You deploy an AI application across multiple Azure regions.The application must be able to view writes across all regions within a predictable time window.You need to determine the appropriate consistency level.What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution.Note: Each correct selection is worth one point.
Answer(s): B,C
The two appropriate consistency levels for a globally distributed AI application needing to view writes within a predictable time window are Bounded Staleness and Strong.Bounded Staleness: This provides a guarantee that reads will lag behind writes by a user-specified "staleness window" (either in time or number of operations). It delivers high availability and predictable read consistency across all regions, balancing performance with strict read-order guarantees.Strong: This provides linearizable and absolute consistency, ensuring that writes are seen synchronously across all regions. It features an exact zero-second predictable time window, though it comes at the expense of higher latency for multi-region operations due to the time required to replicate globally.
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
HOTSPOT (Drag and Drop is not supported)You are developing an event-driven system that reacts to new or updated documents in Azure Cosmos DB for NoSQL.The application must process new or updated documents as they appear in the container by using a change feed processor. The application must also maintain processor state between runs.You need to configure the components required for stateful change feed processing.Which components should you configure? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: Monitored container. To read new or updated documents from the source, you need to configure the monitored container. The change feed processor listens to this container for any changes and sequentially retrieves the sorted list of modified documents.Box 2: Lease Container Lease Container is a separate container that acts as state storage. It maintains processing checkpoints across multiple worker instances, which allows your system to resume operations from where it left off.Box 3: Latest version mode To process new or updated documents, you need to use the Latest version mode of the change feed. Latest version mode is the default change feed mode. It provides only the most recent version of each changed item (inserts and updates) in the exact order they were modified. If an item is updated multiple times in quick succession, the processor will only receive the final state after the latest update.
https://learn.microsoft.com/en-us/azure/cosmos-db/change-feed-processor
HOTSPOT (Drag and Drop is not supported)You are developing an application that uses a Python API to perform similarity queries against Azure Database for PostgreSQL. The application creates a new database connection for every request.During peak traffic, the application intermittently fails to open new database sessions. Logs indicate that the maximum number of connections have been reached.You need to configure the connection pooling strategy to reduce connection setup overhead and maximize reuse for the high-concurrency workload.What should you configure? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: PgBouncer To resolve the connection exhaustion issue, you should use the built-in PgBouncer connection pooler configured in Transaction Pooling mode.Because PostgreSQL uses a process-per-connection architecture, opening and closing a new session for every API request causes massive CPU/memory overhead and rapidly drains available slots during traffic spikesBox 2: Transaction Pooling mode
https://learn.microsoft.com/en-us/azure/postgresql/connectivity/concepts-connection-pooling-best-practices
HOTSPOT (Drag and Drop is not supported)You are developing a Retrieval-Augmented Generation (RAG) solution for a company.AI responses and embedding vectors are cached in Redis.The solution must meet the following requirements:-AI responses must expire exactly 24 hours after they are cached.-Cached embeddings must always reflect the current source data.You need to configure Redis to meet the requirements.Note: Each correct selection is worth one point.Hot Area:
Box 1: Set a Time for Live (TTL) on each key. To configure Redis to expire your AI responses exactly 24 hours after caching, you must apply a Time-To-Live (TTL) of 86,400 seconds to the specific keys holding those responses.Box 2: Delete related keys when the document changes You must invalidate or update those cached keys when source data changes to prevent your RAG system from retrieving stale information.Incorrect: Configuring allkeys-lru (Least Recently Used) is not recommended to ensure your cached embeddings or AI responses reflect current source data.allkeys-lru is an eviction policy. It only deletes data when Redis reaches its maximum memory limit, and it targets keys that haven't been accessed recently. It does not verify if the underlying source data has changed, meaning users will get dangerously outdated (hallucinated) AI responses if the source document is updated but the cached response remains in memory.
https://zenvanriel.com/ai-engineer-blog/ai-caching-strategies/
HOTSPOT (Drag and Drop is not supported)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.Hot Area:
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.
https://redis.io/docs/latest/develop/ai/search-and-query/vectors/
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