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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 3: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 60
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
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.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key in Azure Key Vault and reference it from an App Service application setting.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 61
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: D
Explanation:
Azure AI Speech's Speech Translation capability performs real-time speech-to-text and translation in a single streaming pipeline, purpose-built for low-latency live audio scenarios, unlike the text-only Translator service.
NEW QUESTION # 62
An AI application retrieves configuration values from App Configuration. The application uses static configuration.
You need to implement a solution that supports dynamic configuration updates and minimizes latency for application requests.
What should you do?
Answer: C
Explanation:
Use the Azure App Configuration provider ' s caching and dynamic refresh mechanism with a configured refresh interval . The application loads configuration into a local cache and periodically checks App Configuration for changes. When a registered key changes, the provider refreshes the cached configuration without requiring an application restart. Microsoft documents SetRefreshInterval and equivalent provider settings as the mechanism for controlling the minimum time between configuration-refresh checks.
This approach satisfies both requirements. The configuration is dynamic because updated values can be detected and reloaded at runtime, while request latency remains low because normal application requests read configuration from the local cached copy instead of making a remote App Configuration request every time.
For request-driven refresh, Microsoft notes that refresh occurs asynchronously and does not block the incoming request. Before the configured refresh interval expires, additional refresh calls effectively perform no remote operation.
Microsoft also recommends caching App Configuration values instead of loading them whenever they are used, both to reduce latency and to avoid excessive service requests, throttling, and unnecessary charges.
Environment variables remain static unless the application is restarted or redeployed. Retrieving configuration on every request increases latency and service load. Key Vault is primarily intended for secrets, not general dynamic application configuration.
Study Guide references: Azure App Configuration; dynamic configuration refresh; provider caching; refresh intervals; request-driven refresh; configuration resiliency.
NEW QUESTION # 63
An Azure Storage Blob upload must trigger backend processing.
The solution must:
- Avoid polling.
- Minimize latency.
- Trigger automatically when the upload occurs.
You need to ensure that processing starts as soon as a file is uploaded.
Which trigger should you implement?
Answer: A
Explanation:
Azure Event Grid is the best trigger type to use because it pushes real-time event notifications with minimal latency and completely avoids polling.
Push-based model: Sends events instantly as blobs are created.
Lowest latency: Delivers notifications in milliseconds.
No polling overhead: Saves resources compared to timer or queue polling.
How It Works
A user uploads a file to Azure Blob Storage.Blob Storage fires an Microsoft.Storage.BlobCreated event.
Event Grid pushes this event directly to your backend endpoint (such as an Azure Function or Webhook).Processing starts right away.
Reference:
https://damle-amit075.medium.com/azure-databricks-auto-loader-plugging-the-gap-for-batch-loads-72585b0f158e
NEW QUESTION # 64
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You are using Azure Monitor Application Insights to investigate a production API. You open the Logs blade and set the time range to Last 24 hours.
An engineer recommends the following query to count requests by result code and sort the results from most frequent to least frequent:
requests
| summarize request_count = count() by resultCode
| order by request_count desc
You need to determine whether the query returns the number of requests grouped by result code and sorted from most to least frequent.
Solution: The result codes are sorted alphabetically.
Does the solution meet the goal?
Answer: B
Explanation:
The solution does not meet the goal because the query does not sort by resultCode. The summarize operator groups request records by each distinct resultCode value and calculates the number of requests in each group by using count().
The resulting table contains a resultCode column and a calculated request_count column. The next statement:
| order by request_count desc
sorts the output by request_count in descending order , which places the most frequently occurring result code first and the least frequent last.
Therefore, the output is sorted by frequency , not alphabetically or numerically by the resultCode field.
If alphabetical sorting by result code were required, the query would instead use a statement such as:
| order by resultCode asc
That is not what the provided query does.
Because the Logs blade is already scoped to the Last 24 hours , the query operates over that selected time range unless additional time filtering is explicitly added.
Study Guide references: Azure Monitor Logs; Application Insights requests table; KQL summarize; count() aggregation; order by; descending sort semantics.
NEW QUESTION # 65
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