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| Section | Objectives |
|---|---|
| Topic 1: Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
| Topic 2: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 3: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions |
>> AI-200 Testking Learning Materials <<
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NEW QUESTION # 10
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: Store the API key as an App Service application setting configured through the Azure Portal.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 11
You are using the Python SDK to develop a containerized AI application that retrieves the value of a runtime setting stored in an Azure App Configuration resource. The application will run in Azure in the security context of a managed identity.
The application must work in a local development environment without any code changes.
You need to complete the code that implements the retrieval.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: var1 = DefaultAzureCredential(); retrieval call = get_configuration_setting(...).
Detailed Explanation: DefaultAzureCredential is designed to support the same application code across local development and Azure-hosted environments. Locally it can use developer credentials, while in Azure it can use the workload's managed identity. The App Configuration client's get_configuration_setting method retrieves a specific key/label pair, which matches the code shown. Using ManagedIdentityCredential directly would not provide the same local-development credential chain without code or environment-specific handling.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Azure App Configuration Python client | Managed identities for Azure resources
NEW QUESTION # 12
You are developing an application that must extract structured field data (invoice number, total, vendor name) from scanned invoices in multiple layouts.
You need a solution that requires no custom model training.
What should you use?
Answer: C
Explanation:
Use the Azure AI Document Intelligence prebuilt invoice model . The prebuilt invoice model is specifically designed to analyze invoices and extract structured fields and line items without requiring an organization to train a custom model. Microsoft documents support for fields including InvoiceId , VendorName , InvoiceTotal , InvoiceDate, CustomerName, AmountDue, tax information, addresses, and invoice line items.
Because the model is pretrained to recognize common invoice structures, it can process invoices that use different layouts and formats while returning normalized structured output. This directly satisfies the requirement to extract invoice number, total, and vendor information from scanned documents while minimizing development and training effort.
The Azure AI Vision Read API performs OCR and can extract printed or handwritten text, but it does not provide invoice-specific semantic field extraction. Custom named entity recognition would require training and is designed for text entity extraction rather than document-layout understanding. A Document Intelligence custom neural model can handle organization-specific forms and layouts, but it requires custom model training, directly violating the requirement.
Therefore, the prebuilt invoice model provides the required combination of OCR, document understanding, structured extraction, and zero custom training.
Study Guide references: Azure AI Document Intelligence # prebuilt invoice model; structured field extraction; OCR; invoice fields and line items.
NEW QUESTION # 13
You need to deploy Azure function resources and apps by using an automated, version-controlled CI/CD pipeline that supports declarative infrastructure deployment. What should you use?
Answer: B
Explanation:
Detailed Explanation: GitHub Actions is the correct orchestration mechanism among the options because the case requires an automated, version-controlled pipeline and Bicep-based declarative deployment. Microsoft documents GitHub Actions as a supported way to deploy Bicep and automate Azure resource deployment.
Local Git, Azure Functions Core Tools, and Azure CLI can participate in development or scripted deployment, but they do not by themselves satisfy the stated requirement for a repository-driven, repeatable CI
/CD pipeline.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Deploy Bicep with GitHub Actions | Automate Function app resource deployment
NEW QUESTION # 14
Hotspot Question
You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.
The application must use managed identity for authentication.
You review the following code segment that retrieves a secret from Key Vault.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
No, this code will not retrieve the latest version of dbPassword.
Instead, it explicitly retrieves the specific version designated by the string "123".
To pull the latest active version of a secret, omit the version parameter entirely from the get_secret function.python# Change this line:
secret = client.get_secret("dbPassword", version="123")
# To this line:
secret = client.get_secret("dbPassword")
Box 2: Yes
Yes, the code will successfully authenticate to Azure Key Vault without storing client secrets when deployed to an Azure-hosted environment with a managed identity enabled.
DefaultAzureCredential Behavior: This class is a sequential chaining credential mechanism.
When deployed to Azure (such as Azure App Service, Azure VMs, or Azure Functions), it automatically looks for an active Managed Identity environment endpoint to fetch an access token.
Zero Secret Storage: Because the platform relies on the underlying Azure host infrastructure to handle identity token generation, you do not need to hardcode client secrets, passwords, or certificates anywhere inside your application code or configuration settings.
Box 3: No
No, subsequent calls to this code will not retrieve the new version.
By explicitly including version="123" in the get_secret method, you are requesting a specific immutable version of the secret.
Reference:
https://learn.microsoft.com/en-us/azure/key-vault/secrets/quick-create-python
NEW QUESTION # 15
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