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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 3: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 4: Connect to and consume Azure services | - Integrate Azure services
|
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NEW QUESTION # 121
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 # 122
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 # 123
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.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Delete keys on mutation. Implement an event-driven or database-hook system to purge related Redis keys the moment the source data updates.
Use absolute expiration. Set a strict, un-extendable Time-To-Live (TTL) of 10 minutes when writing to the cache, ensuring the data naturally expires even if a deletion event is missed.
Incorrect:
[Not C]
Implementing a sliding expiration is not a good step for this specific architecture because it violates the requirement that entries must not be returned if the underlying data changes.
Reference:
https://medium.com/real-world-net/net-caching-strategies-compared-redis-vs-memory-vs-hybrid-b8b8be1e708d
NEW QUESTION # 124
You have two Azure Container Registry (ACR) instances: ACR01 and ACR02.
You plan to implement a containerized application named APP1 that will use a base image named BASE1.
The image for APP1 will be stored in ACR01. The image BASE1 will be stored in ACR02.
You need to automate the planned implementation by using a sequence of five Azure command-line interface (Azure CLI) commands. Your solution must ensure that the APP1 image stored in ACR01 will be automatically updated when the BASE1 image is updated.
In which order should you arrange the commands to develop the solution? To answer, move all commands from the list of commands to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
NEW QUESTION # 125
You are developing an AI application. The application configuration will depend on a dynamically retrieved value of a designated key stored in an Azure App Configuration resource.
You must deploy the application to the test, staging, and production environments.
You need to be able to set the value differently in each environment.
Which feature of Azure App Configuration resource should you use?
Answer: B
Explanation:
You should use the Label feature of the Azure App Configuration resource.
In Azure App Configuration, keys can optionally include a label, which acts as a dimension or tag to create distinct variants of the same key. This allows you to store environment-specific values (such as test, staging, and production) under a single key name and load the appropriate configuration dynamically based on the current deployment environment.
References:
https://learn.microsoft.com/en-us/answers/questions/2108879/azure-app-configuration-resource-manager-api-is-de
NEW QUESTION # 126
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