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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 2: Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Topic 3: Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Topic 4: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
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NEW QUESTION # 120
You need to add a custom voice that matches your company's brand for a text-to-speech feature, distinct from any standard neural voice. What should you use?
Answer: A
Explanation:
Custom Neural Voice lets you train a unique synthetic voice from recorded voice samples, producing a brand-specific voice rather than using one of the standard prebuilt neural voices.
NEW QUESTION # 121
You need to optimize secure database connectivity from the containerized Recommendation API.
How should you configure the application?
Answer:
Explanation:
Explanation:
* Comply with the authentication policy for database access: Use managed identity authentication.
* Support high-concurrency requests with minimal latency: Use a connection pooling library.
* Protect database stability during traffic spikes: Configure maximum pool size.
The Recommendation API should use managed identity authentication because Fabrikam explicitly requires service-to-database authentication without long-lived credentials. Azure Container Apps managed identities allow applications to obtain Microsoft Entra tokens at runtime and access supported Azure resources without storing usernames, passwords, or connection secrets in application configuration.
For high-concurrency PostgreSQL access, implement a connection pooling library . Azure Database for PostgreSQL documentation recommends connection pooling because repeatedly opening new database connections creates backend processes and consumes CPU and memory. Reusing existing connections reduces connection-establishment overhead and improves latency and throughput. Azure also provides PgBouncer as a built-in pooling option for Flexible Server.
To protect PostgreSQL during sudden traffic spikes, configure a maximum pool size . An unlimited pool can allow application demand to create excessive concurrent connections, which can exhaust database memory and increase contention. Microsoft warns that increasing connection counts can cause significant performance degradation and recommends conservative connection limits combined with pooling.
Therefore, the optimal configuration combines managed identity + connection pooling + bounded pool size
.
Study Guide references: Azure Container Apps managed identities; Azure Database for PostgreSQL connection management; PgBouncer; connection-pool sizing and resource protection.
NEW QUESTION # 122
You are troubleshooting a production Azure OpenAI service that is experiencing intermittent connectivity issues. All telemetry is sent to a Log Analytics workspace.
You must write a KQL query that analyzes the AppRequests table. The query must filter for requests that occurred within the last 30 minutes and have a failed status.
The final output must display the total number of failures for each unique operation.
You need to select the KQL operators required to filter the time range and aggregate the failure counts.
Which two operators should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
You should use both the where and summarize operators to achieve this. The where operator filters the data by time and status, while the summarize operator aggregates the failure counts by operation.
Example code:
AppRequests
| where TimeGenerated > ago(30m)
| where Success == false
| summarize FailureCount = count() by OperationName
Reference:
https://digitalmaterial.ch/blog/query-data-from-the-application-insights-workspace-with-the-rest-api/
NEW QUESTION # 123
Drag and Drop Question
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome 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: Supports automatic rotation.
When an AI application retrieves a secret from Azure Key Vault without specifying a version, it always fetches the latest active version. This choice impacts your security and operations in the following ways:
Latest Version Fetch: The vault automatically serves the current active secret value.
No Code Changes: Your application reads the new credential immediately after rotation without a code redeployment.
Zero Downtime: Automated rotation pipelines can update the secret in Key Vault while the app seamlessly switches to the new token.
Box 2: Requires manual update after rotation
Using a specific version identifier when retrieving a secret from Key Vault means the application always requests one exact version of that secret. This choice requires manual update after rotation because the application code or configuration pins a specific version ID and will not fetch the new value when the secret rotates.
Box 3: Prevents credential storage
Prevents credential storage is the correct outcome of using managed identity for authentication.
No credentials to manage: Managed identities eliminate the need for developers to manage credentials entirely.
Automatic token handling: Azure automatically creates an identity for the application in Microsoft Entra ID and handles the service principal's lifecycle behind the scenes, ensuring that no secrets or connection strings are ever hardcoded or stored in configuration files.
Box 4: Introduces credential exposure
Introduces credential exposure and Requires manual update after rotation are the primary outcomes of storing a client secret in environment variables.
Storing clear-text secrets in environment variables poses a significant security risk. Any process, compromised dependency, or user with access to the execution environment (such as container details, application logs, or process dumps) can easily read them.
Reference:
https://medium.com/simform-engineering/building-automated-secret-rotation-using-azure-key-vault-and-net-01e0701f0a2e
https://www.varonis.com/blog/azure-managed-identities
NEW QUESTION # 124
You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
[C]
You can configure an Azure Service Bus scale rule in Azure Container Apps, which utilizes built- in KEDA (Kubernetes Event-driven Autoscaling) to monitor queue length and scale the app from zero instances to meet your processing demands.
[B]
Configure the scaling rule to allow for the termination of all active replicas is the correct action to take.
To ensure that no compute resources are consumed when the queue is empty, you must set the minimum replica count (minReplicas) to 0 in your Azure Container Apps scaling configuration.
When the Azure Service Bus queue has zero messages, KEDA will scale the container replicas down to zero, stopping all compute billing.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/scale-app
NEW QUESTION # 125
......
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