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NEW QUESTION # 28
You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB to meet the following requirements:
Create a vector index on the appropriate column.
Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer : =
* Vector index column # embedding
* Vector search function # VECTOR_SEARCH
* Final clause # ORDER BY s.distance
The first selection is embedding because CREATE VECTOR INDEX must target the column that stores vector data. Microsoft documents the syntax as CREATE VECTOR INDEX ... ON object (vector_column) , so the index belongs on dbo.Products.embedding , not on distance or product_name .
The second selection is VECTOR_SEARCH . This function is designed for vector similarity retrieval and works with vector indexes in SQL database in Microsoft Fabric . In the shown code, the supplied
@query_vector VECTOR(1536) is compared against the embedding column using cosine distance, and TOP_N = 10 requests the ten nearest matches. Microsoft specifically documents VECTOR_SEARCH for approximate nearest-neighbor searches and its integration with vector indexes.
The final clause is ORDER BY s.distance because VECTOR_SEARCH returns a distance value representing how far each stored vector is from the supplied query vector. With cosine distance, smaller distance means greater similarity , so ascending order returns the most relevant products first.
Therefore, the completed selections are:
CREATE VECTOR INDEX idx_products_embedding
ON dbo.Products (embedding)
WITH (METRIC = ' cosine ' , TYPE = ' DiskANN ' );
FROM VECTOR_SEARCH (
TABLE = dbo.Products AS t,
COLUMN = embedding,
SIMILAR_TO = @query_vector,
METRIC = ' cosine ' ,
TOP_N = 10
) AS s
ORDER BY s.distance;
So the verified hotspot answers are embedding # VECTOR_SEARCH # ORDER BY s.distance .
NEW QUESTION # 29
You have a GitHub Codespaces environment that has GitHub Copilot Chat installed and is connected to a SQL database in Microsoft Fabric named DB1. DB1 contains tables named Sales.Orders and Sales.Customers.
You use GitHub Copilot Chat in the context of DB1.
A company policy prohibits sharing customer Personally Identifiable Information (PII), secrets, and query result sets with any AI service.
You need to use GitHub Copilot Chat to write and review Transact-SQL code for a new stored procedure that will join Sales.Orders to Sales.Customers and return customer names and email addresses. The solution must NOT share the actual data in the tables with GitHub Copilot Chat.
What should you do?
Answer: A
Explanation:
To use GitHub Copilot Chat effectively in this environment without exposing sensitive data, you should focus your prompts entirely on the schema and logic rather than the data itself.
Since Copilot Chat can "see" your open files, the best approach is to provide the table structures as DDL (Data Definition Language) statements or a simplified description.
Steps to generate the stored procedure:
Define the Schema: Open a new SQL file in your Codespace. Paste the CREATE TABLE scripts (without any data) for your two tables.
Prompt Copilot: Use the Chat view to request the procedure.
Example Prompt: "Based on the table definitions in my open file, write a T-SQL stored procedure that joins TableA and TableB on [Join Column]. Include logic to filter by [Parameter] and ensure no PII columns are included in the SELECT statement." Review for PII/Secrets: Before executing, manually verify that the generated code doesn't include hardcoded secrets or call PII columns you intended to omit.
Security Check: Because you are in a Codespace, ensure your .env files or connection strings are in your .gitignore so they aren't indexed by Copilot.
Reference:
https://github.com/orgs/community/discussions/141924
NEW QUESTION # 30
You have an Azure SQL database that contains a table named Rooms. Roomswas created by using the following Transact-SQL statement.
You discover that some records in the Rooms table contain NULL values for the Owner field.
You need to ensure that all future records have a value for the Owner field.
What should you add?
Answer: A
Explanation:
A CHECK constraint is one way to do it.
If you use a CHECK constraint (e.g., CHECK (ColumnName IS NOT NULL)), the database will indeed reject new NULL entries. However, the column's metadata will still technically allow NULLs, which can sometimes affect how external tools or APIs interact with your schema.
Reference:
https://www.postgresql.org/docs/7.0/sql-createtable.htm
NEW QUESTION # 31
You have an Azure SQL database that supports a customer-facing API. The API calls a stored procedure named dbo.GetCustomerOrders thousands of times per hour.
After a deployment that updated indexes and statistics, users report that the API endpoint backed by dbo.
Getcustomerorders is slower. In Query Store, the same query now has two persisted execution plans. During the last hour, the newer plan had a significantly higher average duration and CPU time than the older plan.
You need to restore the previous performance quickly, without changing the API code.
Which Transact-SQL command should you run?
Answer: B
Explanation:
The scenario says Query Store already shows two persisted execution plans for the same query, and the older plan performed much better than the newer one during the last hour. Microsoft documents that sp_query_store_force_plan is used to force a particular plan for a particular query in Query Store .
That makes it the fastest way to restore the previously good plan without changing application code , which is exactly what the question requires.
Why the other options are not the best fit:
* sp_query_store_set_hints is for adding or updating Query Store hints to influence compilation behavior, but when you already know the exact older good plan, Microsoft points to plan forcing as the direct remedy.
* DBCC FREEPROCCACHE clears cached plans broadly and is disruptive; it does not guarantee a return to the known good plan.
* ALTER DATABASE is too general and does not directly restore the prior execution plan.
So the right Transact-SQL command is:
EXEC sp_query_store_force_plan
using the relevant @query_id and @plan_id from Query Store for the older, better-performing plan.
Microsoft also notes that when a plan is forced, SQL Server tries to use that plan whenever it encounters the query again.
NEW QUESTION # 32
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 # 33
......
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