Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You manage a Fabric Eventstream that ingests 100,000 telemetry events per second from IoT devices into an Eventhouse table named Telemetry. Analysts complain that KQL queries filtered to the most recent hour take several seconds to return. Investigation shows that ingestion generates thousands of small extents (<50 MB). Without provisioning additional capacity, which configuration change is most likely to improve both ingestion efficiency and query performance?
Reduce the table data retention period from 30 days to 7 days.
Enable delta ingestion mode on the Eventstream output to the Telemetry table.
Increase the target extent size by editing the ingestion batching policy for the Telemetry table.
Create a materialized view that summarizes the Telemetry table every five minutes.
Eventhouses are backed by the Azure Data Explorer engine, which stores data in compressed columnar extents. When ingestion creates many small extents, each query must touch a large number of files, increasing both scan time and CPU overhead. By increasing the target extent (batch) size through the table's ingestion batching policy, the service accumulates more data before committing an extent, resulting in fewer, larger extents. This reduces the number of extents that queries must open and also improves compression efficiency, giving better overall performance without additional compute. Shortening retention only affects how long data is kept, not how it is laid out. Materialized views help for pre-aggregated workloads but do not accelerate raw-data queries that filter on recent time ranges. "Delta ingestion mode" is not a supported optimization in Fabric Eventstreams/Eventhouses.
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Microsoft Fabric Data Engineer Associate DP-700
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