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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Developing AI-Enabled Database Solutions (DP-800) |
| Exam Number: | DP-800 |
| Passing Score: | 700/1000 (typical Microsoft passing score; subject to confirmation) |
| Exam Format: | Multiple choice, Case studies |
| Certificate Validity Period: | 1 year (Microsoft role-based certifications require annual renewal) |
| Available Languages: | English |
| Recommended Training: | Microsoft Learn - Azure Data & AI Learning Paths |
| Exam Registration: | Microsoft Certification Dashboard |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | No formal prerequisites required, but familiarity with Azure data services and database concepts is recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/ |
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NEW QUESTION # 40
Hotspot Question
You have a SQL database in Microsoft Fabric that contains a table named dbo.Products.
dbo.Products contains product catalog data.
You need to create a stored procedure that performs hybrid search. The solution must meet the following requirements:
- Use approximate nearest neighbor (ANN) to retrieve the top 20
candidate products.
- Re-rank only the candidates that also match a full-text query.
- Generate the query embedding.
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:
NEW QUESTION # 41
Hotspot Question
You have an Azure SQL database that contains a table named dbo.SupportTickets.
dbo.SupportTickets contains a JSON column named Payload and a datetime column CreatedAt.
You need to generate a report for the last seven days that meets the following requirements:
- Returns exactly one row per customer per day
- For each customer and day, returns the earliest ticket
- Includes the customer ID stored in Payload
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 42
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 # 43
Case Study 1 - Contoso
Existing Environment
Azure Environment
Contoso has an Azure subscription in North Europe that contains the corporate infrastructure.
The current infrastructure contains a Microsoft SQL Server 2017 database. The database contains the following tables.
The FeedbackJsoncolumn has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the UNMASKpermission.
Problem Statements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following:
* AI workloads
* Vector search
* Modernized API access
* Retrieval Augmented Generation (RAG) pipelines
Sometimes the ingestion pipeline fails due to malformed JSON and duplicate payloads.
The engineers at Contoso report that the following dashboard query runs slowly.
You review the execution plan and discover that the plan shows a clustered index scan.
VehicleIncidentReportsoften contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Requirements
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support AI-powered semantic search over incident reports.
Security Requirements
Contoso identifies the following security requirements:
* Restrict the support staff from viewing Personally Identifiable Information (PII) data, which is full email addresses and phone numbers.
* Enforce row-level filtering so that analysts see only incidents for the fleets to which they are assigned. The analysts can be assigned to multiple fleets.
Database Performance and Requirements
Contoso identifies the following telemetry requirements:
* Telemetry data must be stored in a partitioned table.
* Telemetry data must provide predictable performance for ingestion and retention operations.
* latitude, longitude, and accuracyJSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
* Ensure that any changes to a row in the MaintenanceEventstable updates the corresponding value in the LastModifiedUtccolumn to the time of the change.
* Avoid recursive updates.
AI Search, Embeddings, and Vector Indexing
Contoso plans to implement semantic search over incident data to meet the following requirements:
* Embeddings must be stored in dedicated Azure SQL Database tables.
* Embeddings must be generated from rich natural language fields.
* Chunking must preserve semantic coherence.
* Hybrid search must combine the following:
- Vector similarity
- Keyword filtering or boosting
Development Requirements
The development team at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases.
Contoso identifies the following requirements for querying data in the FeedbackJsoncolumn of the CustomerFeedbacktable:
* Extract the customer feedback text from the JSON document.
* Filter rows where the JSON text contains a keyword.
* Calculate a fuzzy similarity score between the feedback text and a known issue description.
* Order the results by similarity score, with the highest score first.
Hotspot Question
You need to meet the development requirements for the FeedbackJson column.
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 44
You have a Microsoft SQL Server 2025 database that contains a table named products. products contains two columns named description and embedding. embedding is generated from description.
You have an application named App1. App1 has a feature that uses the following query.
VECTOR_DISTANCE('cosine', @query_vector, embedding)
Users report that the feature is slow during peak usage times.
You discover that during peak usage times, the semantic search latency increases and CPU utilization spikes.
You need to reduce latency and CPU utilization related to the App1 feature.
What should you do?
Answer: C
Explanation:
To optimize your Microsoft SQL Server 2025 environment, you should transition from a brute- force distance calculation to an Approximate Nearest Neighbor (ANN) search using a DiskANN- based vector index.
The Solution: Vector Indexing
The VECTOR_DISTANCE function performs a full table scan, calculating the distance for every single row. During peak times, this consumes massive CPU cycles. A vector index allows SQL Server to navigate a pre-built graph to find matches, reducing complexity from O(n) to O(log n).
Implementation Steps
1. Create the Vector Index
You must specify the distance metric during index creation. For cosine similarity, use COSINE.
CREATE VECTOR INDEX idx_product_embedding
ON products (embedding)
WITH (METRIC = 'COSINE');
2. Update the Search Query
Replace the WHERE or ORDER BY clause that uses VECTOR_DISTANCE with the VECTOR_SEARCH function.
SELECT TOP (10) description, embedding
FROM products
WHERE VECTOR_SEARCH(
embedding,
@query_vector,
'COSINE',
10 -- Number of neighbors to return
);
Why This Solves Your Problem
CPU Relief: Avoids calculating distances for millions of rows.
Lower Latency: DiskANN is designed for sub-second retrieval even on large datasets.
Peak Performance: By offloading the math to a specialized index structure, the engine remains responsive for other transactional queries.
Reference:
https://learn.microsoft.com/en-us/azure/postgresql/extensions/how-to-use-pgdiskann
NEW QUESTION # 45
......
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