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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement computer vision solutions |
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
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NEW QUESTION # 13
Hotspot Question
You are reviewing the Python tracing configuration for an application that must send distributed traces to Azure Monitor.
The following code configures OpenTelemetry tracing:
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:
Box 1: Yes
Yes, the code configures the tracer provider before any spans are created.
While the tracer provider initialization sequence is structurally correct, no spans can be captured yet at the moment the tracer instance is fetched because the processor and exporter are attached too late in the execution flow.
Box 2: No
No, this configuration does not export traces synchronously.
The code utilizes BatchSpanProcessor, which batches completed spans and exports them asynchronously on a background thread to prevent telemetry collection from blocking your main application code.
Box 3: Yes
Yes, this configuration successfully enables the export of distributed traces to Azure Monitor.
The code provided correctly initializes and wires up the native OpenTelemetry SDK components with the official Azure Monitor exporter library.
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/monitor-opentelemetry-exporter-readme
NEW QUESTION # 14
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
* Correlate failed requests with dependency calls during the last hour.
* Calculate the average dependency duration per operation.
You need to construct the KQL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
Answer: A,B,C
Explanation:
Use where , join , and summarize .
The where operator filters telemetry to the required scope, including records from the last hour and failed requests. For example, a predicate can restrict timestamp > ago(1h) and request success status in the same filtering expression.
Next, use join to correlate request telemetry with dependency telemetry. Application Insights assigns related telemetry a common operation_Id , and Microsoft specifically demonstrates joining the dependencies and requests tables on operation_Id to identify dependency calls associated with requests.
Finally, use summarize with avg(duration) grouped by the appropriate operation identifier or operation name.
summarize performs aggregation over groups and is therefore the required operator for calculating average dependency duration. Microsoft documents dependency duration as supporting an Avg aggregation.
extend is unnecessary because no derived column is required, while distinct merely removes duplicate rows and cannot calculate averages or correlate two telemetry tables.
A representative structure is:
requests
| where timestamp > ago(1h) and success == false
| join (dependencies | where timestamp > ago(1h)) on operation_Id
| summarize avg_dependency_duration = avg(duration1) by operation_Name
Study Guide references: Azure Monitor Application Insights; KQL where; telemetry correlation using operation_Id; join; summarize; dependency duration analysis.
NEW QUESTION # 15
You are developing an Azure Functions app.
All functions in the app meet the following requirements:
Run until either a successful run or until 10 run attempts occur.
Ensure that there are at least 20 seconds between attempts for up to 15 minutes.
You need to configure the host.json file.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 16
You need to detect whether an uploaded image contains adult or violent content before allowing it to be posted to a public forum. What should you use?
Answer: A
Explanation:
Azure AI Content Safety's image moderation capability is purpose-built to detect categories such as sexual, violent, self-harm, and hate content in images, returning severity scores you can act on.
NEW QUESTION # 17
You need to secure an Azure OpenAI resource so that it is only reachable from your virtual network and not from the public internet. What should you configure?
Answer: C
NEW QUESTION # 18
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