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The Microsoft sector is an ever-evolving and rapidly growing industry that is crucial in shaping our lives today. With the growing demand for skilled Microsoft professionals, obtaining Developing AI-Enabled Database Solutions (DP-800) certification exam has become increasingly important for those who are looking to advance their careers and stay competitive in the job market.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, optimize, and deploy database solutions | 35-40% | - Secure database solutions
|
| Topic 2: Implement AI capabilities in database solutions | 25-30% | - Build intelligent search and retrieval
|
| Topic 3: Design and develop database solutions | 35-40% | - Design database solutions
|
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NEW QUESTION # 15
You have an Azure SQL database that stores sales data and contains tables named Sales and Products . Sales contains three columns named SalesDate , ProductKey , and TotalSale .
Sales is 10 TB and is loaded nightly by using a batch process. Most reporting queries scan large portions of Sales , filter on SalesDate or ProductKey , and use SUM() to aggregate TotalSale .
Products is relatively small and is used primarily for point lookups and joins to Sales .
You need to recommend which indexes to create to optimize the reporting queries. The solution must minimize storage requirements.
Which type of index should you recommend for each table? To answer, drag the appropriate index types to the correct tables. Each index type 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:
* Sales # A clustered columnstore index
* Products # A clustered rowstore index
For Sales , the correct choice is a clustered columnstore index . Microsoft identifies clustered columnstore indexes as the standard storage choice for large fact tables and analytical/data-warehouse workloads . The Sales table is 10 TB, loaded in nightly batches, and its reporting queries scan large portions of the table and perform aggregations such as SUM(TotalSale) . Those are exactly the workload characteristics that benefit from columnstore storage, batch-mode execution, aggregate pushdown, and high compression. Microsoft also notes that clustered columnstore indexes can provide substantial storage reduction compared with traditional uncompressed rowstore structures, which directly supports the requirement to minimize storage requirements .
For Products , the correct choice is a clustered rowstore index . Microsoft states that rowstore B-tree indexes perform best for point lookups, equality searches, and small-range retrieval , whereas columnstore is optimized for large analytical scans. Since Products is relatively small and primarily supports point lookups and joins to Sales , a rowstore structure is the better fit.
A nonclustered columnstore index would retain the underlying rowstore and add another compressed copy of selected columns, increasing storage. That is more appropriate for real-time analytics over an OLTP table, not for this dedicated large analytical fact table.
NEW QUESTION # 16
Hotspot Question
You have a database named db1. The schema is stored in a Git repository as an SDK-style SQL database project. The repository contains the following GitHub Action workflow.
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 # 17
You have an Azure SQL database that contains tables named dbo.Tickets and dbo.TicketNotes.
dbo.Tickets contains support tickets and dbo.TicketNotes contains ticket notes.
A retrieval query returns the top five relevant ticket notes for a user question.
You plan to implement a Retrieval Augmented Generation (RAG) pattern that meets the following requirements:
- Formats the retrieved relational data for large language model (LLM)
processing
- Sends the user question and retrieved context to an Azure OpenAI REST endpoint for chat completions
- Extracts the response text from the LLM response
Which Transact-SQL function should you use to extract the response text?
Answer: C
Explanation:
The Transact-SQL function used to extract the response text from the Azure OpenAI REST endpoint is JSON_VALUE.
When you call the Azure OpenAI REST endpoint using sp_invoke_external_rest_endpoint, the response is returned as a JSON string. To isolate the actual text content from the LLM, you must parse this JSON structure.
Function: JSON_VALUE(response_body, '$.choices[0].message.content')
Purpose: It extracts a scalar (text) value from a JSON string.
Path: In the OpenAI schema, the generated response is always located at
$.choices[0].message.content.
Reference:
https://pub.towardsai.net/mastering-retrieval-augmented-generation-from-zero-to-expert-in-rag- for-quickly-building-a-08141a308836
NEW QUESTION # 18
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:
We have encountered plan regression. This often happens after maintenance (like updating statistics) because the query optimizer generates a new execution plan that it thinks is better based on the new data distribution, but it ends up being less efficient in practice.
Since you've already identified the Plan ID for the faster plan and the Query ID from Query Store, you can force the "good" plan immediately using:
EXEC sp_query_store_force_plan @query_id = [YourQueryID], @plan_id = [YourFastPlanID]; Use code with caution.
This tells Azure SQL to ignore the new, slower plan and stick to the one that worked, providing an almost instant fix for your API's performance without requiring a code deployment.
Reference:
https://daxsws.com/blog/real-world-dynamics-365-performance-tuning-scenarios-and-fixes
NEW QUESTION # 19
Why is indexing important for vector search?
Answer: B
Explanation:
Specialized indexes (e.g., ANN) speed up nearest-neighbor searches.
NEW QUESTION # 20
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