Your application must classify uploaded product images into one of 40 custom categories specific to your business (e.g., proprietary part numbers). What should you use?

• A. Azure AI Document Intelligence
• B. Azure AI Vision prebuilt image analysis
• C. Azure AI Face API
• D. Azure AI Custom Vision (image classification)

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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.

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

Drag and Drop Question
You deploy an API to Azure Container Apps.
The solution must provide the following functionality:
- Support the concurrent activation of multiple application versions.
- Allocate a specific percentage of incoming requests to a secondary
version.
You need to configure revision behavior.
Which configurations should you use? 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.

Explanation:
Box 1: Multiple revisions mode
To test a new revision without affecting production traffic, you must configure the Container App to use Multiple active revisions mode and set the traffic allocation for the new revision to 0%.
Box 2: Traffic splitting
To split traffic between multiple versions, you must switch the container app's revision mode to Multiple revision mode and configure the traffic splitting weights under the Ingress configurations.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/revisions-manage

Your application stores conversation history so a multi-turn chatbot can reference earlier turns, but the context window is limited. You need to keep the most relevant history within token limits.
What should you implement?

• A. Increase max_tokens for output only
• B. Store history only in the client and never send it to the model
• C. Summarize older turns and retain recent turns verbatim
• D. Truncate all history after 2 turns

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Drag and Drop Question
You are configuring sampling for a distributed application that sends traces to Azure Monitor.
The solution must
- Preserve upstream sampling decisions across distributed traces.
- Capture all spans during local testing.
- Sample 10 percent of traces in production.
You need to apply the appropriate sampling configuration for each requirement.
What should you do? 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.

Explanation:
Box 1: ParentBasedSampler
ParentBasedSampler is the correct setup to use because it inspects incoming trace context and honors the sampling decision made by an upstream parent span.
Conserves Decisions: If an upstream service marks a trace as sampled or dropped, the ParentBasedSampler delegates the exact same decision to downstream child services.
Prevents Broken Traces: It avoids gaps in end-to-end telemetry by ensuring entire transaction chains are kept or discarded together.
Box 2: AlwaysOnSampler
AlwaysOnSampler is the correct setup to use because it samples every single span (100% trace capture rate) without dropping any telemetry, making it ideal for local development, debugging, and testing.
AlwaysOnSampler: Samples and records 100% of all traces and spans regardless of volume or upstream decisions.
Box 3: TraceIdRatioBasedSampler(0.1)
To capture exactly 10 percent of traces in production using OpenTelemetry and Azure Monitor, you should use TraceIdRatioBasedSampler(0.1).
Fixed-Rate Sampling: Azure Monitor and standard OpenTelemetry configurations achieve a fixed percentage rate by evaluating the randomly generated 128-bit Trace ID.
Ratio Representation: A sampling ratio between 0 and 1 represents the percentage. Passing a parameter of 0.1 mathematically translates to capturing exactly 10% of your production traffic.
Reference:
https://medium.com/@varun_0K/sampling-strategies-in-distributed-tracing-a-comprehensive-guide-6e80092068c3
https://learn.microsoft.com/en-us/azure/azure-monitor/app/application-insights-faq