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NEW QUESTION # 27
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,F
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 # 28
Drag and Drop Question
You have an Azure SQL database named SalesDB. SalesDB contains a table named dbo.Articles. dbo.Articles contains the following columns:
- ArticleId
- Title
- Body
- LastModifiedUtc
- EmbeddingVector
You have an application that generates embeddings from the concatenation of Title and Body and stores the results in EmbeddingVector.
You plan to implement an incremental embedding maintenance method that will use change data capture (CDC) to update embeddings only for rows that change, without scanning the entire table.
You need to ensure that only the columns required to generate the embeddings are captured.
The solution must support querying net changes.
How should you complete the Transact-SQL script? 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:
NEW QUESTION # 29
You have an Azure Al Search service and an index named hotels that includes a vector Held named DescriptionVector.
You query hotels by using the Search Documents REST API.
You add semantic ranking to the hybrid search query and discover that some queries return fewer results than expected, and captions and answers are missing.
You need to complete the hybrid search request to meet the following requirements:
* Include more documents when ranking.
* Always include captions and answers.
Answer:
Explanation:
Explanation:
These are the correct selections for a hybrid query that uses semantic ranking in Azure AI Search.
Use k = 50 because Microsoft explicitly recommends that when you combine semantic ranking with vector queries , you should set k to 50 so the semantic ranker has enough candidates to rerank. If you use a smaller value such as 10, semantic ranking can receive too few inputs, which is exactly why some queries return fewer results than expected.
Use queryType = " semantic " because captions and answers are only available on semantic queries.
Microsoft documents that captions is valid only when the query type is semantic, and semantic answers are returned only for semantic queries.
Use captions = " extractive " because semantic captions are extractive passages pulled from the top-ranked documents. Microsoft's REST documentation states that the valid captions option here is extractive and that it defaults to none if not specified.
Use answers = " extractive " because semantic answers in Azure AI Search are extractive, not generated.
Microsoft documents that semantic answers are verbatim passages recognized as answers and the REST API lists extractive as the answer-return option.
NEW QUESTION # 30
You implement ProcedureDocuments to support the planned changes.
When users consume data through the Retrieval Augmented Generation (RAG) pattern, they experience data retrieval delays.
You need to improve the data retrieval performance and reduce the number of tokens per retrieval.
What should you implement?
Answer: D
Explanation:
A). Chunking is the correct answer.
Chunking divides large documents into smaller, semantically meaningful sections that can be independently embedded, indexed, and retrieved in a Retrieval Augmented Generation (RAG) architecture.
The ProcedureDocuments table contains a Content NVARCHAR(MAX) column and an Embedding VECTOR (1536) column. Storing entire documents as individual embedding units can introduce irrelevant contextual information and increase token consumption.
Technical breakdown:
* Document segmentation: Split lengthy medical procedure documents into smaller chunks while preserving their semantic meaning.
* Independent embeddings: Generate a separate vector embedding for each chunk to support more granular semantic similarity searches.
* Improved retrieval relevance: Retrieve only document sections relevant to the user ' s query instead of entire documents.
* Reduced token consumption: Send fewer irrelevant tokens to the LLM, reducing context-processing costs.
* Performance optimization: Smaller retrieved contexts can reduce downstream processing latency.
Appropriate vector indexing and chunk sizing are also necessary for efficient retrieval.
Microsoft ' s RAG guidance specifically confirms that smaller chunks help return relevant content while reducing token usage.
Why the other options are incorrect:
* B. Embeddings: Enable semantic similarity searches but do not independently reduce retrieved text size.
* C. Small language model (SLM): May reduce inference costs but does not directly optimize document retrieval.
* D. JSON content: Provides structured data representation without inherently improving retrieval performance or reducing tokens.
DP-800 Official Study Guide Reference:
Main topic: Implement AI capabilities in database solutions.
Subtopics: Design and implement models and embeddings and Design and implement intelligent search.
Specific examination objectives: Design and implement chunks for embeddings and evaluate performance of vector and hybrid search.
Official reference: Microsoft Learn - RAG Chunking Phase .
NEW QUESTION # 31
You have an Azure SQL database that supports an AI-enabled product catalog API. The database contains a table named Sales.Orders. Sales.Orders contains 20 million rows and has a nonclustered index on a column named CreateDate. The API passes a date parameter named @OrderDate. You have a stored procedure named get_latest_day_orders that filters by running the following query: WHERE CAST(CreateDate AS date) =
@OrderDate A review of the query execution reveals that the query causes an index scan. You need to modify the predicate to ensure that Microsoft SQL Server can use an index seek on CreateDate for a single day. Which WHERE clause should you use?
Answer: B
NEW QUESTION # 32
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