DP-203 Develop data processing Practice Question
You are designing a batch processing pipeline in Azure Databricks. The data is stored in Delta Lake and you need to perform a time-series join between two tables: 'events' (100 billion rows) and 'sessions' (10 billion rows). The join condition is on 'device_id' and a timestamp range (event_time BETWEEN session_start AND session_end). Which join strategy would be most efficient?
⚠ Common exam trap
Microsoft often tests the misconception that broadcasting a large table is acceptable if it fits in memory, but the trap here is that candidates overlook the driver memory limit and assume broadcast join scales linearly, while the correct answer requires understanding Delta Lake's specialized range join optimization for time-series data.
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
✓
Use a range join with interval threshold using Delta Lake's optimized join.
Delta Lake's optimized range join leverages interval threshold pruning and data skipping to efficiently handle time-series joins on large datasets. This strategy avoids full shuffles by using min/max statistics and Bloom filters to eliminate non-matching partitions, making it far more efficient than generic join methods for 100B and 10B row tables.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Broadcast the smaller table (sessions) to all nodes.
Why it's wrong here
Sessions table is huge (10B rows), broadcasting it would cause memory issues.
- ✓
Use a range join with interval threshold using Delta Lake's optimized join.
Why this is correct
Delta Lake supports range join optimization with interval thresholds, reducing data shuffle.
- ✗
Use a sort-merge join by repartitioning both tables on device_id.
Why it's wrong here
Sort-merge join still shuffles both tables, which is expensive.
- ✗
Bucket both tables on device_id with 500 buckets.
Why it's wrong here
Bucketing helps equi-joins but not range conditions on timestamp.
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