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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Passing Score: | 700 |
| Real Exam Qty: | 40-60 |
| Certificate Validity Period: | 1 year |
| Exam Format: | Build list, Multiple select, Drag and drop, Multiple choice, Case studies |
| Exam Duration: | 100 minutes |
| Exam Price: | $165 USD |
| Related Certifications: | Microsoft Certified: SQL AI Developer Associate |
| Available Languages: | Portuguese (Brazil), Chinese (Traditional), Korean, Chinese (Simplified), Japanese, English, German, Spanish, Arabic (Saudi Arabia), Indonesian (Indonesia), Italian, French |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or in-person at a testing center |
| Pre Condition: | No specific prerequisites, but candidates should have experience with SQL and a basic understanding of Azure services. Familiarity with AI or machine learning concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-800/ |
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| Topic | Details |
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NEW QUESTION # 86
You have an Azure SQL database that contains tables named dbo.Tickets and dbo.TicketNotes.
dbo.Tickets contains support tickets and dbo.TicketNotes contains ticket notes.
A retrieval query returns the top five relevant ticket notes for a user question.
You plan to implement a Retrieval Augmented Generation (RAG) pattern that meets the following requirements:
- Formats the retrieved relational data for large language model (LLM)
processing
- Sends the user question and retrieved context to an Azure OpenAI REST endpoint for chat completions
- Extracts the response text from the LLM response
Which Transact-SQL function should you use to extract the response text?
Answer: D
Explanation:
The Transact-SQL function used to extract the response text from the Azure OpenAI REST endpoint is JSON_VALUE.
When you call the Azure OpenAI REST endpoint using sp_invoke_external_rest_endpoint, the response is returned as a JSON string. To isolate the actual text content from the LLM, you must parse this JSON structure.
Function: JSON_VALUE(response_body, '$.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 # 87
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 # 88
You have an Azure SQL database that contains a table named Tickets_Embeddings.
Tickets_Embeddings contains a VECTOR(1536) column.
Embeddings are generated by using text-embedding-ada-002 and the /openai/v1/embeddings endpoint.
After a review of the current environment, the following changes are requested:
- The AI architect has recommended switching to text-embedding-3-small.
- The security team requires authentication by using Microsoft Entra
managed identities only. Storing API keys in an application is
prohibited.
Which two actions should you include in the solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
To migrate your Azure SQL database to use the text-embedding-3-small model while implementing Microsoft Entra managed identity authentication, you should take the following actions:
[B]
Regenerate all the embeddings: Even though text-embedding-ada-002 and the default configuration of text-embedding-3-small both use 1,536 dimensions, the underlying vector representations are mathematically incompatible. Comparing an "ada" vector to a "v3-small" vector using cosine similarity will yield incorrect results. You must re-embed your existing data using the new model to maintain search accuracy.
[C]
Change the endpoint: To switch models, you must update your application code or database configuration (such as an AI_GENERATE_EMBEDDINGS function call or a stored procedure) to point to the new Azure OpenAI deployment endpoint for text-embedding-3-small.
Why these actions are necessary:
Incompatibility: Vectors generated by different models exist in different latent spaces. While you do not need to Alter the database tables (since the VECTOR(1536) column type remains identical for both models), the actual data inside those columns must be replaced.
Managed Identity: To meet the requirement of using Microsoft Entra managed identities and prohibiting API keys, you must configure your application or the Azure SQL DB to authenticate with Azure OpenAI using a managed identity. This involves granting the identity the Cognitive Services OpenAI User role and updating your connection strings/calls to exclude the api-key header.
Reference:
https://microsoftlearning.github.io/mslearn-postgresql/Instructions/Labs/13-generate-vector- embeddings-azure-openai.html
NEW QUESTION # 89
You need to recommend a solution that will resolve the ingestion pipeline failure issues. Which two actions should you recommend? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
The two correct actions are D and E because the ingestion failures are caused by malformed JSON and duplicate payloads , and these two controls address those two problems directly. Microsoft's JSON documentation states that SQL Server and Azure SQL support validating JSON with ISJSON , and Microsoft specifically recommends using a CHECK constraint to ensure JSON text stored in a column is properly formatted.
For the duplicate-payload issue, creating a unique index on a hash of the payload is the appropriate design.
Microsoft documents using hashing functions such as HASHBYTES to hash column values, and SQL Server allows a deterministic computed column to be used as a key column in a UNIQUE constraint or unique index . That makes a persisted hash-based computed column plus a unique index a practical and exam- consistent way to reject duplicate payloads efficiently.
The other options do not solve the stated root causes:
* Snapshot isolation addresses concurrency behavior, not malformed JSON or duplicate payload detection.
* A trigger to rewrite malformed JSON is not the right integrity control and is brittle.
* Foreign key constraints enforce referential integrity, not JSON validity or duplicate-payload prevention
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 # 90
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 # 91
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