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| Section | Objectives |
|---|---|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 107
Case Study 2 - Proseware Inc.
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 by 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 perform a one-time analysis of logs collected from AKS, ACA, and Azure Function apps according to the business requirements.
What should you use?
Answer: B
Explanation:
Business Requirements
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
A KQL query is the correct tool to use for a one-time, cross-resource analysis to correlate telemetry across AKS, Azure Container Apps (ACA), and Azure Functions.
Cross-Resource Querying: KQL (Kusto Query Language) allows you to use the union operator to combine tables from different workspaces or applications effortlessly.
Correlation Capabilities: You can join logs using standard cloud fields like operation_Id,
_ResourceId, or custom headers to track a single request across your entire architecture.
On-Demand Analysis: It requires no setup time, making it ideal for a one-time investigation directly inside the Azure Monitor Log Analytics interface.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-perform-cross-workspace-queries-in-azure-log-analytics/view
NEW QUESTION # 108
Your solution must answer questions about numeric data in large Excel-based reports (e.g.,
"What was Q3 revenue for the West region?"). Which approach is most appropriate?
Answer: A
Explanation:
Precise numeric/tabular lookups are handled poorly by vector similarity search or free-text embedding of numbers. Structuring the data (e.g., in a database) and letting a function-calling agent issue precise queries yields accurate, verifiable answers.
NEW QUESTION # 109
Drag and Drop Question
A Python API retrieves a document from Azure Database for PostgreSQL by using a SQL statement. The API accepts the document ID from user input. The current implementation inserts the document ID directly into the SQL statement.
You need to secure the SQL statement execution by minimizing the possibility of SQL injection.
How should you modify the current implementation? 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:
Box 1: Use a parameterized query
To secure your SQL statement against SQL injection, you must replace direct string insertion with parameterized queries (also known as prepared statements).
Box 2: Pass the ID as an argument
The current implementation inserts the document ID directly into the SQL statement.
Box 3: Supply the parameter tuple to the SDK method
Supply parameter tuples: Pass the user input as a separate tuple or list argument into your database driver's execution method (e.g., cursor.execute(query, (document_id,))).
Reference:
https://www.stackhawk.com/blog/finding-and-fixing-sql-injection-vulnerabilities-in-flask-python/
NEW QUESTION # 110
Case Study 2 - Proseware Inc.
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 by 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 deploy a batch embedding workload according to the planned application architecture.
What should you use?
Answer: C
Explanation:
Scenario, application architecture
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
YAML-formatted files should be used for this deployment.
In a production-grade Kubernetes architecture, declarative management using configuration files provides the predictability, version control, and automation required for complex microservices.
Declarative Configuration: Kubernetes relies natively on YAML to define the desired state of resources like Deployments, KEDA ScaledObjects (for event-driven Azure Function scaling), and Secrets.
Complex Deployments: Your workload requires setting up multiple components, environmental variables, and scaling rules that cannot be efficiently managed via single-line commands.
GitOps Readiness: Storing configurations in YAML allows you to track changes in source control and deploy updates automatically through CI/CD pipelines.
Reference:
https://developer.okta.com/blog/2022/05/05/kubernetes-microservices-azure
NEW QUESTION # 111
You have an Event Grid subscription that triggers an Azure Function.
You need to prevent loss of events in case the endpoint returns an HTTP 400 response.
Which action should you perform?
Answer: B
Explanation:
Configuring a dead-letter destination is exactly the right step to take.
By default, when an endpoint returns an HTTP 400 (Bad Request) or HTTP 413 (Payload Too Large) response, Azure Event Grid immediately stops delivery attempts and drops the message.
It treats these specific errors as non-transient, client-side issues, meaning it skips its standard 24- hour retry policy.
Configuring a dead-letter destination ensures that these dropped events are safely preserved for future troubleshooting and reprocessing.
Reference:
https://turbo360.com/blog/azure-event-grid-dead-letter-monitoring
NEW QUESTION # 112
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