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| Section | Objectives |
|---|---|
| Topic 1: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
| Topic 2: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 3: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement computer vision solutions - Implement knowledge mining with Azure AI Search |
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NEW QUESTION # 45
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You must reduce the risk of serving outdated data while minimizing cache management overhead.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: A
Explanation:
Detailed Explanation: If the priority is to minimize stale results, source-driven invalidation is the direct control: when the underlying record changes, delete the corresponding cache entry so the next request repopulates it from current data. Sliding expiration can keep a frequently accessed stale item alive, disabling expiration is worse, and an allkeys-LRU policy evicts according to memory pressure rather than data freshness. Invalidation therefore best addresses correctness without requiring constant cache-wide maintenance.
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
NEW QUESTION # 46
You need to configure the Redis integration for the Recommendation API.
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:
* Comply with the defined expiration policy for recommendation results: Configure Time to Live on each cache key.
* Support the data integrity and freshness requirement for catalog updates: Publish invalidation events to a Redis channel.
* Meet the search latency target for recurring product requests: Implement cache-aside pattern with lazy loading.
The 10-minute expiration requirement should be implemented by configuring a Time to Live (TTL) on each Redis cache key. Redis automatically removes a key after its assigned expiration interval, ensuring recommendation results cannot remain cached indefinitely. Microsoft documents expiration as a standard mechanism for preventing cached data from becoming stale.
For catalog changes, Fabrikam explicitly requires a reactive invalidation mechanism . Publishing invalidation events to a Redis Pub/Sub channel allows application components to notify subscribers immediately when product metadata or pricing changes. Redis Pub/Sub broadcasts messages to channel subscribers, which can then invalidate affected cache entries instead of waiting for TTL expiration.
For repeated recommendation queries, implement the cache-aside pattern with lazy loading . The application first checks Redis; on a cache hit, results are returned from the low-latency in-memory cache. On a miss, the application retrieves data from PostgreSQL, places the result in Redis, and returns it. Microsoft identifies cache-aside as an appropriate pattern for frequently read data and explicitly notes that Azure Managed Redis provides low-latency, high-throughput access.
Redis RDB persistence is unnecessary for these requirements because the cache is an optimization layer rather than the authoritative data store.
Study Guide references: Azure Managed Redis # cache-aside pattern; TTL/expiration; Pub/Sub; cache invalidation and data freshness.
NEW QUESTION # 47
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column.
The schema must support vector similarity search and reliable date filtering.
You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
NEW QUESTION # 48
An ACA app processes messages from an Azure Storage queue.
The app must scale automatically based on messages in a specific Azure Storage queue by using a Kubernetes Event-driven Autoscaler (KEDA) custom scale rule.
You need to configure the required scale rule values.
Which two values should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
[A]
QeueName: The exact name of the specific Azure Storage queue your app is polling is required.
[C]
Why Trigger Type is Required
When configuring a custom scale rule in Azure Container Apps (ACA), the platform utilizes KEDA underneath. Because a custom rule can connect to many different event sources (such as Kafka, Redis, or Azure Storage), you must explicitly define the Trigger type (e.g., azure-queue) so KEDA knows which specific scaler to initiate.
Reference:
https://techcommunity.microsoft.com/blog/fasttrackforazureblog/container-apps-a-practical-scaling-with-azure-queue-scale-rule/3722075
NEW QUESTION # 49
You are configuring an Azure-hosted AI application that uses Azure App Configuration to manage its runtime settings.
The application must provide the following functionality:
- Support percentage-based, targeted rollouts.
- Maximize token limits per request.
- Store a key that is retrieved securely at runtime.
- Reference a list of supported deployment regions.
You need to configure the App Configuration features for the app.
Which features should you configure? To answer, move the appropriate App Configuration features to the correct configuration items. You may use each App Configuration features 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:
* Support percentage-based, targeted rollouts: Feature flag
* Maximize token limits per request: Key-value
* Store a key that is retrieved securely at runtime: Key Vault reference
* Reference a list of supported deployment regions: Key-value
Azure App Configuration provides three distinct mechanisms for these scenarios. A Feature flag is the correct choice for percentage-based and targeted rollouts. Feature Management supports conditional activation based on filters such as percentage allocation, users, groups, and other targeting criteria, making it appropriate for gradual deployment and controlled experimentation.
A Key-value is the correct mechanism for ordinary runtime configuration data such as a model token limit or a list of supported deployment regions. These values are application settings rather than secrets or feature- state controls. The application can retrieve and refresh them dynamically without redeployment. A token-limit setting such as MaxTokensPerRequest and a region list such as SupportedRegions fit naturally into App Configuration key-value entries.
For sensitive data, use a Key Vault reference . App Configuration stores only the reference to the Azure Key Vault secret rather than the secret value itself. At runtime, the application resolves the secret securely, typically by using a managed identity. This avoids embedding credentials or sensitive keys in configuration values.
Therefore, the correct mapping is Feature flag # Key-value # Key Vault reference # Key-value .
Study Guide references: Azure App Configuration # Feature Management; key-values; Key Vault references; dynamic application configuration.
NEW QUESTION # 50
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