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| Section | Objectives |
|---|---|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Connect to and consume Azure services | - Integrate Azure services
|
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NEW QUESTION # 25
You are building a semantic search feature for a chatbot. You store document embeddings in Redis.
You review the following Python code that connects to Redis and stores an embedding value:
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:
Verified answer: Yes; No; Yes.
Detailed Explanation: `HSET` stores the binary embedding as a field in the Redis hash keyed by `doc:1`, so the first statement is true. The code does not create a RediSearch/Redis Query Engine vector index or define a vector field schema; merely storing bytes does not enable similarity search, so the second statement is false.
`EXPIRE doc:1 600` sets a 600-second lifetime, which is ten minutes, making the third statement true.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector search in Azure Managed Redis
NEW QUESTION # 26
You store embeddings in Redis by using keys formatted as doc:(id). Some embeddings are accessed frequently. Others are rarely used.
You need to implement a caching strategy that keeps only frequently accessed embeddings in memory.
What should you use?
Answer: C
Explanation:
To implement this strategy, you should use allkeys-lru combined with the EXPIRE command as a secondary fallback.
Primary Mechanism: Configure your Redis maxmemory-policy to allkeys-lru.
Secondary Mechanism: Apply the EXPIRE command to your keys as a safety net.
Why allkeys-lru is the Best Choice
Memory Management: It automatically evicts the Least Recently Used (LRU) keys across your entire dataset when Redis hits its memory limit.
Frequent Access: It guarantees that frequently accessed embeddings stay in memory, regardless of when they were created.Prefix Independent: It scans all keys, making it perfect for your doc:(id) format Reference:
https://rahulchowdhury.in/blog/redis-caching-patterns-every-mern-dev
NEW QUESTION # 27
Hotspot Question
You are developing several microservices to run on Azure Container Apps.
You need to monitor and diagnose the microservices.
Which features should you use? To answer, select the appropriate feature in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Log streaming
To view console logs from an Azure Container App in near real-time, you should use the Log stream feature.
How to Access Log Stream
Navigate to your container app in the Azure portal.
Under the Monitoring section of the sidebar menu, select Log stream.
Set the log type to Console to instantly stream your application's stdout and stderr streams.
Box 2: Container console
To debug your microservice from inside the container, you should use the Container console feature.
Key Features for Debugging and Monitoring
Container console: This feature allows you to connect directly to the Linux console/shell inside your running container instance. You can use it to execute commands, inspect files, and troubleshoot the container's environment in real-time.
Log stream: This tool is used alongside the console to view streaming system and console logs from your container in near real-time.
Azure Monitor Logs: Used for querying and analyzing historical application and system logs over longer periods.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/observability
NEW QUESTION # 28
You need to configure vector embedding updates according to the business and technical requirements.
Which information should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Identify document changes: Change feed processor. Scale out vectorization processing:
Lease container.
Detailed Explanation: The change feed processor is designed to react to inserts and updates in a monitored Cosmos DB container, which directly matches the requirement to generate embeddings for new or changed documents. Its lease container stores processing state and coordinates work across multiple workers, allowing the processing workload to scale out without duplicating ownership of change-feed ranges. A periodic full scan would consume unnecessary RUs, and neither strong consistency nor container RU throughput is the coordination mechanism for distributed change-feed workers.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Azure Cosmos DB change feed processor
NEW QUESTION # 29
You develop an application that sells Al 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 06 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 grouped into two- day periods 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:
NEW QUESTION # 30
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