PDE Preparing and Using Data for Analysis Practice Question
Your team uses Looker to develop a model on top of BigQuery. The data is partitioned by ingestion time, and analysts frequently query the last 7 days. However, Looker queries are scanning the entire table, causing high costs. Which change should you implement?
⚠ Common exam trap
The question initially stated 'Pick two' but then contradicted itself, potentially confusing candidates. The correct answer is a single best approach.
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
✓
Apply a LookML access_filter to dynamically filter on the partition column.
The single best approach is to apply a partition filter requirement in LookML and enable partition pruning in BigQuery. Other options are not directly about Looker or are suboptimal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable BI Engine on the BigQuery table to accelerate queries.
Why it's wrong here
BI Engine accelerates queries but does not reduce the amount of data scanned; costs would still be high.
- ✓
Apply a LookML access_filter to dynamically filter on the partition column.
Why this is correct
Access filters in LookML can be used to restrict queries to a specific partition range, reducing full table scans.
- ✗
Create a materialized view that aggregates data daily and point Looker to that view.
Why it's wrong here
Materialized view could reduce scan but adds maintenance; the simplest is to enforce partition filter in LookML.
- ✗
Use clustering on the order_date column to improve query performance.
Why it's wrong here
Clustering helps with query performance but does not reduce the data scanned if the query still scans all partitions.
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