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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 126
Hotspot Question
You are developing an application that uses a Python API to perform similarity queries against Azure Database for PostgreSQL. The application creates a new database connection for every request.
During peak traffic, the application intermittently fails to open new database sessions. Logs indicate that the maximum number of connections have been reached.
You need to configure the connection pooling strategy to reduce connection setup overhead and maximize reuse for the high-concurrency workload.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: PgBouncer
To resolve the connection exhaustion issue, you should use the built-in PgBouncer connection pooler configured in Transaction Pooling mode.
Because PostgreSQL uses a process-per-connection architecture, opening and closing a new session for every API request causes massive CPU/memory overhead and rapidly drains available slots during traffic spikes Box 2: Transaction Pooling mode Reference:
https://learn.microsoft.com/en-us/azure/postgresql/connectivity/concepts-connection-pooling-best-practices
NEW QUESTION # 127
Drag and Drop Question
You are developing a new page for a website that uses Azure Cosmos DB for data storage. The feature uses documents that have the following format:
You must display data for the new page in a specific order. You create the following query for the page:
You need to configure an Azure Cosmos DB policy to support the query.
How should you configure the policy? To answer, drag the appropriate JSON segments to the correct locations. Each JSON segment 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:
Box 1: compositeIndexes
Azure Cosmos DB requires a defined composite index for any query utilizing an ORDER BY clause with multiple properties.
Box 2: descending
In an ORDER BY clause, properties without an explicit direction default to ascending order. Your query ORDER BY p.name, p.city DESC translates to an ascending sort on /name and a descending sort on /city.
Reference:
https://docs.azure.cn/en-us/cosmos-db/index-policy
NEW QUESTION # 128
Hotspot Question
You have an Azure Functions app using the Consumption hosting plan for a company. The app contains the following functions:
You plan to enable dynamic concurrency on the app. The company requires that each function has its concurrency level managed separately.
You need to configure the app for dynamic concurrency.
Which file or function names should you use? To answer, select the appropriate values in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* File name: host.json
* Function name: f3
Azure Functions dynamic concurrency is enabled at the function-app host level in the host.json file . The concurrency configuration section contains the dynamicConcurrencyEnabled property. Setting this value to true enables the Functions host to dynamically learn and adjust concurrency levels for supported triggers rather than requiring fixed manual limits.
Although dynamic concurrency is enabled globally, Microsoft explicitly states that the learned concurrency level is managed independently for each individual function . This allows a resource-intensive function to operate at a lower concurrency level while a lightweight function in the same app can execute with greater concurrency, protecting host health while maximizing throughput.
Of the functions listed, f3 , which uses an Azure Queue Storage trigger , supports dynamic concurrency.
Microsoft currently documents dynamic concurrency support for Azure Queue Storage, Azure Blob Storage, and Azure Service Bus triggers , subject to the required extension versions. HTTP and Timer triggers do not participate in this dynamic-concurrency model.
Therefore, configure dynamic concurrency in host.json , and f3 is the function whose concurrency will be dynamically managed.
Study Guide references: Azure Functions # Concurrency; Dynamic concurrency; host.json; Queue Storage trigger concurrency.
NEW QUESTION # 129
You are developing an application that must extract structured field data (invoice number, total, vendor name) from scanned invoices in multiple layouts.
You need a solution that requires no custom model training.
What should you use?
Answer: C
Explanation:
Use the Azure AI Document Intelligence prebuilt invoice model . The prebuilt invoice model is specifically designed to analyze invoices and extract structured fields and line items without requiring an organization to train a custom model. Microsoft documents support for fields including InvoiceId , VendorName , InvoiceTotal , InvoiceDate, CustomerName, AmountDue, tax information, addresses, and invoice line items.
Because the model is pretrained to recognize common invoice structures, it can process invoices that use different layouts and formats while returning normalized structured output. This directly satisfies the requirement to extract invoice number, total, and vendor information from scanned documents while minimizing development and training effort.
The Azure AI Vision Read API performs OCR and can extract printed or handwritten text, but it does not provide invoice-specific semantic field extraction. Custom named entity recognition would require training and is designed for text entity extraction rather than document-layout understanding. A Document Intelligence custom neural model can handle organization-specific forms and layouts, but it requires custom model training, directly violating the requirement.
Therefore, the prebuilt invoice model provides the required combination of OCR, document understanding, structured extraction, and zero custom training.
Study Guide references: Azure AI Document Intelligence # prebuilt invoice model; structured field extraction; OCR; invoice fields and line items.
NEW QUESTION # 130
You are developing an AI search API that caches semantic search results in Redis.
Search results must remain cached for 10 minutes. If the underlying data changes, cached entries must NOT be returned.
You need to implement a cache aside strategy to ensure data consistency.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Detailed Explanation: The cache-aside design needs both bounded lifetime and explicit invalidation. A ten- minute TTL ensures cached search results do not persist indefinitely, while deleting related keys when source data changes prevents known-stale entries from being returned during that ten-minute window. Sliding expiration would extend lifetime based on access and can preserve stale data. Keyspace notifications report Redis key events; they do not replace application logic that invalidates entries when the authoritative source changes.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Azure Managed Redis documentation
NEW QUESTION # 131
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