Transactional (OLTP) workloads center on many small data modifications that must succeed or fail as a single unit, so they depend on ACID properties such as atomic commit and strong consistency. The other options describe analytical patterns: large-scale scans and aggregations, schema-on-read storage of raw data in a data lake, or periodic batch refreshes for dashboards. These scenarios emphasize read-heavy analytics over write-intensive, consistency-critical operations and therefore are not typical of transactional workloads.
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Microsoft Azure Data Fundamentals DP-900
Describe core data concepts
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