hardMultiple Select
Google PCA Practice Question: A company runs a batch analytics job every hour…
A company runs a batch analytics job every hour on BigQuery. The job processes terabytes of data and the results are stored in Cloud Storage. The job must complete within 30 minutes. Which TWO actions can reduce query execution time? (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
✓
Use a partitioned table based on the timestamp column
Option B is correct because partitioning the table on the timestamp column lets BigQuery prune irrelevant partitions, so each hourly run scans only the data for the relevant time range instead of the full multi-terabyte table, dramatically reducing bytes processed and execution time. Option E is correct because increasing the number of BigQuery slots allocated to the project provides more compute capacity (slots) for the query, allowing BigQuery to parallelize the work across more workers and finish the batch job faster. Option A is not reliable here because cached results are invalidated whenever the underlying tables change, and an hourly job over freshly arriving data will almost never hit a valid cache. Option C is wrong because legacy SQL is a deprecated dialect with no performance advantage over GoogleSQL. Option D is wrong because querying data in Cloud Storage via an external table typically performs worse than querying native BigQuery storage, since it lacks BigQuery's columnar storage and optimization.
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 cached results from the previous run
Why it's wrong here
Cached results are only reused when the query text and underlying tables are unchanged, so a recurring hourly job reading freshly loaded data will not hit the cache. It is tempting because caching genuinely accelerates repeated identical queries over static data, which is not this scenario.
- ✓
Use a partitioned table based on the timestamp column
Why this is correct
Partitioning on the timestamp column lets BigQuery prune irrelevant partitions, scanning only the hour's data rather than the full table. This directly cuts the bytes read, which is the dominant cost driver for a terabyte-scale batch job, helping the query finish inside the 30-minute window.
- ✗
Convert the query to use legacy SQL
Why it's wrong here
Legacy SQL lacks BigQuery's columnar and standard-SQL optimisations, so it cannot exploit the query planner improvements that cut scan time on terabyte datasets. It is tempting because legacy SQL suits quick ad-hoc queries on small, flat tables, but it does not reduce execution time for large analytical jobs.
- ✗
Export the data to Cloud Storage and query with an external table
Why it's wrong here
External tables read data from Cloud Storage at query time, adding network and parsing overhead rather than reducing execution time; BigQuery's columnar storage is faster to scan. It is tempting because external tables avoid loading data, but that suits infrequently queried or externally managed datasets, not a 30-minute terabyte batch deadline.
- ✓
Increase the number of BigQuery slots assigned to the project
Why this is correct
Adding slots increases the compute capacity available to the query, allowing more parallel workers to process the terabyte-scale scan. This shortens execution time when the job is slot-constrained, helping it finish within the 30-minute requirement.
Go deeper
Related to this question
Learn chapter
Data Migration and Transfer Services
Key term
Column
A column is a vertical set of values in a database table that stores one specific type of attribute for every row.
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
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