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Data Store ManagementeasyMultiple ChoiceObjective-mapped

DEA-C01 Data Store Management Practice Question

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and needs to be partitioned by year, month, day, and event type for efficient querying with Amazon Athena. Which S3 key prefix structure is most appropriate?

⚠ Common exam trap

AWS often tests the distinction between Hive-style partitioning (key=value) and flat or date-only prefixes, where candidates mistakenly choose a structure that does not support partition pruning or is incompatible with Athena's partition discovery.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet

Uses Hive-style partitioning (event_type=events/year=2024/month=01/day=01), which Athena and other query engines natively support. This structure allows Athena to perform partition pruning, reading only the relevant directories based on WHERE clause filters, significantly reducing data scanned and improving query performance. Option D also uses Hive-style partitioning but with a different order of partition keys (day, month, year). While still valid, this non-standard order may cause issues with automatic partition discovery when using MSCK REPAIR TABLE, which expects the partition order to match the table definition. Therefore, option C is the most appropriate because it follows the common convention of listing partitions from coarse to fine granularity (year > month > day) and can be easily loaded into Athena without additional configuration.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • s3://bucket/events/2024-01-01/event_type=data.parquet

    Why it's wrong here

    Flat date prefix without partition keys; Athena cannot prune partitions effectively.

  • s3://bucket/2024/01/01/event_type/events/data.parquet

    Why it's wrong here

    Directory structure mimicking file system but missing partition key-value pairs; Athena requires Hive-style for partition discovery.

  • s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet

    Why this is correct

    Correct Hive-style partitioning with logical key order (year, month, day) and event type, enabling efficient partition pruning.

  • s3://bucket/day=01/month=01/year=2024/event_type=events/data.parquet

    Why it's wrong here

    Uses Hive-style partitioning but with an unconventional key order (day, month, year, event_type). While Athena can still read this structure with proper table definitions, the non-standard order may complicate automatic partition discovery and is not the most appropriate choice. Option C is preferred because it follows the typical convention of coarse-to-fine partition order.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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