Case Study 1 - Fabrikam Inc.
Background
Fabrikam Inc. is a global retail analytics company that provides AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure.
The platform ingests transaction data, generates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security.
The solution must meet strict performance, scalability, and security requirements.
Current environment
Application architecture
The Recommendation engine is a customer-facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA).
Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
Semantic retrieval uses metadata filtering combined with vector similarity search.
Azure Managed Redis is used as a caching layer.
Front-end and API workloads are deployed to Azure Container Apps (ACA).
Batch model retraining workloads run in Azure Kubernetes Service (AKS).
Container and CI/CD
Container images are stored in Azure Container Registry (ACR).
CI/CD uses ACR Tasks to build images on commit.
ACA environments support revision management.
AKS workloads are deployed by using Kubernetes manifest files stored in Git.
Monitoring
Logs are collected in Azure Monitor.
Teams inspect container logs and Kubernetes events when troubleshooting.
Developers write KQL queries to analyze latency spikes.
Business requirements
Customer experience: Maintain a seamless, low-latency recommendation experience for end- users, even during unpredictable seasonal traffic spikes.
Operational cost efficiency: Minimize compute expenditures by deallocating resources during periods of inactivity and by preventing runaway scaling costs.
Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction.
Security and compliance: Adhere to a Zero Trust security model by eliminating long-lived credentials and centralizing the management of all sensitive secrets.
Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers.
Technical requirements
Performance: Semantic search latency must remain under 200 milliseconds at peak load.
Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints.
Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization.
Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete.
Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries upon metadata updates.
Identity: Use managed identities for all service-to-service and service-to-database authentication.
Plain-text credentials in configuration files are strictly prohibited.
Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy.
Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero.
CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits.
Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.
Drag and Drop Question
You need to configure the Redis integration for the Recommendation API.
Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Explanation:
Scenario, Application: Azure Managed Redis is used as a caching layer.
Box 1: Configure the Time to Live (TTL) on each cache key
You must configure the Time to Live (TTL) on each cache key to meet this requirement. Azure Managed Redis (like standard Redis) does not support a global, database-wide default TTL configuration for newly created keys.
Scenario: Technical requirements: Caching: Redis cache entries must expire automatically after
10 minutes.
Box 2: Publish invalidation events to a Redis channel
Publishing invalidation events to a Redis Pub/Sub channel is a highly effective, standard architecture to invalidate cache entries across microservices or application nodes. It establishes a reactive, event-driven backplane that ensures system data integrity without requiring tight coupling between your database updates and web servers.
Scenario: Technical requirements: Implement a reactive mechanism to invalidate cache entries upon metadata updates.
Box 3: Implement the cache-aside pattern with lazy loading
To meet your search latency target for recurring product requests, you should implement the cache-aside pattern with lazy loading.
Directly reduces latency: Lazy loading ensures that frequently requested product data is kept in memory (Redis), allowing subsequent recurring requests to bypass the slower database completely.
Optimizes memory usage: Data is only loaded into the cache when actually requested, preventing your Azure Managed Redis instance from filling up with rarely accessed product data.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/databases/architecture/write-through-caching-azure-sql-managed-redis
https://www.gigson.co/blog/what-is-redis-a-beginners-guide-to-in-memory-caching

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.

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

You are developing an AI application. The application configuration will depend on a dynamically retrieved value of a designated key stored in an Azure App Configuration resource.
You must deploy the application to the test, staging, and production environments.
You need to be able to set the value differently in each environment.
Which feature of Azure App Configuration resource should you use?

• A. Resource tags
• B. Labels
• C. Key prefixes
• D. Content types

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An AI application uses a database. The database credential rotates every 30 days.
The application currently requires a manual update each time the credential rotates.
You need to ensure that the application always uses the latest secret version without manual updates.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. Configure a Key Vault rotation policy.
• B. Retrieve secrets without specifying a version identifier.
• C. Cache the secret for 30 days.
• D. Retrieve secrets by specifying a version identifier.

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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?

• A. A vector-only index
• B. A hybrid search index combining vector fields and searchable text fields
• C. Two completely separate indexes queried independently
• D. A keyword-only index

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