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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Select appropriate Azure AI services - Plan security and compliance requirements |
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NEW QUESTION # 15
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must construct a vector similarity query that orders documents by similarity to the input embedding and returns only the top five results.
You need to construct the similarity query.
What should you do?
Answer: B
Explanation:
The query should limit the number of returned results by using TOP 5 together with ORDER BY VectorDistance(...) . Azure Cosmos DB for NoSQL provides the VectorDistance() system function for comparing a stored vector with an input query vector. Microsoft documents that a vector similarity query should order the results by VectorDistance so that the nearest matches are returned first.
A representative query is:
SELECT TOP 5
c.id,
VectorDistance(c.embedding, @embedding) AS SimilarityScore
FROM c
ORDER BY VectorDistance(c.embedding, @embedding)
Microsoft specifically recommends using a TOP N clause for vector search queries because returning more results than necessary increases RU consumption and latency. In this scenario, TOP 5 directly satisfies the requirement to return only the five most similar documents.
GROUP BY is used for aggregation and does not rank vectors by similarity. DISTINCT removes duplicate projected values but does not restrict results to the five nearest vectors. Offset pagination is unnecessary and can introduce additional query cost when only the highest-ranked five results are required.
Therefore, the correct action is to limit the number of results .
Study Guide references: Azure Cosmos DB for NoSQL # VectorDistance() ; vector search; ORDER BY ; TOP N ; RU and latency optimization.
NEW QUESTION # 16
You plan to deploy an Azure Container app.
You need to configure the container app to support session affinity.
Which ingress type and revision mode should you assign to the container app?
Answer: B
Explanation:
HTTP ingress type and single revision mode must be assigned. Session affinity (sticky sessions) requires an HTTP-based proxy layer to track and route client requests to the same replica, which is incompatible with raw TCP ingress. Single revision mode ensures standard traffic flow without multi-revision splitting complexity.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/ingress-environment-configuration
NEW QUESTION # 17
You are building a multi-agent solution in Azure AI Foundry where one agent handles scheduling and another handles billing questions, and a request may need both. What should you implement?
Answer: B
Explanation:
Multi-agent orchestration patterns use a coordinating (orchestrator) agent to decompose a request, delegate sub-tasks to specialized agents, and aggregate their outputs -- this is more maintainable and accurate than one broad prompt or model trying to cover disparate domains.
NEW QUESTION # 18
A recommendation system stores 1 million embeddings in Azure Database for PostgreSQL During peak hours. P95 similarity-query latency increases, and the cache hit ratio drops significantly.
You suspect that the vector index working set no longer fits in memory, resulting in more disk reads.
You need to scale server resources and validate the outcome.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 19
Hotspot Question
You develop an application that sells AI generated images based on user input. You recently started a marketing campaign that displays unique ads every second day.
Sales data is stored in Azure Cosmos DB with the date of each sale being stored in a property named 'whenFinished'.
The marketing department requires a view that shows the number of sales for each unique ad.
You need to implement the query for the view.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: count(c.whenFinished)
For the number of sales we need to use the count function.
Box 2: DateTimeBin(c.whenFinished, 'day', 2)
Box 3: DateTimeBin(c.whenFinished, 'day', 2)
Reference:
https://learn.microsoft.com/en-us/cosmos-db/query/datetimebin
NEW QUESTION # 20
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