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| Section | Objectives |
|---|---|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 72
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 # 73
You are developing a microservices-based application that uses Azure Container Apps The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
You must secure the container apps. All apps must reside in the same virtual network, share the same Dapr configuration, and share the same logging location.
Apps must support the configuration of the amount of memory and compute resources available to containers.
You need to configure the Azure Container App
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: `az containerapp env create ... --enable-workload-profiles`.
Detailed Explanation: The shared boundary for Container Apps is the managed environment. Apps in one environment can share the environment's virtual network integration, observability destination, and Dapr- related environment configuration. Enabling workload profiles provides selectable compute profiles and resource sizing options for workloads in that environment. The CLI must therefore create a Container Apps environment and enable workload profiles. Creating an individual container app alone would not establish the common environment-level network, logging, and resource-profile boundary required by the scenario.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Azure Container Apps environments
NEW QUESTION # 74
You deploy a GPT-4o model in Azure AI Foundry. Users report inconsistent completions for the same prompt. You need to make outputs more deterministic without changing the prompt. What should you do?
Answer: C
Explanation:
Temperature controls randomness in token selection. Setting it near 0 makes the model consistently choose the highest-probability next token, producing more deterministic and repeatable output for identical prompts.
NEW QUESTION # 75
A RAG application's retrieval step is returning documents that are topically related but not precisely relevant to the user's question. You need to improve retrieval precision without retraining any model. What should you do?
Answer: A
Explanation:
Azure AI Search's semantic ranker re-scores the initial retrieval results using a secondary language-understanding model, improving precision by pushing more contextually relevant results to the top without any custom model training.
NEW QUESTION # 76
Drag and Drop Question
You are building a back-end pipeline that receives AI inference requests.
The pipeline must perform the following actions:
- Publish a message so that multiple independent consumers receive it.
- Process messages in first-in, first-out (FIFO) order.
- Isolate any failed messages.
You need to configure the appropriate Service Bus entities.
How should you configure the Service Bus entities? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: topic
You should use an Azure Service Bus Topic to publish the message.
In a pub/sub architecture, the topic acts as the single destination where the sender publishes the message.
Box 2: queue
You should use a dedicated FIFO queue for this purpose.
While a standard queue allows parallel processing that can disrupt the arrival order, a strict FIFO (First-In, First-Out) queue guarantees that your AI inference requests are processed in the exact order they are received.
Order Preservation: Standard message queues only guarantee "best-effort" ordering. FIFO queues ensure strict sequential processing.
Exactly-Once Processing: FIFO queues inherently prevent duplicate message delivery. This prevents running the same costly AI inference twice.
Concurrency Control: You can limit or throttle consumers. This prevents overwhelming your AI models or GPUs.
Box 3: dead-letter queue
A dead-letter queue (DLQ) is the exact industry-standard pattern and correct architectural choice to isolate failed messages in an AI inference pipeline. It captures messages that fail processing repeatedly due to errors like model timeouts, malformed input payloads, or downstream GPU memory crashes.
Prevents Blockages: Stops bad requests from clogging the main queue and halting pipeline throughput.
Enables Debugging: Preserves the exact failed payload and error metadata for post-mortem analysis.
Protects Resources: Keeps infrastructure from getting stuck in endless retry loops on broken data.
Reference:
https://wearenotch.com/blog/azure-service-bus-tips-to-optimize-functions/
NEW QUESTION # 77
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