Semi-Structured, Unstructured and Integration Design
This topic covers Cosmos DB, Blob Storage, Data Lake Storage, files, queues, analytical stores, data integration, durability, performance, and lifecycle design.
How to study for AZ-305
Treat each AZ-305 item as an architecture decision: identify the workload requirement, governance boundary, data shape, recovery objective, and infrastructure tradeoff before choosing.
Core concepts
Concept 1
Semi-Structured, Unstructured and Integration Design questions test Azure architecture tradeoff decisions rather than isolated service-name recall.
Exam cue: Identify the requirement, workload type, data shape, access boundary, recovery target, and operational ownership model.
Concept 2
The best answer maps business requirements to identity, governance, data, resilience, compute, network, security, cost, and operations constraints.
Exam cue: Choose the Azure design that satisfies the constraint with the least unnecessary operational burden.
Concept 3
Eliminate answers that ignore governance scope, recovery objectives, data durability, private connectivity, security boundaries, or workload fit.
Exam cue: Prefer Well-Architected choices: secure, reliable, cost-aware, observable, governed, and scalable.
Risk pitfalls and guardrails
Choosing a service before identifying the business driver and nonfunctional requirement.
Guardrail: Avoid answers that pick services without checking governance scope, data model, recovery target, migration dependency, or network boundary.
Solving availability while ignoring identity, data protection, compliance, or cost constraints.
Guardrail: Avoid answers that pick services without checking governance scope, data model, recovery target, migration dependency, or network boundary.
Assuming a migration, networking, or storage pattern is correct without checking workload dependencies.
Guardrail: Avoid answers that pick services without checking governance scope, data model, recovery target, migration dependency, or network boundary.
Memory anchors
Azure Cosmos DB
Azure Cosmos DB is a globally distributed NoSQL database with multiple APIs and tunable consistency.
Partition Key
A Cosmos DB partition key distributes data and workload across logical partitions.
Consistency Level
Cosmos DB consistency levels trade off latency, availability, and read consistency.
Blob Storage
Azure Blob Storage stores massively scalable unstructured object data.
Data Lake Storage
Azure Data Lake Storage supports analytics workloads with hierarchical namespace capabilities.
Lifecycle Management
Lifecycle management moves or deletes blob data based on rules and age.
Storage Redundancy
Storage redundancy protects data within a region, zone, or paired region depending on option.
Data Integration
Data integration moves, transforms, or analyzes data across systems for reporting and analytics.
Hot Cool Archive
Blob access tiers balance access frequency, retrieval time, and storage cost.
Analytical Store
An analytical store supports analytics over operational data without heavy impact on transactional workloads.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
You must store JSON documents with single-digit-millisecond reads at global scale and multi-region writes. Which service fits?
Your Cosmos DB workload needs the strongest guarantee that reads always return the most recent committed write globally. Which consistency level fits?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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