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)

⚠ Common exam trap

The trap is picking autoscaling slots for 'predictable workloads' — autoscaling is for variable demand, while committed use reservations are the correct answer for dedicated, predictable baseline capacity.

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

Option B is correct because partitioning the table by date lets BigQuery prune partitions and scan only the date ranges a query touches, which directly reduces bytes processed and therefore cost for a dataset that is frequently queried by time range. Option C is correct because committed use reservations (capacity commitments) provide dedicated, predictable slot capacity at a discounted rate for steady baseline workloads, satisfying the requirement that predictable workloads have dedicated slots. Option A is not appropriate as a primary cost strategy here because query caching only helps when identical queries are repeated and results are still cached, which does not address time-range pruning or dedicated capacity. Option D is wrong because a materialized view over the entire table would be expensive to maintain and does not target the time-range access pattern. Option E is wrong because autoscaling slots add flexible, on-demand capacity rather than the dedicated, predictable capacity the scenario requires.

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
Query caching only reuses identical repeated results, so it cannot reserve dedicated slot capacity for predictable workloads; that requires committed-use or reservations. It is tempting because caching genuinely cuts bytes billed for repeated identical queries, and would be the right cost lever in a workload dominated by duplicate queries.
✓
Partition the table by date
Why this is correct
Partitioning by date restricts each query to the relevant date range, so BigQuery scans only matching partitions rather than the whole table. This directly cuts bytes processed for time-range queries, satisfying the cost-optimisation requirement in the stem.
✓
Purchase committed use reservations for baseline capacity
Why this is correct
Committed use reservations provide dedicated slot capacity at a discounted rate for predictable baseline workloads, satisfying the requirement for guaranteed capacity. This complements partitioning, which reduces bytes scanned by limiting queries to relevant date ranges.
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Create a materialized view for the entire table
Why it's wrong here
A materialised view spanning the entire table precomputes all rows, incurring full storage and refresh costs without exploiting the time-range filter pattern. Partitioning by the time column prunes scanned data for range queries; materialised views suit repeated aggregations over stable subsets, not whole-table scans.
✗
Enable autoscaling slots
Why it's wrong here
Autoscaling slots add capacity dynamically as demand rises, which suits unpredictable or spiky query workloads. Predictable workloads needing dedicated capacity require committed-use or reservations instead, since autoscaling provides no guaranteed slot allocation and cannot satisfy that requirement.

Go deeper

PDE Maintaining and Automating Data Workloads Practice Question

Why each option matters

Option-by-option breakdown

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