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| Section | Objectives |
|---|---|
| Topic 1: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 2: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 60
You optimize an AI inference API that uses Redis caching.
You must reduce the risk of serving outdated data while minimizing cache management overhead.
You need to implement the caching strategy that satisfies the requirements.
What should you do?
Answer: B
Explanation:
Trigger invalidation when source data changes is the best action.
Data Freshness: This is a proactive cache-aside or write-through approach. By explicitly deleting or updating the specific Redis key when the underlying database or model data changes, you completely eliminate the risk of serving stale data.
Low Overhead: It keeps cache management overhead minimal because invalidation logic is execution-dependent (event-driven). Instead of constantly updating expirations or relying on heavy computing logic, you only trigger an action when a write/change event occurs.
Incorrect:
[not B]
Set eviction policy to allkeys-lru:
This policy is a memory management feature, not a data-freshness strategy. It only deletes the Least Recently Used (LRU) keys when your Redis instance runs out of RAM. If memory usage is stable, outdated data will sit in the cache forever, posing a major risk of serving stale inferences.
[not D]
Reset expiration on each read (Sliding TTL):
This actively increases the risk of serving outdated data. If a specific AI response is highly popular and read continuously, its expiration window will extend indefinitely. The data remains stuck in the cache even if the source data undergoes critical updates.
Reference:
https://oneuptime.com/blog/post/2026-01-30-llm-caching-strategies/view
NEW QUESTION # 61
You configure ACR Tasks to automate image builds.
Container images must rebuild when:
- Application updates occur.
- Base image updates occur, such as when the underlying OS image is
updated.
- Regular scheduled rebuilds are required.
You need to configure ACR Tasks to support automated image rebuilds.
Which three triggers should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B,E
NEW QUESTION # 62
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:
Box 1: host.json
To configure dynamic concurrency for the app, use the host.json configuration file.
host.json: Global metadata file where you enable the app-level dynamic concurrency setting ("dynamicConcurrencyEnabled": true) under the appropriate extension/extension lock configuration.
Box 2: f3
Apply the host.json file to the Azure Queue trigger function (f3), as dynamic concurrency is natively supported and managed per extension for runtime-driven storage and queue triggers rather than HTTP or timer triggers.
Azure Queue trigger function: Dynamic concurrency in Azure Functions specifically targets runtime-driven background bindings like Azure Queue, Blob, and Service Bus storage triggers, allowing individual management and automatic adjustments of processing limits per function.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-concurrency
NEW QUESTION # 63
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.
Answer:
Explanation:
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
NEW QUESTION # 64
You need to configure a connection string for the partner-facing service according to the technical requirements.
What should you use?
Answer: C
Explanation:
Detailed Explanation: Azure Key Vault references in App Service settings satisfy the requirement to keep secrets out of container images, source control, and directly stored application configuration. App Service resolves the referenced secret at runtime by using the app identity, so the application can consume the value as a normal setting without embedding credentials in the image. GitHub secrets are build/deployment secrets rather than a runtime App Service secret-delivery mechanism. Dockerfile ENV instructions would place secret material in the image configuration and violate the case requirements.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Use Key Vault references for App Service and Functions | Managed identities for Azure resources
NEW QUESTION # 65
......
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