Microsoft Azure Data Fundamentals DP-900 Practice Question
A manufacturing company needs its monitoring solution to raise safety alerts within seconds whenever temperature sensors on a production line exceed a critical threshold. Longer term analytics reports can run overnight. Which data processing approach best meets the alerting requirement?
An hourly micro-batch process that groups sensor readings before analysis.
A nightly batch job that aggregates the day's sensor files.
A weekly ETL batch that loads sensor data into a data warehouse.
Stream (real-time) processing of the incoming telemetry.
Streaming (also called real-time) processing is designed to handle a continuous flow of events with very low latency, so it can evaluate each sensor reading almost immediately and trigger an alert if a threshold is breached. Batch processing-whether hourly, nightly, or weekly-collects data over a period and processes it later, so it cannot satisfy the sub-second or near-real-time responsiveness required for safety alerts. Therefore, streaming is appropriate for the alerting scenario, while batch jobs remain suitable for the separate historical reporting task. Options describing overnight, weekly, or manual ETL jobs are examples of batch workloads and would not meet the company's immediacy goal.
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Microsoft Azure Data Fundamentals DP-900
Describe an analytics workload
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