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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Plan and manage Azure AI solutions | - Plan security and compliance requirements - Select appropriate Azure AI services - Monitor and optimize AI solutions |
| Topic 3: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
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NEW QUESTION # 142
You are implementing a Retrieval-Augmented Generation (RAG) system by using the native vector search capabilities of Azure Cosmos DB for NoSQL API.
You have a container named Documents that stores technical articles. Each article includes a property named embedding.
You must ensure that the system can perform efficient similarity searches between user queries and the stored articles.
You need to configure the database resources to support semantic retrieval.
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.
Answer:
Explanation:
Explanation:
* Facilitate mathematical distance calculations between data points: Configure a vector index.
* Define the document schema for high-dimensional data: Store data as a numeric array.
Azure Cosmos DB for NoSQL supports native vector search by storing embeddings directly in JSON documents and defining a vector embedding policy plus a vector index on the embedding path. Microsoft documents that vector search compares a query vector with stored vectors by calculating similarity or distance through the VectorDistance() system function. A vector index materially improves this process by reducing search latency, increasing throughput, and lowering RU consumption compared with an unindexed vector scan.
The embedding property itself must be stored as an array of numeric values . Microsoft examples show embedding properties such as " contentVector " : [2, -1, 4, ...] and define the associated vector policy with attributes including path, data type, dimensions, and distance function. This representation is required because embeddings are high-dimensional numerical vectors generated by an embedding model.
A composite index optimizes queries involving multiple scalar properties but does not provide vector- distance indexing. A Base64-encoded string cannot be used directly for native vector similarity calculations because Cosmos DB expects the vector field to contain numeric values matching the configured dimensionality.
Study Guide references: Azure Cosmos DB for NoSQL # vector embedding policies; vector indexes; VectorDistance(); numeric embedding arrays; native vector search.
NEW QUESTION # 143
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 # 144
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 # 145
You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
Reference the Azure Key Vault without requiring any changes to the Java code.
Dynamically add and remove instances of the Azure Functions host based on the number of incoming application events.
Ensure that instances are perpetually warm to avoid any cold starts.
Connect to a VNet.
Authentication to the Azure Key Vault instance must be removed if the Azure Function application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
Which three 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:
Step 1: Create the Azure Functions app with a Consumption plan type.
Use the Consumption plan for serverless.
Step 2: Create a system-assigned managed identity for the application.
Create a system-assigned managed identity for your application.
Key Vault references currently only support system-assigned managed identities. User-assigned identities cannot be used.
Step 3: Create an access policy in Key Vault for the application identity.
Create an access policy in Key Vault for the application identity you created earlier. Enable the " Get " secret permission on this policy. Do not configure the " authorized application " or applicationId settings, as this is not compatible with a managed identity.
Reference:
https://docs.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 146
An application deployed to AKS depends on an internal API hosted in the same cluster. All pods are healthy and report Ready status, but requests between the services time out.
You need to determine whether the issue is related to Kubernetes service configuration or application code.
What should you do first?
Answer: A
NEW QUESTION # 147
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