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| Section | Objectives |
|---|---|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
You are developing a microservices-based application that uses Azure Container Apps. The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
Report generation must be implemented as follows:
Process reports every Sunday at midnight.
Minimize app resources during report generation.
Record metrics in the same location as other containers.
Use the same virtual network as other containers.
Correct Answer:
Explanation:
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 a Cosmos DB 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.
Correct Answer:
Explanation:
Strong consistency is required for the patient-status scenario because it provides linearizability : reads return the latest committed value. This is the appropriate guarantee when updates can originate from different locations and the application must never return an older patient status. Microsoft defines Strong as the highest Azure Cosmos DB consistency level and notes that it provides the strongest freshness guarantee, at the cost of greater latency and reduced availability compared with weaker models.
For health-monitoring data, Bounded Staleness is the intended consistency level. It explicitly limits how far reads can lag behind writes using a maximum number of versions (K) or a time interval (T). Conceptually, this matches a requirement that data may be current or only a limited number of versions behind.
For finalized billing data, Eventual consistency provides the lowest latency and highest availability. Once discharge processing is complete and no further billing writes occur , replicas eventually converge on the same final value. Microsoft specifically notes that, in the absence of further writes, weaker consistency levels ultimately yield the same converged data.
Azure Cosmos DB also permits requests to relax consistency below an account ' s Strong default , which is exactly the optimization pattern described here.
Study Guide references: Azure Cosmos DB for NoSQL # Strong consistency; Bounded Staleness; Eventual consistency; per-request consistency override; latency/availability trade-offs.
You are deploying a model in Azure AI Foundry and must ensure the endpoint can handle unpredictable bursts of traffic while keeping cost low during idle periods.
Which deployment type should you choose?
Correct Answer: A 🗳️
Explanation: Only visible for VerifiedDumps members. You can sign-up / login (it's free).
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history. The API key must not be stored in application configuration directly. The API key must be accessed securely from Azure Key Vault.
You need to ensure that the API key is stored securely in Azure Key Vault and is available to the container at runtime without being exposed in source control or Git commit history.
Solution: Embed the API key as a hardcoded environment variable in the Dockerfile.
Does the solution meet the goal?
Correct Answer: A 🗳️
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?
Correct Answer: D 🗳️
Explanation: Only visible for VerifiedDumps members. You can sign-up / login (it's free).
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