AWS Certified Data Engineer Associate DEA-C01 Practice Question
Your company ingests about 2 TB of time-series sensor data into AWS every day. The data is append-only, must be retained for 3 years, and is accessed by data scientists through Amazon Athena for ad-hoc SQL that may scan an entire year of records. You need the most cost-effective storage configuration that still keeps query latency reasonable. Which solution meets these requirements?
Store Gzip-compressed CSV files in an Amazon S3 Standard-IA bucket and query them with Athena.
Stream the data into a time-series table in Amazon DynamoDB with a 3-year TTL and export to Athena when needed.
Write daily partitions of Parquet files to Amazon S3 with S3 Intelligent-Tiering enabled and query them with Athena.
Load the data each day into an Amazon Redshift RA3 cluster and run analytics there.
Storing the data as columnar Parquet files dramatically reduces the amount of data Amazon Athena must scan, lowering query latency and cost. Partitioning by day lets Athena prune partitions so that only the necessary objects are read. S3 Intelligent-Tiering keeps the data in a cost-optimized storage class without retrieval charges or performance penalties, so frequent analytical queries do not incur extra fees. Storing compressed CSV in S3 Standard-IA would add per-GB retrieval costs, Amazon Redshift would lock compute to storage and be more expensive for three years of rarely updated data, and DynamoDB is not designed for large-scale analytical scans via Athena.
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Why is Parquet preferred for analytical queries over CSV?
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What does S3 Intelligent-Tiering do and why is it beneficial here?
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Why is Amazon Athena a good choice for querying S3 data?
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What is the advantage of using Parquet files over CSV for analytical queries?
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How does S3 Intelligent-Tiering help optimize data storage costs?
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Why is partitioning data by day beneficial for Athena queries?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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