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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI capabilities in database solutions | 25-30% | - Integrate AI models and services
|
| Topic 2: Design and develop database solutions | 35-40% | - Develop database solutions
|
| Topic 3: Secure, optimize, and deploy database solutions | 35-40% | - Deploy and maintain solutions
|
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NEW QUESTION # 95
You need to enable similarity search to provide the analysts with the ability to retrieve the most relevant health summary reports. The solution must minimize latency.
What should you include in the solution?
Answer: C
NEW QUESTION # 96
You have a GitHub Enterprise subscription.
Your team is developing an Azure SQL dataset solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
A mix of GitHub Copilot instructions is configured at different levels, including organization-wide, repository- wide, agent-specific, and personal.
Based on the GitHub Copilot instruction precedence rules, which instructions will take precedence over the others?
Answer: D
NEW QUESTION # 97
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 # 98
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 # 99
You have a Microsoft SQL Server 2025 instance that contains a database named SalesDB SalesDB supports a Retrieval Augmented Generation (RAG) pattern for internal support tickets. The SQL Server instance runs without any outbound network connectivity.
You plan to generate embeddings inside the SQL Server instance and store them in a table for vector similarity queries.
You need to ensure that only a database user account named AlApplicationUser can run embedding generation by using the model.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
Explanation:
Because the SQL Server 2025 instance has no outbound network connectivity , the embedding model cannot rely on a remote REST endpoint such as Azure AI Foundry or Azure OpenAI. Microsoft's CREATE EXTERNAL MODEL documentation includes a local deployment pattern using ONNX Runtime running locally with local runtime/model paths . That is the right design when embeddings must be generated inside the SQL Server instance without external network access. Microsoft explicitly documents a local ONNX Runtime example for SQL Server 2025 and notes the required local runtime setup and model path configuration.
The permission requirement is handled by granting the application user access to use the external embeddings model. Microsoft's AI_GENERATE_EMBEDDINGS documentation states that, as a prerequisite, you must create an external model of type EMBEDDINGS that is accessible via the correct grants, roles, and/or permissions . Among the choices, the exam-appropriate action is to grant execute permission on the external model project to AlApplicationUser so only that database user can run embedding generation through the model.
NEW QUESTION # 100
......
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