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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement computer vision solutions - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - 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 | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
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NEW QUESTION # 24
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 # 25
You are developing a solution for a hospital to support the following use cases:
*The most recent patient status details must be retrieved even if multiple users in different locations have updated the patient record.
*Patient health monitoring data retrieved must be the current version or the prior version.
*After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.
You provision a Cosmos DB NoSQL database and set the default consistency level for the database account to Strong. You set the value for Indexing Mode to Consistent.
You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.
Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Strong consistency is required for the patient-status scenario because it provides linearizability : reads return the latest committed value. This is the appropriate guarantee when updates can originate from different locations and the application must never return an older patient status. Microsoft defines Strong as the highest Azure Cosmos DB consistency level and notes that it provides the strongest freshness guarantee, at the cost of greater latency and reduced availability compared with weaker models.
For health-monitoring data, Bounded Staleness is the intended consistency level. It explicitly limits how far reads can lag behind writes using a maximum number of versions (K) or a time interval (T). Conceptually, this matches a requirement that data may be current or only a limited number of versions behind.
For finalized billing data, Eventual consistency provides the lowest latency and highest availability. Once discharge processing is complete and no further billing writes occur , replicas eventually converge on the same final value. Microsoft specifically notes that, in the absence of further writes, weaker consistency levels ultimately yield the same converged data.
Azure Cosmos DB also permits requests to relax consistency below an account ' s Strong default , which is exactly the optimization pattern described here.
Study Guide references: Azure Cosmos DB for NoSQL # Strong consistency; Bounded Staleness; Eventual consistency; per-request consistency override; latency/availability trade-offs.
NEW QUESTION # 26
You need to translate real-time spoken customer conversations from English to Spanish text during a support call, with minimal latency.
Which Azure AI service should you use?
Answer: A
Explanation:
Use Azure AI Speech with Speech Translation . Microsoft documents Speech Translation as a real-time capability that accepts a live audio stream in a source language and returns translated text, synthesized speech, or both in one or more target languages. This matches the scenario precisely: the input is spoken English during a live support call, the required output is Spanish text, and latency must be minimal.
Speech Translation can be implemented through the Speech SDK , which returns intermediate recognition and translation results as speech is detected. That streaming behavior is materially better suited to live conversations than a workflow that first transcribes audio and then separately calls a text-only translation API.
Microsoft specifically positions Speech Translation for low-latency, real-time speech-to-text and speech-to- speech translation scenarios.
Azure AI Translator translates text and therefore would require a separate speech-to-text stage before translation. Azure AI Language provides NLP capabilities such as classification, sentiment, and entity extraction rather than live speech translation. Azure AI Document Intelligence extracts structured information from documents and forms and is unrelated to streaming conversational audio.
Therefore, Azure AI Speech (Speech Translation) is the correct choice.
Study Guide references: Azure AI Speech # Speech Translation; real-time transcription and translation; Speech SDK; streaming audio translation; target-language text output.
NEW QUESTION # 27
Drag and Drop Question
You are deploying an Azure Function app that retrieves secrets from Key Vault by using a managed identity.
The deployment must ensure that identity and secret configuration are in place before the function code is deployed.
You need to deploy the function app securely.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Create the function app
You must first provision the underlying Azure Functions infrastructure before you can bind an identity or configuration settings to it.
Step 2: Assign a managed identity to the function app
Turning on the managed identity (such as a system-assigned identity) creates a distinct security principal in Microsoft Entra ID for the resource.
Step 3: Grant access to Key Vault
Use the managed identity's principal ID to create an access policy or RBAC role assignment in Key Vault, allowing the app to read secrets.
Step 4: Add Key Vault references to application settings.
Configure the Function App's application settings to point to the Key Vault secret URIs (@Microsoft.KeyVault(...)), which can now be securely resolved by the identity.
Step 5: Deploy the function code
Finally, deploy the application code. This ensures that when the code initializes and executes, all environment variables and secrets are already active and accessible, preventing application startup failures.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 28
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
Answer:
Explanation:
Explanation:
Verified answer: Initialize connection: CosmosClient. Define shared throughput: Database. Perform item CRUD: Container.
Detailed Explanation: The Cosmos DB SDK follows the service resource hierarchy. CosmosClient represents the account connection and is initialized with the account endpoint and credential. Shared throughput is configured at the database level when multiple containers share database throughput. Item create, read, update, and delete operations are performed through a container object because containers hold items and define partitioning/indexing behavior. An indexing policy is configuration attached to a container, not the SDK object used for CRUD.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Cosmos DB databases, containers, and items
NEW QUESTION # 29
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