Data Store Management — AWS Data Engineer Associate practice questions

Which is TRUE of Redshift concurrency scaling?

1. It caches every query result in S3 for future dashboard hits
2. It replaces the primary cluster during a heavy write burst
3. It is only available with dc2 provisioned node types today
4. It adds transient read clusters billed per second when active

Answer: D — It adds transient read clusters billed per second when active

D) Concurrency Scaling adds transient read clusters billed per second (with a free-hour credit). B) The primary cluster is never replaced during scaling. C) Available on ra3 and Serverless too. A) Result caching is separate.

Which is the BEST layout for an Athena table storing 10 years of daily events?

1. Parquet files partitioned by year, month, and day with Snappy compression
2. One large CSV file per year stored in a flat prefix without partitioning
3. JSON documents grouped by user id in a single flat S3 prefix without partitions
4. Avro files with all events for the decade written into a single un-partitioned prefix

Answer: A — Parquet files partitioned by year, month, and day with Snappy compression

A) Columnar + time-partitioned is the canonical Athena layout for pruning. B) CSV is row-based and huge files are hard to prune. C) Un-partitioned JSON forces full scans. D) Avro is row-based and un-partitioned is worst-case.

Which is TRUE about S3 Intelligent-Tiering charges?

1. It charges a large per-object monitoring fee plus per-retrieval fees
2. It charges a small per-object monitoring fee but has no retrieval fees
3. It is free of charge for objects smaller than 128 KB with no restrictions
4. It waives the standard PUT and GET request fees for stored objects

Answer: B — It charges a small per-object monitoring fee but has no retrieval fees

B) A small per-object monitoring fee; no retrieval fees — this is Intelligent-Tiering's core value. A) Retrieval fees do not apply. C) Objects under 128 KB stay in Frequent Access but are not free. D) PUT/GET fees still apply.

Which BEST describes Amazon S3 Storage Lens?

1. A dashboard providing usage and activity trends across your S3 estate
2. A search index built on top of S3 objects for full-text queries
3. A billing tool that automatically transitions objects to Glacier when hot
4. A rewrite engine that converts CSV to Parquet on the fly at read time

Answer: A — A dashboard providing usage and activity trends across your S3 estate

A) Storage Lens gives account-wide S3 metrics on usage, activity, and cost. B) That is Athena or CloudSearch. C) That is lifecycle rules, not Storage Lens. D) That is not a Storage Lens function.

What does VACUUM REINDEX on a Redshift table do?

1. Re-analyzes interleaved sort keys and re-sorts the data
2. Refreshes materialized views built on top of the table
3. Rebuilds all foreign key constraints declared on the table
4. Reclaims deleted rows without touching sort order

Answer: A — Re-analyzes interleaved sort keys and re-sorts the data

B) That is REFRESH MATERIALIZED VIEW. A) VACUUM REINDEX re-analyzes an interleaved sort key so the sort order stays optimal. C) Redshift does not enforce foreign keys. D) That is VACUUM DELETE ONLY — a different mode.

Which combination lets you query an Iceberg table on S3 with the LEAST operational overhead?

1. Amazon Athena using the Iceberg SerDe with the Glue Catalog
2. EMR Serverless with Trino manually managing Iceberg metadata files
3. Redshift RA3 with COPY loading Iceberg snapshots into local storage
4. DynamoDB Streams processing Iceberg manifest events into OpenSearch

Answer: A — Amazon Athena using the Iceberg SerDe with the Glue Catalog

A) Athena has native Iceberg support via the Glue Catalog — no cluster to manage. B) EMR Serverless can query Iceberg but you still author Trino config. C) Loading defeats the point of Iceberg; also Redshift Spectrum supports Iceberg. D) DynamoDB has no relation to Iceberg querying.

For petabyte-scale ad-hoc Athena queries, which storage layout MOST reduces scan cost?

1. One large CSV file per day in a flat S3 prefix
2. JSON lines partitioned by hour with gzip compression
3. Parquet files partitioned by dt and compressed with Snappy
4. Avro files in a single un-partitioned prefix

Answer: C — Parquet files partitioned by dt and compressed with Snappy

A) Row-based CSV forces full-column scans; costs balloon on Athena. C) Columnar Parquet with partitioning enables column pruning and partition pruning — the two largest scan-cost levers. B) JSON is still row-oriented even with partitions. D) Avro is row-based and un-partitioned defeats pruning.

Which S3 tool BEST performs a bulk copy of billions of objects into a new bucket?

1. S3 Batch Operations with a manifest listing objects to copy
2. A single aws s3 cp command running interactively from a laptop
3. Manual object-by-object copy through the S3 console for each item
4. An S3 lifecycle rule copying objects to a new bucket on a schedule

Answer: A — S3 Batch Operations with a manifest listing objects to copy

A) Batch Operations copy billions of objects reliably with a manifest and CloudTrail audit. B) Laptop copies are fragile and slow. C) Console manual copy does not scale. D) Lifecycle rules do not copy across buckets.

Which is TRUE about Athena workgroups?

1. They automatically convert CSV tables to Parquet on the fly at query time
2. They provision dedicated Presto clusters reserved for the workgroup only
3. They replace IAM policies with a workgroup-level access control model
4. They isolate query results and enforce per-workgroup data-scanned limits

Answer: D — They isolate query results and enforce per-workgroup data-scanned limits

D) Workgroups isolate result locations and enforce per-workgroup scanned-byte and cost limits. B) Athena is serverless — no dedicated cluster. C) IAM still applies; workgroups add controls, not replace. A) No automatic conversion; use CTAS.

Which is TRUE about Iceberg schema evolution on S3?

1. Adding, dropping, or renaming columns is safe without rewriting Parquet data files
2. Every column addition requires a full rewrite of every Parquet file for the table
3. Only columns of type STRING are allowed to evolve after the initial CREATE TABLE
4. Schema evolution requires switching the table from Iceberg to Delta Lake first

Answer: A — Adding, dropping, or renaming columns is safe without rewriting Parquet data files

A) Iceberg's schema evolution is metadata-only for adds, drops, renames, and type promotion — no data rewrite. B) Metadata-only is the whole point. C) All types evolve. D) No format switch is needed.

Amazon Athena?

Serverless interactive SQL query on S3 data. Uses Glue catalog. Pay per TB scanned.

COPY command?

Fast bulk load into Redshift from S3/DynamoDB/Kinesis. Splits files across slices for parallelism.

Apache Iceberg vs Delta vs Hudi on AWS?

Open table formats over S3 with ACID, schema evolution, time travel. Iceberg has native support in Athena, Glue, EMR.

Compression for Parquet?

Snappy (default, fast) or zstd (better compression, slower). gzip works but slower decode.