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| Section | Weight | Objectives |
|---|---|---|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
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NEW QUESTION # 53
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.
Answer:
Explanation:
Explanation:
* Facilitate mathematical distance calculations between data points: Configure a vector index.
* Define the document schema for high-dimensional data: Store data as a numeric array.
Azure Cosmos DB for NoSQL supports native vector search by storing embeddings directly in JSON documents and defining a vector embedding policy plus a vector index on the embedding path. Microsoft documents that vector search compares a query vector with stored vectors by calculating similarity or distance through the VectorDistance() system function. A vector index materially improves this process by reducing search latency, increasing throughput, and lowering RU consumption compared with an unindexed vector scan.
The embedding property itself must be stored as an array of numeric values . Microsoft examples show embedding properties such as " contentVector " : [2, -1, 4, ...] and define the associated vector policy with attributes including path, data type, dimensions, and distance function. This representation is required because embeddings are high-dimensional numerical vectors generated by an embedding model.
A composite index optimizes queries involving multiple scalar properties but does not provide vector- distance indexing. A Base64-encoded string cannot be used directly for native vector similarity calculations because Cosmos DB expects the vector field to contain numeric values matching the configured dimensionality.
Study Guide references: Azure Cosmos DB for NoSQL # vector embedding policies; vector indexes; VectorDistance(); numeric embedding arrays; native vector search.
NEW QUESTION # 54
A semantic search application queries Azure Database for PostgreSQL and stores document embeddings and metadata in a table with the following columns:
* embedding (pgvector)
* department
* created_at
The application must return the top five most similar documents for a given query embedding only from the finance department. You need to implement semantic retrieval with metadata filtering.
Which query components should you select? To answer, move the appropriate query components to the correct requirements. You may use each query component once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation:
Verified Answer: Filter: `WHERE department = ' finance ' `. Rank/top five: `ORDER BY embedding < = > query_embedding LIMIT 5`.
Detailed Explanation: The metadata predicate must restrict the candidate rows to the Finance department, so the `WHERE department = ' finance ' ` component supplies the required filter. The pgvector cosine-distance operator ` < = > ` orders rows by vector distance to the supplied query embedding, and `LIMIT 5` keeps only the five nearest matches. Ordering by creation date would rank recency rather than semantic similarity, and a wildcard department filter would not satisfy the Finance-only requirement.
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 | Vector similarity search with Azure PostgreSQL
NEW QUESTION # 55
A company uses Azure Monitor Application Insights to monitor application behavior, including incoming requests and dependencies.
You must identify failed requests from the last hour. You must also calculate the average duration of failed request dependency calls, grouped by operation name.
You need to analyze telemetry in Application insights.
Which operators should you mm? To answer, select the appropriate options in the answer area NOTE: Each cot reel selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 56
You are reviewing an Azure Function app that processes incoming order requests for a company. The function must:
* Accept order submissions from an external client application.
* Require controlled access for security
* Return a response containing the processed request payload.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: Yes; No; Yes; No.
Detailed Explanation: `auth_level=FUNCTION` means invocation requires a function key unless a stronger platform authentication layer is configured, so the first statement is true. The route explicitly permits POST, not GET. The function reads `req.get_body()` and returns that value in the HttpResponse, so the response contains the request body. No schema, type, required-field, or other payload validation is performed before the response is constructed, making the final statement false.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions HTTP trigger
NEW QUESTION # 57
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 ensure that events can be sent to Azure Event Grid from the queues. The solution must use the principle of least privilege and minimize costs.
Which Azure Service Bus role and tier level should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Tier: Premium. Role: Azure Service Bus Data Receiver.
Detailed Explanation: Microsoft documents Event Grid integration for Service Bus queues and topics as requiring a Service Bus Premium namespace. The Azure Function subscriber must then receive messages from the queue; the least-privilege built-in data-plane role for that purpose is Azure Service Bus Data Receiver. Data Sender does not permit receiving, and Data Owner is broader than necessary. Premium therefore satisfies the feature prerequisite, while Data Receiver satisfies the stated least-privilege requirement.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Service Bus to Event Grid integration | Authenticate Service Bus with Microsoft Entra ID
NEW QUESTION # 58
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