Databricks-DA-Assoc Analyzing Queries Practice Question
A data analyst is reviewing a Databricks SQL query that runs slowly and opens the Query Profile. The analyst wants to identify whether an individual task is disproportionately slow compared with its peers, indicating skew. Which TWO areas of the Query Profile should the analyst examine? (Choose two.)
⚠ Common exam trap
The trap here is blaming cluster size for a slow stage, when skew is a data distribution problem that adding executors cannot solve.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The number of rows and bytes written by each shuffle partition
Skew manifests as an individual task running far longer than its peers because one partition holds disproportionate data. Task duration distribution within a stage exposes the straggler, while per-partition shuffle write rows and bytes show the imbalanced partition that causes it. Together these two views confirm skew rather than a general resource shortage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The version of the Databricks Runtime used by the warehouse
Why it's wrong here
Runtime version determines available features and performance characteristics across the board, but it is not a diagnostic signal for a single straggling task. Two queries on the same runtime can differ entirely in skew behavior, so runtime version cannot explain why one task within a stage runs far longer than its peers.
- ✓
The number of rows and bytes written by each shuffle partition
Why this is correct
Per-partition shuffle write sizes reveal imbalance directly: if one partition receives vastly more bytes or rows than its peers, the downstream task processing it will run long. Comparing these counts across partitions confirms skew at the shuffle boundary and points to the join or grouping key that is unevenly distributed.
- ✗
The SQL warehouse cluster size configured for the query
Why it's wrong here
Warehouse cluster size is a configuration setting that affects overall parallelism and cost, not a per-task diagnostic. Increasing cluster size adds executors but does not fix skew, because the oversized partition still runs on a single task; the analyst needs task-level and partition-level evidence before deciding whether resizing helps.
- ✗
The total number of stages in the physical plan
Why it's wrong here
Stage count reflects how many shuffle boundaries the plan introduces, which speaks to overall plan structure rather than per-task imbalance. A query can have many stages with perfectly even task durations, and a skewed query can have few stages, so stage count alone cannot reveal that one task is disproportionately slow.
- ✓
The duration distribution of tasks within a single stage
Why this is correct
The task duration distribution for a stage shows whether one or a few tasks run far longer than the median. A long tail where a single task dominates the stage runtime is the classic signature of skewed data, because one partition holds many more rows than the others and becomes the bottleneck for the whole stage.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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