AWS Certified Data Engineer - Associate DEA-C01Demo

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QuestionQ74

Data Store Management

A company consolidates high-frequency sensor telemetry in an Amazon S3 data lake. Every sensor stream produces structured records hourly. The records contain metadata including sensor category, unit ID, operational state, event timestamp, and site location. The data volume grows to millions of records per day. Each day, the company runs complex queries to identify performance insights for particular sensor categories.

Which solution will satisfy these requirements with the FASTEST query execution time?

  • A Persist the data in Apache ORC format. Partition the data by date. Sort the data by sensor category.
  • B Persist the data in CSV format. Partition the data by date. Sort the data by operational status.
  • C Persist the data in Parquet format. Partition the data by sensor category. Sort the data by date
  • D Persist the data in CSV format. Partition the data by date. Sort the data by sensor category.
Explanation

Partitioning by sensor category enables partition pruning for the recurring category-specific queries, so the engine reads only the relevant category data. Parquet is a columnar format optimized for analytical retrieval: it reduces I/O through column selection, compression, predicate pushdown, and parallel reads. Sorting by date supports efficient time-range filtering within each category partition. AWS documents that Parquet and ORC are optimized for fast retrieval and that partition keys should support the query patterns.

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