-consuming queries from the last two hours by using Query Store data. The solution must aggregate CPU consumption across executions and return only the top 15 query hashes.
How should you complete the Transact-SQL code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 71
Hotspot Question
You have an SDK-style SQL database project named MyDatabaseProject.sqlprojstored in a private GitHub repository. The repository contains the following GitHub Actions workflow.
The repository contains the AZURE_SQL_CONNECTION_STRING secrets.
The target is an Azure SQL database that allows access to Azure services and is configured to support mixed authentication.
The workflow runs successfully.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 72
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