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| Section | Weight | Objectives |
|---|---|---|
| Design and develop database solutions (35–40%) | 35-40% | - Design and implement relational database schemas
|
| Implement AI capabilities in database solutions (25–30%) | 25-30% | - Integrate Azure AI services
|
| Secure, optimize, and deploy database solutions (35–40%) | 35-40% | - Implement data platform resources
|
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NEW QUESTION # 94
You need to create a table in the database to store the telemetry data. You have the following Transact-SQL code.
Answer:
Explanation:
Explanation:
The first statement is No. The requirement says telemetry data must be stored in a partitioned table to provide predictable performance for ingestion and retention operations. However, the shown CREATE TABLE statement does not define a partition function or partition scheme, and the table is created with a regular clustered primary key on TelemetryId. Microsoft's partitioning guidance states that creating a partitioned table requires a partition function, a partition scheme, and creating the table or index on that partition scheme using a partitioning column. None of that appears in the code, so the table is not partitioned.
The second statement is Yes. The code creates a JSON index named JI_VehicleTelemetry_Location on LocationJson for these specific JSON paths: $.location.latitude, $.location.longitude, and $.location.accuracy.
That matches the requirement that those JSON properties must be filterable by using an index seek. Microsoft documents that JSON indexing is used to optimize filtering and sorting on JSON properties, and the index only helps for the properties included in the index definition.
The third statement is No. The JSON index is defined only for latitude, longitude, and accuracy. A query filtering on $.location.heading references a different path that is not included in the index definition, so that query would not use JI_VehicleTelemetry_Location for that predicate. JSON indexes are path-specific; they do not automatically cover unrelated properties in the same JSON document.
Topic 1, Contoso Case Study
Existing 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 FeedbackJson column has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the unmask permission.
Requirements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following;
* Al 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.
SELECT VehicleTd, Lastupdatedutc, EngineStatus, BatteryHealth FROM dbo.VehicleHealthSumary where fleetld - gFleetld ORDER BV LastUpdatedUtc DESC; You review the execution plan and discover that the plan shows a clustered index scan.
vehicleincidentReports often contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support Al-powered semantic search over incident reports.
Security 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 accuracy JSON 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 MaintenanceEvents table updates the corresponding value in the LastModif reduce column to the time of the change.
* Avoid recursive updates.
AI Search, Embedding's, and Vector indexing
The development learn 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 FeedbackJson column of the customer-Feedback table:
* 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.
NEW QUESTION # 95
You have an Azure SQL database that supports an AI-driven product search API.
You need to identify the top CPU-consuming queries from the last two hours by using Query Store data. The solution must aggregate CPU consumption across executions and return only the top 15 query hashes.
How should you complete the Transact-SQL code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer:
CPU aggregation expression # rs.avg_cpu_time
Runtime interval source # sys.query_store_runtime_stats_interval
Last two hours filter # DATEADD(HOUR, -2, GETUTCDATE())
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents: = The first correct selection is rs.avg_cpu_time. Query Store stores CPU statistics per aggregation interval, and Microsoft documents avg_cpu_time as the average CPU time per execution, in microseconds. To calculate total CPU consumption across all executions, multiply count_executions by avg_cpu_time, then divide by
1000.0 to convert microseconds to milliseconds:
SUM(count_executions * rs.avg_cpu_time / 1000.0)
This is the exact pattern Microsoft uses in its documented query for identifying the top 15 CPU-consuming queries by query hash.
The second selection is sys.query_store_runtime_stats_interval because sys.query_store_runtime_stats.
runtime_stats_interval_id is a foreign key to this view. The interval view provides start_time and end_time, allowing Query Store data to be limited to the required time window.
The third selection is DATEADD(HOUR, -2, GETUTCDATE()). Microsoft's own Azure SQL performance- monitoring example for the last two hours uses exactly:
rsi.start_time > = DATEADD(HOUR, -2, GETUTCDATE())
and then ranks by total CPU descending to return the top 15 query hashes.
Final drag-and-drop selections:
First target: rs.avg_cpu_time
Second target: sys.query_store_runtime_stats_interval
Third target: DATEADD(HOUR, -2, GETUTCDATE())
NEW QUESTION # 96
You have an Azure SQL database that contains a table named Table1. Table1 contains 25,000,000 rows of data and a datetime2 column named DateKey. The data in Table1 spans the years 2020 through 2021.
You need to partition the data in Table1 by year. The solution must minimize how long it takes to rebuild or reindex the table.
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:
* Partition range # RANGE RIGHT
* Boundary values # ' 2020-01-01 00:00:00 ' , ' 2021-01-01 00:00:00 '
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The correct configuration is to use RANGE RIGHT with boundary values at the start of each year :
CREATE PARTITION FUNCTION PartitionByYear (datetime2)
AS RANGE RIGHT
FOR VALUES (
' 2020-01-01 00:00:00 ' ,
' 2021-01-01 00:00:00 '
);
Microsoft documents that with RANGE RIGHT , each boundary value belongs to the partition on its right .
For date-based partitioning, this is the natural pattern because a boundary such as 2021-01-01 becomes the lower boundary of the 2021 partition.
With these boundaries, the resulting ranges are effectively:
* Partition 1: dates before 2020-01-01
* Partition 2: 2020-01-01 through before 2021-01-01
* Partition 3: 2021-01-01 and later
This cleanly separates the 2020 and 2021 data into year-aligned partitions. Microsoft specifically recommends RANGE RIGHT for date-based boundaries because the first day of a period remains in the same partition as the rest of that period.
The other boundary choices are incorrect or less appropriate:
* Using year-end timestamps with RANGE LEFT is more cumbersome and can be sensitive to datetime2 precision.
* Monthly boundaries would partition by month, not year.
* Including 2019 and a 2021-12-31 23:59:59 boundary creates unnecessary partitions and is not the cleanest year-based design.
Therefore:
* First dropdown: RANGE RIGHT
* Second dropdown: ' 2020-01-01 00:00:00 ' , ' 2021-01-01 00:00:00 '
NEW QUESTION # 97
You are creating a table that will store customer profiles.
You have the following Transact-SQL code.
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:
Explanation:
* The schema meets the security requirements for PII data. # Yes
* Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No
* The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users
, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.
NEW QUESTION # 98
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 # 99
......
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