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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search |
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問題 #99
Drag and Drop Question
You are implementing a Retrieval-Augmented Generation (RAG) system by using the native vector search capabilities of Azure Cosmos DB for NoSQL API.
You have a container named Documents that stores technical articles. Each article includes a property named embedding.
You must ensure that the system can perform efficient similarity searches between user queries and the stored articles.
You need to configure the database resources to support semantic retrieval.
Which configurations should you use? 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.
答案：
解題說明：
Explanation:
Box 1: Configure a vector index.
To support semantic retrieval and perform mathematical distance calculations using the native vector search capabilities of Azure Cosmos DB for NoSQL, you must configure a vector index.
Azure Cosmos DB for NoSQL relies on a specialized indexing structure (such as DiskANN or QuantizedFlat) to organize vector data. Defining a vector index policy within the container configurations is a strict requirement for enabling high-speed similarity searches (using functions like VectorDistance) without requiring full, unindexed scans of documents.
Box 2: Store data as a numeric array
Azure Cosmos DB for NoSQL, you must store the embedding property as a numeric array (an array of floating-point numbers like float32) inside your JSON documents.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-retrieval
問題 #100
You are developing a Retrieval-Augmented Generation (RAG) solution for a company. AI responses and embedding vectors are cached in Redis. The solution must meet the following requirements.
* AI responses must expire exactly 24 hours after they are cached
* Cached embeddings must always reflect the current source data.
You need to configure Redis to meet the requirements.
答案：
解題說明：
Explanation:
I response expiry: set a 24-hour TTL on each key. Embedding freshness: delete related cache keys when the source document changes.
Detailed Explanation: A fixed TTL encodes the requirement that an AI response must expire after a defined period rather than survive indefinitely or have its lifetime extended on every read. Embedding freshness is a separate consistency requirement: when the authoritative document changes, any cached representation derived from the old document must be invalidated immediately. Memory eviction policies are capacity- management mechanisms and cannot guarantee a precise 24-hour lifetime or source-data consistency.
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
問題 #101
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?
答案：C
解題說明：
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.
問題 #102
Hotspot Question
You are developing a solution that uses several Azure Service Bus queues. You create an Azure Event Grid subscription for the Azure Service Bus namespace. You use Azure Functions as subscribers to process the messages.
You need to emit events to Azure Event Grid from the queues. You must use the principle of least privilege and minimize costs.
Which Azure Service Bus values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: Premium
Azure Service Bus event integration with Event Grid is a specialized feature supported only on the Premium messaging tier. Basic and Standard tiers do not support emitting namespace events to Event Grid.
Box 2: Contributor
Setting up and managing the integration requires management-plane permissions on the namespace to establish the Event Grid subscription, making Contributor the correct and required IAM role specification according to Microsoft's integration guidelines.
Reference:
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-to-event-grid-integration-concept
問題 #103
You have an Azure Service Bus namespace that contains a topic named Topic1.
You plan to create a subscription named Sub1 to Topic1. In Sub1, you plan to filter messages from Topic1 based on their system properties and apply an action that will annotate each filtered message.
You need to configure the filtering.
How should you configure the filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Filter type: Use the SQL type.
Filtering action: Copy a message and annotate its metadata.
Use a SQL filter because Azure Service Bus SQL subscription filters can evaluate both system properties and application-defined properties . Microsoft documents that system properties are referenced using the sys. scope in SQL expressions, while custom properties can use the user. scope. This makes SQL filtering appropriate when the subscription must evaluate message system metadata.
More importantly, Service Bus supports a SQL rule action together with a SQL filter. After a message satisfies the filter, the rule action can add, replace, or remove message properties by using a SQL-like action expression. Microsoft explains that the action executes after the message matches the filter and before it is selected into the subscription .
A topic subscription does not modify the original message published to Topic1. Service Bus selects and copies the matching message into the subscription ' s virtual queue , and any annotation performed by the rule action is private to that subscription copy. Therefore, the accurate action description is copy a message and annotate its metadata , rather than modifying the original message.
The Boolean filter only represents always-true/false selection behavior, while Correlation filters provide efficient property matching but do not provide the SQL action behavior required here.
Study Guide references: Azure Service Bus # topic subscriptions; SQL filters; system properties; SQL rule actions; subscription message copies.
問題 #104
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