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| Section | Objectives |
|---|---|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Connect to and consume Azure services | - Integrate Azure services
|
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NEW QUESTION # 30
Hotspot Question
You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Vector
The correct field type for storing and querying embeddings in Redis is VECTOR.
Box 2: HNSW index
To support low-latency, large-scale approximate nearest neighbor (ANN) vector search in Redis, you should use the HNSW (Hierarchical Navigable Small World) index type with Float32 data type.
Reference:
https://redis.io/docs/latest/develop/ai/search-and-query/vectors/
NEW QUESTION # 31
You need to deploy Azure function resources and apps by using an automated, version-controlled CI/CD pipeline that supports declarative infrastructure deployment. What should you use?
Answer: C
NEW QUESTION # 32
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 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 query returns one row per unique resultCode value with the number of requests in each group.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* The query returns one row per unique resultCode value with the number of requests in each group.
The Kusto Query Language (KQL) query uses the summarize operator, which acts as a grouping and aggregation mechanism.
summarize request_count = count() by resultCode
This groups all the individual rows in the requests table by their unique resultCode. It then counts the total number of logs within each group and places that value into a new column called request_count.
order by request_count desc: This sorts those aggregated rows so that the resultCode with the highest number of requests appears at the top.
Incorrect:
* The result codes are sorted alphabetically.
* The query lists every individual request along with its result code.
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/reference/tables/apprequests
NEW QUESTION # 33
An ACA app processes messages from an Azure Storage queue.
The app must scale automatically based on messages in a specific Azure Storage queue by using a Kubernetes Event-driven Autoscaler (KEDA) custom scale rule.
You need to configure the required scale rule values.
Which two values should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
[A]
QeueName: The exact name of the specific Azure Storage queue your app is polling is required.
[C]
Why Trigger Type is Required
When configuring a custom scale rule in Azure Container Apps (ACA), the platform utilizes KEDA underneath. Because a custom rule can connect to many different event sources (such as Kafka, Redis, or Azure Storage), you must explicitly define the Trigger type (e.g., azure-queue) so KEDA knows which specific scaler to initiate.
Reference:
https://techcommunity.microsoft.com/blog/fasttrackforazureblog/container-apps-a-practical-scaling-with-azure-queue-scale-rule/3722075
NEW QUESTION # 34
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 # 35
......
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