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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
>> Exam Questions Microsoft AI-200 Vce <<
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NEW QUESTION # 138
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:
* Initialize CosmosClient.
* Specify the database name.
* Obtain the container reference.
* Run the query.
The Python Azure Cosmos DB SDK follows the resource hierarchy Account # Database # Container # Items . The first step is to initialize a reusable CosmosClient with the Azure Cosmos DB account endpoint and an appropriate credential. Microsoft identifies CosmosClient as the primary client for interacting with databases in an account.
Next, specify the existing database by calling client.get_database_client(database_name). This returns a DatabaseProxy representing that database. Importantly, DatabaseProxy should not be instantiated directly ; Microsoft explicitly states that it should be obtained through CosmosClient.get_database_client().
From the database reference, obtain the target container by using database.get_container_client (container_name). Microsoft documents this as the standard way to retrieve an existing container reference.
Finally, execute the SQL query through container.query_items() , which accepts the Cosmos DB for NoSQL query and optional parameters and returns an iterable result set.
Therefore, Initialize DatabaseProxy is the distractor: the SDK creates the DatabaseProxy through get_database_client; application code should not construct it directly.
Study Guide references: Azure Cosmos DB for NoSQL # Python SDK; CosmosClient; database and container clients; ContainerProxy.query_items().
NEW QUESTION # 139
Drag and Drop Question
You are developing a .NET application that uses Azure Cosmos DB for NoSQL to store application data.
The application uses the Azure Cosmos DB for NoSQL SDK to interact with the database account.
The application must perform the following tasks:
- Initialize the connection by using the account endpoint and key.
- Define shared throughput.
- Perform create, read, update, and delete (CRUD) operations on items
stored in a container.
You need to implement the SDK components required for the application to access and manage data in Azure Cosmos DB for NoSQL.
Which SDK components should you use? To answer, move the appropriate components to the correct requirements. You may use each component 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: CosmosClient
To initialize the connection to an Azure Cosmos DB for NoSQL account using the account endpoint and key, you must use the CosmosClient class.
Box 2: Database
The database SDK component should be used to define shared throughput.
In Azure Cosmos DB, shared throughput (provisioned Request Units per second or RU/s) is configured at the database level. When throughput is provisioned on a database, that capacity is shared among all the containers created within that specific database.
Box 3: Container
To perform item create, read, update, and delete (CRUD) operations, you should use the Container SDK component In the Azure Cosmos DB for NoSQL SDK for .NET, individual JSON documents (items) live inside a container. The Container class exposes the specific methods required to execute CRUD operations on these items.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-dotnet-get-started
NEW QUESTION # 140
Hotspot Question
A company uses Azure Monitor Application Insights to monitor application behavior, including incoming requests and dependencies.
You must identify failed requests from the last hour. You must also calculate the average duration of failed request dependency calls, grouped by operation name.
You need to analyze telemetry in Application Insights.
Which operators should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: where
Filter telemetry rows
The where operator filters a table to rows that satisfy a specific predicate. Both box 1 and box 2 require filtering telemetry data to the last hour (timestamp > ago(1h)).
Box 2: where
In the Kusto Query Language (KQL), the where operator filters a table to rows that satisfy a specific predicate or condition. Inside the inner subquery, you need to filter the dependencies telemetry table to only include records from the last hour (timestamp > ago(1)) Box 3: summarize Aggregate calculated data The summarize operator produces a table that aggregates the content of the input table. It is used here with the avg() aggregation function to calculate the average duration grouped by the operation_Name.
Reference:
https://tryhackme.com/room/kqlkustobasicqueries
NEW QUESTION # 141
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?
Answer: D
Explanation:
Progressive summarization of older conversation turns, combined with keeping recent turns verbatim, is a standard technique to preserve conversational context within a limited token budget without abruptly losing history.
NEW QUESTION # 142
You are reviewing the Python tracing configuration for an application that must send distributed traces to Azure Monitor.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
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:
* The code configures the tracer provider before any spans are created: Yes
* The configuration exports traces synchronously: No
* The configuration enables export to Azure Monitor: Yes
The first statement is Yes. The code calls trace.set_tracer_provider(TracerProvider()) before obtaining the tracer and before any span-creation operation occurs. In OpenTelemetry, retrieving a tracer by using trace.
get_tracer() does not itself create a span; spans are created later through methods such as start_span() or start_as_current_span().
The second statement is No. The code uses BatchSpanProcessor, which batches completed spans and exports them through a background processing mechanism rather than exporting every span synchronously on the application ' s execution path. OpenTelemetry documentation states that BatchSpanProcessor batches ended spans before sending them to the configured exporter, and the Python implementation uses a background thread for export processing.
The third statement is Yes. AzureMonitorTraceExporter is instantiated with an Azure Monitor/Application Insights connection string and passed to BatchSpanProcessor. That processor is then registered with the tracer provider. Microsoft provides this same pattern specifically for exporting OpenTelemetry traces to Azure Monitor/Application Insights.
Study Guide references: Azure Monitor OpenTelemetry; TracerProvider; BatchSpanProcessor; AzureMonitorTraceExporter; distributed tracing.
NEW QUESTION # 143
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