Quiz AI-200: Microsoft Developing AI Cloud Solutions on Azure
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Quiz
Question 1/101/10
Topic 1, Proseware Inc. Case Study
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
Vou need to deploy Azure function resources and apps by using an automated, version-controlled
failed pipeline that supports declarative infrastructure deployment. What should you use?
failed pipeline that supports declarative infrastructure deployment. What should you use?
Select the answer:Select the answer
1 correct answerA.
Local Git deployment
B.
GitHub Actions
C.
Azure Functions Core Tools
D.
Azure CLI
Quiz
Question 2/102/10
Topic 1, Proseware Inc. Case Study
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
DRAG DROP
You need to implement trace correlation according to the business requirements.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct
orders you select.
You need to implement trace correlation according to the business requirements.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct
orders you select.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 3/103/10
Topic 1, Proseware Inc. Case Study
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
You need to address the known issue resulting from vector similarity queries.
Which two actions should you perform? Each correct answer presents part of the solution. Choose
two.
NOTE: Each correct selection is worth one point.
Which two actions should you perform? Each correct answer presents part of the solution. Choose
two.
NOTE: Each correct selection is worth one point.
Select multiple answer: (Choose 2)Select the answer
2 correct answersA.
Add a composite index on the vector fields and metadata properties of the container.
B.
Modify the indexing precision of the vector fields.
C.
Set the account consistency level to Strong.
D.
Change the vector index type from flat to quantizedFlat or diskANN.
Quiz
Question 4/104/10
Topic 1, Proseware Inc. Case Study
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
You need to configure vector embedding updates according to the business and technical
requirements.
Which information should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct select is worth one point.
requirements.
Which information should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct select is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 5/105/10
Topic 1, Proseware Inc. Case Study
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval,
and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support
containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a
GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever
their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents
and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across
Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update
search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure
Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be
processed by internal AI workflows for semantic search and retrieval.
Monitoring:
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions
apps logs.
Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK
instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be
automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into
the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on
local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure
Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration
directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed
identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated,
version-controlled pipeline. Local and command-line deployments must be eliminated to ensure
repeatable, auditable deployments.
Known issues
RU consumption spikes during vector similarity queries.
You need to configure a connection string for the partner-facing service according to the technical
requirements.
What should you use?
requirements.
What should you use?
Select the answer:Select the answer
1 correct answerA.
Azure Key Vault references in App Service settings
B.
GitHub secrets
C.
Azure Container Registry Helm chart package
D.
Dockerfile ENV instructions
Quiz
Question 6/106/10
You are developing a Java application to be deployed in Azure. The application stores sensitive data
in Azure Cosmos DB.
You need to configure Always Encrypted to encrypt the sensitive data inside the application.
What should you do first?
in Azure Cosmos DB.
You need to configure Always Encrypted to encrypt the sensitive data inside the application.
What should you do first?
Select the answer:Select the answer
1 correct answerA.
Create a Microsoft Entra ID managed identity and assign the identity to a new Azure Key Vault instance.
B.
Create a new container to include an encryption policy with the JSON properties to be encrypted.
C.
Create a customer-managed key (CMK) and store the key in a new Azure Key Vault instance.
D.
Create a data encryption key (DEK) by using the Azure Cosmos DB SDK and store the key in Azure Cosmos DB.
Quiz
Question 7/107/10
You are designing an Azure Function app that exposes a public API.
The solution must:
Validate incoming request data and return results immediately to the caller.
Support Microsoft Entra ID authentication.
Scale automatically under variable load.
You need to implement a trigger.
Which trigger should you implement?
The solution must:
Validate incoming request data and return results immediately to the caller.
Support Microsoft Entra ID authentication.
Scale automatically under variable load.
You need to implement a trigger.
Which trigger should you implement?
Select the answer:Select the answer
1 correct answerA.
HTTP
B.
Azure Queue storage
C.
Azure Event Grid
D.
Service Bus topic
Quiz
Question 8/108/10
DRAG DROP
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow
for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a
downstream processor.
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.
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow
for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a
downstream processor.
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.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 9/109/10
You deploy a private container image from Azure Container Registry (ACR) to App Service.
App Service must authenticate to ACR to pull the image.
The solution must NOT store static registry credentials.
You need to configure the secure image pull authentication.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
App Service must authenticate to ACR to pull the image.
The solution must NOT store static registry credentials.
You need to configure the secure image pull authentication.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 10/1010/10
You are developing several microservices to run on Azure Container Apps. External HTTP ingress
traffic has been enabled for the Microservices.
A deployed micro service must be updated to allow uses to test new features. You have the following
requirements:
* Enable and maintain a single URL for the updated microservice to provide to test users.
* Update the microservice that corresponds to the current microservice version.
You need to configure Azure Container Apps.
Which features should configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
traffic has been enabled for the Microservices.
A deployed micro service must be updated to allow uses to test new features. You have the following
requirements:
* Enable and maintain a single URL for the updated microservice to provide to test users.
* Update the microservice that corresponds to the current microservice version.
You need to configure Azure Container Apps.
Which features should configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
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