Exam DP-800 Topic 1 Question 9 Discussion
Actual exam question for Microsoft's DP-800 exam
Question #: 9
Topic #: 1
Question #: 9
Topic #: 1
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.
You need to recommend a solution to resolve the slow dashboard query issue. What should you recommend?
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.
You need to recommend a solution to resolve the slow dashboard query issue. What should you recommend?
Suggested Answer: B Vote an answer
Scenario:
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.
To optimize this query, you should create a covering nonclustered index that handles both the filtering (WHERE) and the sorting (ORDER BY) requirements while including the remaining columns to avoid expensive lookups.
Recommended Index Strategy
Creating an index with FleetID as the first key column and LastUpdateUtc as the second key column will allow SQL Server to perform an Index Seek to find the specific fleet and then retrieve those rows in the pre-sorted order required by your ORDER BY clause.
T-SQL Implementation:
CREATE NONCLUSTERED INDEX IX_VehicleHealth_Fleet_Update
ON dbo.VehicleHealthSummary (FleetID, LastUpdateUtc DESC)
INCLUDE (EngineStatus, BatteryHealth);
Use code with caution.
Why this works
Eliminates Clustered Index Scan: The current plan scans the entire table because no index exists that starts with FleetID. This new index allows the engine to jump directly to the relevant rows.
Avoids a Sort Operator: By including LastUpdateUtc DESC in the index key, the data is already physically ordered. SQL Server can read the index and return the results immediately without needing a costly in-memory sort.
Fully Covers the Query: Using the INCLUDE clause for EngineStatus and BatteryHealth ensures all data required by the SELECT statement is present in the index. This prevents "Key Lookups," where the engine would otherwise have to go back to the original table for those specific values.
Reference:
https://www.mssqltips.com/sqlservertip/8192/sql-server-uses-non-clustered-index-rather-than- clustered-index
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.
To optimize this query, you should create a covering nonclustered index that handles both the filtering (WHERE) and the sorting (ORDER BY) requirements while including the remaining columns to avoid expensive lookups.
Recommended Index Strategy
Creating an index with FleetID as the first key column and LastUpdateUtc as the second key column will allow SQL Server to perform an Index Seek to find the specific fleet and then retrieve those rows in the pre-sorted order required by your ORDER BY clause.
T-SQL Implementation:
CREATE NONCLUSTERED INDEX IX_VehicleHealth_Fleet_Update
ON dbo.VehicleHealthSummary (FleetID, LastUpdateUtc DESC)
INCLUDE (EngineStatus, BatteryHealth);
Use code with caution.
Why this works
Eliminates Clustered Index Scan: The current plan scans the entire table because no index exists that starts with FleetID. This new index allows the engine to jump directly to the relevant rows.
Avoids a Sort Operator: By including LastUpdateUtc DESC in the index key, the data is already physically ordered. SQL Server can read the index and return the results immediately without needing a costly in-memory sort.
Fully Covers the Query: Using the INCLUDE clause for EngineStatus and BatteryHealth ensures all data required by the SELECT statement is present in the index. This prevents "Key Lookups," where the engine would otherwise have to go back to the original table for those specific values.
Reference:
https://www.mssqltips.com/sqlservertip/8192/sql-server-uses-non-clustered-index-rather-than- clustered-index
by Berg at Jul 09, 2026, 09:24 AM
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