Study Databricks DA-ASSOC Photon and Query Profile: key concepts, common traps, and exam decision cues.
When the exam says a query is slow, expensive, or unexpectedly behaved, start by asking what evidence Databricks gives you. Query analysis is about reading the right signals, not rewriting SQL blindly.
| Tool or concept | What it helps you understand |
|---|---|
| Query History | what ran, when it ran, and broad execution details |
| Query Profile | deeper execution behavior for a specific query |
| Query Insights | performance insight across problematic queries |
| Photon | execution engine associated with fast analytics workloads |
| caching | reuse that can reduce latency and repeated work |
| If the stem says… | Best first instinct |
|---|---|
| “find poorly performing queries” | query history, query profile, or query insights |
| “reduce latency for repeated analysis” | consider caching and better workflow, not random SQL edits alone |
| “understand Photon” | think supported fast analytics execution, not a governance feature |
| Trap | Better rule |
|---|---|
| rewriting SQL before inspecting execution evidence | look at history or profile first |
| treating Photon like a visualization or storage feature | Photon belongs to query execution behavior |
| assuming cache changes wrong answers into right ones | caching can help latency, not correctness |
Analysis questions usually begin with telemetry scope. If you need to know what ran and how it behaved at a high level, start with Query History. If the same work repeats and the issue is latency, think caching. DA-ASSOC usually rewards diagnosing from the analyst-facing surfaces before reaching for deeper engineering tooling.