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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions |
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NEW QUESTION # 12
Drag and Drop Question
You are developing a solution for a hospital to support the following use cases:
- The most recent patient status details must be retrieved even if
multiple users in different locations have updated the patient record.
- Patient health monitoring data retrieved must be the current version
or the prior version.
- After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.
You provision an Azure Cosmos DB for NoSQL database and set the default consistency level for the database account to Strong.
You set the value for Indexing Mode to Consistent.
You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.
Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Strong
Strong consistency must be used at the query level to meet this requirement.
Linearizability Guarantee: Strong consistency ensures that a read operation always returns the most recent committed version of an item.
Global Consensus: Because multiple users in different locations are updating the patient record, any reader across any region must wait for the updates to be fully committed globally before reading. This prevents any user from reading stale or out-of-order data, which is critical for patient status details.
Box 2: Bounded Staleness
To minimize latency and maximize availability while ensuring that retrieved patient health monitoring data is either the current version or the immediate prior version, you should override the consistency level at the query level to Bounded Staleness.
Version Guarantee: Bounded Staleness ensures that reads are not older than a specific time window or a configured number of versions K versions behind the latest write). Setting the version staleness to 1 (K=1 )guarantees that the data read is either the current version or the prior version.
Lower Latency: Bounded Staleness offers significantly lower latency than the account's default Strong consistency. Strong consistency requires a global majority acknowledgment before a read or write completes, increasing latency.
Higher Availability: By backing down from Strong consistency to Bounded Staleness for this query, you reduce the strict synchronization requirements, thereby minimizing impacts on availability during localized network disruptions.
Box 3: Eventual
Eventual consistency is the correct consistency level to use at the query level for this scenario.
Maximizes Performance: Eventual consistency offers the lowest latency and highest availability among all consistency levels.
Matches Scenario Needs: The billing record is final and no longer changing, so there is no risk of reading stale, in-progress updates.
Optimizes Costs: Eventual consistency queries consume half the Request Units (RUs) compared to Strong or Bounded Staleness queries.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
NEW QUESTION # 13
Drag and Drop Question
You are deploying an AI service to ACA.
The service must retrieve secrets securely from Key Vault by using managed identity.
You need to configure secure access.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Grant the Key Vault Secrets User role to the managed identity
This uses Azure's modern role-based access control (RBAC) to give the application data-plane permission to read the secret values.
Step 2: Retrieve the secret by using the SDK
Once authorized, the application code securely pulls the secret using libraries like the Azure Key Vault secret client library (such as SecretClient alongside DefaultAzureCredential).
Step 3: Assign a system-assigned managed identity
Enabling a system-assigned identity provides the Azure Container App (ACA) with its own credentials managed automatically by Microsoft Entra ID, completely eliminating hardcoded credentials.
Reference:
https://learn.microsoft.com/en-us/azure/key-vault/general/tutorial-net-create-vault-azure-web-app
NEW QUESTION # 14
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.
Answer:
Explanation:
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
NEW QUESTION # 15
You are implementing the expiration requirement for AI-generated summaries in the Redis cache.
You need to purge the AI summaries five minutes after creation, regardless of how often they are accessed.
What should you do?
Answer: C
Explanation:
Configure a fixed expiration timeout on each Redis key when the summary is created , such as a five- minute TTL. Azure Managed Redis automatically removes a key when its configured timeout expires.
Critically, ordinary read operations do not extend that expiration period, so a summary created at time T will expire approximately five minutes later regardless of how frequently clients retrieve it.
This is the required absolute-expiration behavior. In Redis, applications can establish expiration when writing the value or by using expiration commands after creation. The important design property is that the expiration deadline is not refreshed whenever the key is read.
An allkeys-lru eviction policy addresses memory pressure , not deterministic five-minute expiration. It may evict less recently used keys when memory is constrained, but it does not guarantee removal exactly five minutes after creation. The PERSIST command is directly incorrect because it removes an existing timeout , potentially causing the summary to remain indefinitely. Sliding expiration is also unsuitable because it renews or resets the expiration window based on activity, allowing frequently accessed summaries to remain longer than five minutes.
Therefore, a per-key absolute five-minute expiration is the correct configuration.
Study Guide references: Azure Managed Redis # key expiration; TTL behavior; cache expiration versus eviction; fixed versus sliding expiration.
NEW QUESTION # 16
An AI application retrieves configuration values from App Configuration. The application uses static configuration.
You need to implement a solution that supports dynamic configuration updates and minimizes latency for application requests.
What should you do?
Answer: C
Explanation:
You should cache configuration values with a refresh interval.
Enables Dynamic Configuration: Azure App Configuration providers natively support a cache- polling mechanism. Once the refresh interval expires, the application checks for updates and reloads configuration values automatically without requiring an application restart.
Minimizes Latency: By pulling configuration values from a local, in-memory cache for all intermediary requests, you completely bypass the network latency of making a remote API call to Azure on every single application request.
Prevents Throttling: Relying on cached values prevents your application from exhausting your App Configuration resource's request quota.
Reference:
https://learn.microsoft.com/en-us/azure/azure-app-configuration/howto-best-practices
NEW QUESTION # 17
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