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| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Select appropriate Azure AI services - Monitor and optimize AI solutions |
| Implement Azure AI solutions | - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search |
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NEW QUESTION # 16
Drag and Drop Question
You are developing a Python application that reads data from Azure Cosmos DB for NoSQL by using the azure-cosmos SDK.
You need to connect to an existing account and run a SQL query against an existing container.
Which four 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.
Answer:
Explanation:
Explanation:
In the azure-cosmos Python SDK, you do not use an explicit "InitializeDatabaseProxy" method.
Instead, you use the client to get a database reference, and then use that database reference to get a container reference.
Step 1: Initialize CosmosClient
You must first establish the root connection using the client object with your account endpoint and credentials.
Step 2: Specify the database name
From the client, you call client.get_database_client(database_name) to target your specific database resource.
Step 3: Obtain the container reference
From the database proxy object, you call database.get_container_client(container_name) to reference the specific data container.
Step 4: Run the Query
Finally, you execute the SQL operation directly on that container using container.query_items().
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/quickstart-python
NEW QUESTION # 17
You need to secure an Azure OpenAI resource so that it is only reachable from your virtual network and not from the public internet. What should you configure?
Answer: B
NEW QUESTION # 18
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 a GitHub repository secret.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 19
Hotspot Question
You are developing an application that uses a Python API to perform similarity queries against Azure Database for PostgreSQL. The application creates a new database connection for every request.
During peak traffic, the application intermittently fails to open new database sessions. Logs indicate that the maximum number of connections have been reached.
You need to configure the connection pooling strategy to reduce connection setup overhead and maximize reuse for the high-concurrency workload.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: PgBouncer
To resolve the connection exhaustion issue, you should use the built-in PgBouncer connection pooler configured in Transaction Pooling mode.
Because PostgreSQL uses a process-per-connection architecture, opening and closing a new session for every API request causes massive CPU/memory overhead and rapidly drains available slots during traffic spikes Box 2: Transaction Pooling mode Reference:
https://learn.microsoft.com/en-us/azure/postgresql/connectivity/concepts-connection-pooling-best-practices
NEW QUESTION # 20
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
Answer:
Explanation:
Explanation:
Verified Answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | AKS scaling overview | Kubernetes services in AKS
NEW QUESTION # 21
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