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| Section | Weight | Objectives |
|---|---|---|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
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NEW QUESTION # 102
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 # 103
You develop an AI application that stores telemetry documents in Azure Cosmos DB for NoSQL.
Each document contains 30 properties. Queries filter only on deviceId and timestamp.
Write operations consume more RUs than expected.
You need to reduce RU consumption for write operations while maintaining query performance.
What should you do?
Answer: C
Explanation:
Modify the indexing policy to include only deviceId and timestamp paths, and exclude all other paths. By default, Azure Cosmos DB indexes every property in a document, which drastically inflates the Request Units (RUs) consumed during write operations.
Reference:
https://www.datacamp.com/tutorial/azure-cosmos-db
NEW QUESTION # 104
Your Azure AI Search index must support both exact keyword matches on product SKUs and semantic similarity search on product descriptions in a single query. What should you configure?
Answer: C
Explanation:
Hybrid search in Azure AI Search combines traditional keyword (BM25) search over text fields with vector similarity search over embedding fields in a single query, giving both exact-match precision (SKUs) and semantic recall (descriptions).
NEW QUESTION # 105
You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification. What should you implement?
Answer: C
Explanation:
Citation support requires passing document metadata (source name, page/section) through the retrieval step and prompting the model to reference that metadata explicitly in its answer -- this is a pipeline and prompt design pattern, not a sampling parameter.
NEW QUESTION # 106
A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts. What should you implement?
Answer: A
Explanation:
Grounding the agent in a retrieval source (Azure AI Search) and constraining it via system instructions to answer only from retrieved passages is the standard RAG pattern for reducing hallucination, since the model is anchored to verifiable source content rather than relying purely on parametric knowledge.
NEW QUESTION # 107
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