Quick Note — Recommend a Solution for Data Analysis
AZ-305 › Unit 2 › Design data integration › Recommend a solution for data analysis
Quick Note — Recommend a Solution for Data Analysis
Analytics is the part of the Azure data platform that turns raw business events — transactions, clicks, sensor readings, application logs — into the dashboards, reports, and ML features that drive decisions. The catch on AZ-305 is that Azure offers four very different analytics engines whose marketing brochures all sound similar: Azure Synapse Analytics, Microsoft Fabric, Azure Databricks, and Azure Data Explorer. Choosing the wrong one quietly inflates cost by 3–10× and pushes the team into months of unnecessary rebuilding.
Retrieval target
- Recommend a Solution for Data Analysis
- Closed-book recall
- 5 minutes
- Open the full lesson after a miss
Decision anchors
| Prompt | Compact answer |
|---|---|
Azure Synapse Analytics | An end-to-end analytics service unifying data-warehouse SQL, big-data Spark, data integration, and serving in one workspace (Synapse Studio). Three runtime engines: dedicated SQL pool, serverless SQL pool, and Apache Spark pool. Storage is decoupled — usually ADLS Gen2. Pair with Synapse Pipelines for orchestration. |
| Synapse dedicated SQL pool | Formerly SQL Data Warehouse — a provisioned MPP cluster that scales compute via Data Warehousing Units (DWU). You can scale up/down or pause to stop compute billing (storage continues). Pick for predictable, large-volume star-schema warehousing where query latency matters and the load is sustained. Pay-per-DWU-hour. |
| Synapse serverless SQL pool | A pay-per-query SQL engine that runs T-SQL directly over files in ADLS Gen2 (Parquet, CSV, JSON, Delta). No infra to provision; default endpoint created with every Synapse workspace. Charged per TB scanned. Pick for ad-hoc exploration, lakehouse queries, and infrequent analytics — schema-on-read, no data loading. |
Azure Databricks | A fully managed Apache Spark + Delta Lake platform with notebooks, Databricks SQL, MLflow, and Unity Catalog. Two cluster modes: interactive (notebook-driven exploration, autoscale, idle-terminate) and job (transient, spun up per pipeline run). Tight integration with ADLS Gen2, Synapse, Power BI, Key Vault.… |
Read the answers once, then cover the right-hand column and reconstruct each one from the prompt. A useful answer names the requirement, the recommended control or service boundary, and the nearest alternative it rejects. If you can only recognize the answer after seeing it, retrieval is not yet secure.
Ninety-second explanation
Without notes, explain:
- What requirement signals this learning objective rather than a neighbouring one?
- Which two solution families are most likely to be compared?
- Which hard constraint eliminates the strongest distractor?
- What identity, network, data, or failure boundary must appear in the design?
- Which operational test would prove the recommendation works?
Then compare your explanation with the full lesson. Record the missing decision rule—not merely the missed product name—in your error log.
When to open the full lesson
Open the curriculum-linked lesson when you cannot explain a comparison, when a scenario depends on a numeric limit or SKU feature, or when the service is on a retirement path. Use current Microsoft Learn documentation for availability, limits, pricing, naming, and migration milestones; the quick note is intentionally compact.
Source and freshness
Derived from the linked AZ-305 lesson and retrieval deck, grounded in both attached course sources. Reviewed 2026-08-02. Current Microsoft documentation controls changing product contracts.