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| Section | Objectives |
|---|---|
| Topic 1: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Connect to and consume Azure services | - Integrate Azure services
|
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NEW QUESTION # 125
Drag and Drop Question
You are configuring an Azure-hosted AI application that uses Azure App Configuration to manage its runtime settings.
The application must provide the following functionality:
- Support percentage-based, targeted rollouts.
- Maximize token limits per request.
- Store a key that is retrieved securely at runtime.
- Reference a list of supported deployment regions.
You need to configure the App Configuration features for the app.
Which features should you configure? To answer, move the appropriate App Configuration features to the correct configuration items. You may use each App Configuration features 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: Feature flag
Use a Feature flag.
Azure App Configuration provides built-in feature management specifically designed to dynamically control application behavior without redeploying code.
Percentage-Based Rollouts: Feature flags support targeting filters (such as the standard Microsoft.Targeting filter), which natively handle percentage-based rollouts and specific audience targeting rules.
Dynamic Changes: You can safely toggle features or change the exposure percentage on the fly directly through the Azure portal or via APIs.
Box 2: Key-value
Key-value is the correct feature to use.
A standard key-value pair is the optimal choice for managing runtime settings like numeric thresholds or token limits.
Direct Data Injection: It allows you to store the specific maximum token limit (e.g., MaxTokensPerRequest = 4096) as an integer or string that your application can parse directly at runtime.
No Extra Overhead: Unlike the other options, a standard key-value pair maps directly to application configuration variables without requiring secondary lookups or conditional logic evaluation.
Box 3: Key Vault reference
Use an Azure Key Vault reference.
Azure App Configuration is optimized for non-sensitive runtime configurations like feature flags, environment names, and general application settings. Sensitive values (such as API keys for OpenAI/LLM models, database passwords, and connection strings) should always be stored natively in Azure Key Vault.
By creating a Key Vault Reference inside Azure App Configuration, you store only the URI pointing to the secret rather than the secret value itself. When your AI application requests configuration data at runtime, the App Configuration provider automatically contacts Key Vault, resolves the reference, and securely injects the actual secret directly into your application's memory pipeline.
Box 4: Key-value
Use a standard configuration key-value pair (or a JSON array within a key).
How to store it: Save the regions as a comma-separated string or a JSON array under a single key.
Key Example: Deployment:SupportedRegionsValue Example: ["eastus", "westeurope",
"southeastasia"]
Content Type: Set the content type to application/json so your application can automatically parse it into a list or array at runtime.
Reference:
https://learn.microsoft.com/en-us/azure/azure-app-configuration/manage-feature-flags
https://turbo360.com/blog/get-azure-key-value-pairs-from-app-configuration
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 126
You need to deploy Azure Function resources and apps to meet the business and technical requirements.
What should you use?
Answer: D
Explanation:
Use GitHub Actions because Proseware requires an automated, version-controlled deployment pipeline and explicitly prohibits local and command-line deployments. Microsoft documents GitHub Actions as a supported CI/CD mechanism for Azure Functions: a workflow stored under .github/workflows can build and deploy Function app code automatically in response to repository events such as pushes or pull requests. The Azure Functions deployment action can deploy the application package directly to the target Function app.
GitHub Actions also satisfies the infrastructure requirement. Microsoft provides first-class support for deploying Bicep templates through GitHub Actions , enabling Azure resources to be provisioned declaratively from the same source-controlled pipeline. A pull-request workflow can perform validation or Bicep what-if analysis before changes are merged, while a main-branch workflow can perform the actual deployment after approval. This provides repeatability, auditing, and separation from individual developer machines.
Azure Functions Core Tools and Azure CLI are commonly used for interactive/local deployments, which directly conflicts with the requirement to eliminate command-line deployment. Local Git deployment also fails the requirement for a centrally automated Bicep-based deployment process.
Study Guide references: Azure Functions CI/CD; GitHub Actions; infrastructure as code with Bicep; automated Azure deployments; version-controlled deployment workflows.
