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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI capabilities in database solutions (25โ30%) | 25-30% | - Implement AI features
|
| Topic 2: Secure, optimize, and deploy database solutions (35โ40%) | 35-40% | - Implement data platform resources
|
| Topic 3: Design and develop database solutions (35โ40%) | 35-40% | - Implement data management
|
>> DP-800 Free Test Questions <<
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NEW QUESTION # 30
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEnbeddings and DescriptionEabeddings tables have been generated from values in the Description and notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables. The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft's SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.
The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.
NEW QUESTION # 31
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 # 32
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEnbeddings and DescriptionEabeddings tables have been generated from values in the Description and notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables. The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft's SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.
The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.
NEW QUESTION # 33
Hotspot Question
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEmbeddings and DescriptionEmbeddings tables have been generated from values in the Description and Notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables.
The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 34
Case Study 2 - Fabrikam
Existing Environment
Azure Environment
Fabrikam has a single Azure subscription in the East US 2 Azure region. The subscription contains an Azure SQL database named DB1. DB1 contains the following tables:
* Patients
* Employees
* Procedures
* Transactions
* UsefulPrompts
* ProcedureDocuments
You store a column master key as a secret in Azure Key Vault.
You have an on-premises application named TransactionProcessing that uses a hard-coded username and password in a connection string to access DB1.
Problem Statements
Users report that after executing a long-running stored procedure named sp_UpdateProcedureForPatient, updates to the underlying data are sometimes inconsistent.
Requirements
Planned Changes
Fabrikam plans to manage all changes to Azure SQL Database objects by using source control in GitHub. Every pull request submitted to production will be validated before it can be merged.
Deployments must use the Release configuration.
Security Requirements
Fabrikam identifies the following security requirements:
* The TransactionProcessing application must use a passwordless connection to DB1.
* The Employees table contains two columns named TaxID and Salary that must be encrypted at rest.
* Auditors must have a tamper-evident history of transactions with cryptographic proof of changes to the employee data.
Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
AI Search, Embeddings, and Vector Indexing
Fabrikam identifies the following AI-related requirements:
* Queries to the ProcedureDocuments table must use Reciprocal Rank Fusion (RRF).
* Users must be able to query the data in DB1 by using prompts in Copilot in Microsoft Fabric.
* The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
Development Requirements
Fabrikam identifies the following development requirements:
* Provide the functionality to retrieve all the transactions of a given patient between two dates, showing a running total.
* Expose a Data API builder (DAB) configuration file to enable Azure services to perform the following operations over a REST API:
- Read data from the procedures table without authentication.
- Read and insert data into the Transactions table once authenticated.
- Execute the sp_UpdateProcedurePatient stored procedure.
* Provide the functionality to retrieve a list of the names of patients who underwent medical procedures during the last 30 days.
* Information for each medical procedure will be stored in a table. The table will be used with a large language model (LLM) for user querying and will have the following structure.
DAB
You create a DAB configuration file that meets the development requirements for DB1 and includes the following entities.
You plan to implement changes to sp_UpdateProcedureForPatient to meet the performance requirements.
You change the stored procedure to run all its code within a transaction.
Which transaction level should you use?
Answer: C
Explanation:
Scenario: Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
To prevent other transactions from changing records while your stored procedure runs, you should use the Repeatable Read isolation level.
Repeatable ReadMechanism: Places shared locks on all data read by the query.
Prevention: Prevents other transactions from modifying or deleting the rows you have accessed.
Consistency: Ensures that if you read the same data twice within the procedure, the values remain identical.
Incorrect:
[Not C]
Serializable
Strictness: This is the highest level of isolation.
Phantom Protection: In addition to preventing changes to existing records, it prevents other transactions from inserting new records that would fall into the range of your query (phantom reads).
Usage: Use this if your procedure relies on aggregate counts or specific ranges that must remain absolutely static.
Key Considerations
Concurrency: Both levels reduce system performance because they hold locks longer, potentially causing other users to wait.
Deadlocks: Higher isolation levels increase the risk of deadlocks in busy databases.
Reference:
https://learn.microsoft.com/en-us/sql/connect/jdbc/understanding-isolation-levels
NEW QUESTION # 35
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