PDE Maintaining and Automating Data Workloads Practice Question
You want to optimize BigQuery costs for a large dataset that is frequently queried by time range. You also need to ensure that predictable workloads have dedicated slot capacity. Which TWO strategies should you combine? (Choose 2)
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
✓
Partition the table by date
Partitioned tables reduce bytes scanned by time-range queries. Committed use reservations provide predictable slot capacity at a discount. Autoscaling does not provide dedicated capacity; caching is automatic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use query caching
Why it's wrong here
Caching is automatic and does not require configuration; it's not a cost optimization strategy.
- ✓
Partition the table by date
Why this is correct
Partitioning by date limits the bytes scanned per query, reducing cost.
- ✓
Purchase committed use reservations for baseline capacity
Why this is correct
Committed use reservations provide dedicated slots at a lower cost for predictable workloads.
- ✗
Create a materialized view for the entire table
Why it's wrong here
Materialized views may help performance but are not specifically for cost reduction.
- ✗
Enable autoscaling slots
Why it's wrong here
Autoscaling adds slots on demand but does not provide dedicated capacity and costs more per slot.
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