AWS Certified Data Engineer Associate DEA-C01 Practice Question

A company stores call-center events in an Amazon Redshift cluster that receives new records every minute. Daily sales transactions are delivered as partitioned Parquet files in an Amazon S3 data lake that the company queries through Amazon Athena. A data engineer must build an Amazon QuickSight dashboard that contains visuals from both data sources. Call-center visuals must show data that is no more than 5 minutes old, but refreshing the sales visuals once per day is acceptable. The solution must keep query costs as low as possible while meeting the latency requirement. Which approach should the engineer take?

Import both the Redshift and Athena datasets into SPICE and schedule a refresh every 5 minutes for each dataset.
Use direct-query mode for both Redshift and Athena datasets and build all visuals directly against the sources.
Create a QuickSight dataset that uses Redshift in direct-query mode for the call-center data. Import the Athena sales dataset into SPICE and schedule a daily refresh. Build the dashboard from these two datasets.
Create an external table in Redshift Spectrum that joins the sales Parquet files to the call-center data, then use a single Redshift direct-query dataset in QuickSight for the dashboard.

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Answer Description

Direct-query datasets in QuickSight fetch data from the underlying source each time a user opens or refreshes a visual, providing near-real-time results but incurring query charges on every access. SPICE datasets ingest the data into QuickSight's in-memory engine, so subsequent queries do not hit the source and are therefore cheaper, but the data is only as recent as the last scheduled refresh (minimum 15-minute interval).

To meet the 5-minute currency requirement for the call-center data, the engineer should use a direct-query dataset that points to the Redshift cluster. For the sales data, near real-time access is not required, so importing the Athena dataset into SPICE and scheduling a once-per-day refresh avoids repeated Athena query costs. QuickSight can combine visuals from both datasets in the same dashboard, so this configuration meets the performance goal at the lowest ongoing cost.

The other options either refresh SPICE too frequently (increasing ingestion costs and still not meeting a 5-minute SLA), leave both datasets in direct query mode (generating unnecessary Athena charges), or force both data sources through Redshift (adding storage and query expenses without improving latency).

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