PDE Maintaining and Automating Data Workloads Practice Question
A company is migrating their on-premises data warehouse to BigQuery. They have a mix of batch and streaming ingestion. The data team wants to optimize query costs. Which THREE practices should they adopt?
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 materialized views for frequently executed aggregations.
Partitioning by date reduces bytes scanned. Using clustered tables improves performance for filter/join queries. Using materialized views can precompute aggregations and reduce scans. Flat-rate pricing is about reservation management, not cost optimization per query. Limiting slots is not a cost optimization; it may cause throttling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to flat-rate pricing to cap slot usage.
Why it's wrong here
Flat-rate pricing provides predictable costs but does not inherently optimize query costs; it may actually increase costs if not fully utilized.
- ✓
Use materialized views for frequently executed aggregations.
Why this is correct
Materialized views automatically refresh and are used by the query optimizer to speed up queries and reduce scanned bytes.
- ✓
Partition tables by a date or timestamp column.
Why this is correct
Partitioning allows queries to prune partitions, scanning fewer bytes and reducing costs.
- ✗
Limit the number of concurrent queries by setting a maximum slot capacity.
Why it's wrong here
Limiting slots may cause queries to run slower or fail, but does not reduce bytes scanned. It is not a cost optimization.
- ✓
Cluster tables on columns that are frequently used in filters and joins.
Why this is correct
Clustering improves the efficiency of queries by reducing the amount of data scanned when filtering on cluster columns.
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