PDE Preparing and Using Data for Analysis Practice Question
A data engineer needs to build a real-time dashboard in Looker Studio that displays live sales data from BigQuery. The dashboard must refresh every minute. The underlying BigQuery table is updated continuously via streaming inserts. Which two approaches can reduce query cost and latency? (Choose TWO)
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 BigQuery BI Engine to accelerate the Looker Studio queries
BI Engine can accelerate queries in Looker Studio by caching data in memory. Partitioning the table on the date column reduces the amount of data scanned.
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 the BigQuery Data Transfer Service to copy data to a separate dataset
Why it's wrong here
Data Transfer Service is for batch scheduled transfers, not real-time.
- ✗
Create a materialized view that pre-aggregates the data
Why it's wrong here
Materialized views have limitations with streaming inserts (they are eventually consistent).
- ✗
Schedule a script to export the table to Cloud Storage and load into Cloud SQL
Why it's wrong here
This adds latency and complexity, not suitable for real-time.
- ✓
Use BigQuery BI Engine to accelerate the Looker Studio queries
Why this is correct
BI Engine provides in-memory analysis for sub-second query response.
- ✓
Partition the BigQuery table by the date column
Why this is correct
Partitioning reduces scanned bytes for queries filtered by time.
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