Category Breakdown
Category Number of Questions
Connect to and consume Azure services 41
Develop AI solutions by using Azure data management services 39
Secure, monitor, troubleshoot Azure solutions 34
Develop containerized solutions on Azure 28
TOTAL 142
Exam Topic Breakdown
Exam Topic Number of Questions
NEW QUESTION # 127
You need to deploy a batch embedding workload according to the planned application architecture.
What should you use?
Answer: A
Explanation:
The batch embedding workload is planned to run in Azure Kubernetes Service (AKS) , and Kubernetes workloads should be defined declaratively by using YAML manifest files . Microsoft states that a Kubernetes manifest defines the desired state of cluster resources, including Deployments, Jobs, Services, ConfigMaps, and other Kubernetes objects. These manifests can then be deployed by using kubectl apply -f < manifest > .
yaml.
For this case, YAML is particularly appropriate because the technical requirements emphasize repeatable, version-controlled, automated deployments . A batch vector embedding regeneration process would typically be represented as a Kubernetes workload such as a Job or another batch-oriented resource, with its configuration committed to Git alongside the rest of the deployment definitions.
kubectl run and kubectl create are imperative commands and are less appropriate for a controlled Git-based deployment model because they do not inherently preserve the complete desired-state definition as source- controlled configuration. XML is not the standard Kubernetes manifest format. az aks commands are primarily used to provision and manage the AKS cluster itself rather than declaratively define application workloads running inside the cluster.
Therefore, the correct choice is YAML-formatted Kubernetes manifest files .
Study Guide references: Azure Kubernetes Service # Kubernetes manifests; declarative deployments; kubectl apply; Git-based workload configuration.
NEW QUESTION # 128
Your solution must answer questions about numeric data in large Excel-based reports, for example:
" What was Q3 revenue for the West region? "
Which approach is most appropriate?
Answer: C
Explanation:
For precise questions over tabular numeric data, convert the workbook data into a structured relational/table representation and expose controlled query functionality to the AI application through function calling. This preserves columns such as quarter, region, revenue, product, and fiscal period as typed fields that can be filtered, aggregated, and compared deterministically. Microsoft demonstrates natural-language-to-SQL patterns in which a user ' s question is translated into a query against structured PostgreSQL data, while Foundry function calling allows a model or agent to invoke application-defined query tools with structured parameters.
This is materially more reliable than embedding spreadsheet rows as unstructured text. Vector search is optimized for semantic similarity , not exact arithmetic, grouping, filtering, or aggregation. A query such as " Q3 revenue for the West region " is fundamentally a structured lookup or aggregation problem. SQL can express those constraints explicitly and return the authoritative numeric result.
Microsoft Foundry also supports agents working with structured data and provides examples where agent tools analyze numeric files and return computed values.
Relying on parametric model knowledge cannot provide current organization-specific financial figures. Azure AI Vision could OCR an image of a spreadsheet, but converting an already structured workbook into an image would destroy useful schema and increase extraction error risk.
Study Guide references: Microsoft Foundry agents; function calling; structured data access; natural- language-to-SQL; grounding AI responses in enterprise data.
NEW QUESTION # 129
Drag and Drop Question
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Enable ingress
You must first expose your container app to external traffic to generate the default fully qualified domain name (FQDN) needed for DNS mapping.
Step 2: Add DNS records to the domain provider
Log into your domain registrar to create the required TXT (for verification) and CNAME/A records pointing to your container app.
Step 3: Validate the custom domain name
Azure checks your DNS records to confirm that you actually own the domain before allowing it to be linked.
Step 4: Add the custom domain name
Once validation passes, you officially add and register the custom domain name within the Azure Container App configuration.
Step 5: Bind the certificate
Finally, bind an SSL/TLS certificate to the custom domain to secure the connection and enable HTTPS access.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/ingress-overview
NEW QUESTION # 130
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