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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Exam Price: | $165 USD |
| Available Languages: | Spanish, Italian, Arabic (Saudi Arabia), Chinese (Simplified), French, English, Japanese, Chinese (Traditional), Indonesian (Indonesia), Korean, Portuguese (Brazil), German |
| Passing Score: | 700 |
| Exam Format: | Build list, Case studies, Multiple choice, Multiple select, Drag and drop |
| Related Certifications: | Microsoft Certified: SQL AI Developer Associate |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year |
| Real Exam Qty: | 40-60 |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or in-person at a testing center |
| Pre Condition: | No specific prerequisites, but candidates should have experience with SQL and a basic understanding of Azure services. Familiarity with AI or machine learning concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-800/ |
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NEW QUESTION # 43
You have an Azure SQL database that contains a table named stores, stores contains a column named description and a vector column named embedding.
You need to implement a hybrid search query that meets the following requirements:
* Uses full-text search on description for the keyword portion
* Returns the top 20 results based on a combined score that uses a weighted formula of 60% vector distance and 40% full-text rank How should you configure the query components? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For the vector portion, the correct choice is VECTOR_DISTANCE and order by distance ascending . The requirement is to build a combined weighted formula using the actual vector distance. Microsoft documents that VECTOR_DISTANCE returns the exact distance between two vectors. Since lower distance means greater similarity, ascending distance is the right direction for ranking. VECTOR_SEARCH is for ANN retrieval, but this hotspot specifically asks for a weighted formula based on distance , so VECTOR_DISTANCE is the appropriate operator.
For the keyword portion, the correct choice is CONTAINSTABLE on description and return ranked matches . Microsoft documents that CONTAINSTABLE returns a RANK column from 0 through 1000 , which is exactly what is needed for weighted scoring in a hybrid formula.
For the final ranking expression, the best choice is order by (distance * 0.6) + ((1.0 - RANK/1000.0) * 0.4) .
This works because vector distance is a lower-is-better metric, while full-text RANK is a higher-is-better metric. Dividing RANK by 1000 normalizes it to the documented range, and subtracting from 1.0 converts it into a lower-is-better term so both components can be combined consistently in one ascending score. This final step is a sound inference based on Microsoft's documented distance semantics and full-text rank range.
NEW QUESTION # 44
You have an Azure AI Search service and an index named hotels that includes a vector field named DescriptionVector.
You query hotels by using the Search Documents REST API.
You need to implement a hybrid search query that uses DescriptionVector and includes captions.
How should you complete the REST request body? To answer, drag the appropriate values to the correct targets. Each value may be used once, more 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:
Verified Answer : =
* queryType # " semantic "
* semanticConfiguration # " hotels "
* captions # " extractive "
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The correct configuration is queryType: " semantic " , semanticConfiguration: " hotels " , and captions: " extractive " .
The request is already a hybrid search because it contains both a textual search value ( " ocean view " ) and a vectorQueries block targeting DescriptionVector . Microsoft documents that hybrid search executes full-text and vector searches together and merges their result sets. When semantic ranking is required, queryType must be set to " semantic " .
The semanticConfiguration value must reference the semantic configuration defined for the target index.
Since the index in this question is named hotels and the available answer choice is " hotels " , that is the appropriate configuration value in the supplied answer set. Microsoft's REST examples show that semantic queries require both queryType: " semantic " and a valid semanticConfiguration .
To return semantic captions, use " captions " : " extractive " . Microsoft states that extractive captions identify the most relevant passages from the indexed content and return them with each result. They are generated from existing document text rather than generated by an LLM.
The completed request body is therefore:
{
" search " : " ocean view " ,
" queryType " : " semantic " ,
" semanticConfiguration " : " hotels " ,
" captions " : " extractive " ,
" top " : 10,
" hybridSearch " : {
" maxTextRecallSize " : 50
},
" vectorQueries " : [
{
" kind " : " vector " ,
" vector " : [ /* embedding array */ ],
" fields " : " DescriptionVector " ,
" k " : 50
}
]
}
Final drag-and-drop selections:
* First target: " semantic "
* Second target: " hotels "
* Third target: " extractive "
NEW QUESTION # 45
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:
Explanation:
Verified Answer : =
* CPU aggregation expression # rs.avg_cpu_time
* Runtime interval source # sys.query_store_runtime_stats_interval
* Last two hours filter # DATEADD(HOUR, -2, GETUTCDATE())
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The first correct selection is rs.avg_cpu_time . Query Store stores CPU statistics per aggregation interval, and Microsoft documents avg_cpu_time as the average CPU time per execution, in microseconds . To calculate total CPU consumption across all executions, multiply count_executions by avg_cpu_time , then divide by
1000.0 to convert microseconds to milliseconds:
SUM(count_executions * rs.avg_cpu_time / 1000.0)
This is the exact pattern Microsoft uses in its documented query for identifying the top 15 CPU-consuming queries by query hash .
The second selection is sys.query_store_runtime_stats_interval because sys.query_store_runtime_stats.
runtime_stats_interval_id is a foreign key to this view. The interval view provides start_time and end_time , allowing Query Store data to be limited to the required time window.
The third selection is DATEADD(HOUR, -2, GETUTCDATE()) . Microsoft's own Azure SQL performance- monitoring example for the last two hours uses exactly:
rsi.start_time > = DATEADD(HOUR, -2, GETUTCDATE())
and then ranks by total CPU descending to return the top 15 query hashes.
Final drag-and-drop selections:
* First target: rs.avg_cpu_time
* Second target: sys.query_store_runtime_stats_interval
* Third target: DATEADD(HOUR, -2, GETUTCDATE())
NEW QUESTION # 46
What is the primary purpose of Azure AI in SQL development?
Answer: B
Explanation:
Azure AI integrates with SQL to enable intelligent insights like natural language queries, anomaly detection, and predictive analytics.
NEW QUESTION # 47
You have a SQL database in Microsoft Fabric that contains a column named Payload. Payload stores customer data in JSON documents that have the following format.
Data analysis shows that some customers have subaddressing in their email address, for example, user1+promo@contoso.com.
You need to return a normalized email value that removes the subaddressing, for example, user1
+promo@contoso.com must be normalized to user1@contoso.com.
Which Transact-SQL expression should you use?
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