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Data Operations and SupporteasyMultiple ChoiceObjective-mapped

DEA-C01 Data Operations and Support Practice Question

Network Topology
$ aws s3api list-objectsbucket my-data-lakeprefix logs/2024/01/15/$ aws s3api head-objectkey logs/2024/01/15/app.logquery "Contents[?Size<`100`].Key"output textRefer to the exhibit.logs/2024/01/15/app.loglogs/2024/01/15/error.log"ContentLength": 50,"ContentType": "application/octet-stream","Metadata": {}

A data engineer is investigating why Amazon Athena queries on the 'my-data-lake' bucket are slow. The table is partitioned by year/month/day. The exhibit shows the objects in one partition. What is the MOST likely cause of poor query performance?

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

The files are too small, causing excessive read overhead

The exhibit shows tiny files (50 bytes), which cause excessive metadata overhead and read operations in Athena, leading to poor query performance. Option B (compression) is not indicated as the primary issue. Option C (partition columns) is likely appropriate given the partition structure. Option D (CSV format) is not necessarily the cause; while Parquet may improve performance, the main issue here is the file size, not the format.

Answer analysis

Option-by-option breakdown

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

  • The files are too small, causing excessive read overhead

    Why this is correct

    Many small files cause many S3 GET requests and slow performance.

  • The files are not compressed

    Why it's wrong here

    Compression helps but not the primary issue; tiny files are worse.

  • The partition columns are not appropriately chosen

    Why it's wrong here

    Partitioning by date is appropriate.

  • The data format is CSV instead of Parquet

    Why it's wrong here

    CSV is less efficient but not the main issue here.

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Same concept, more angles

1 more way this is tested on DEA-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A data engineer is troubleshooting a slow-running Amazon Athena query on a large dataset stored in S3. The query scans many small files. Which TWO actions can improve query performance?

medium
  • A.Increase the number of files to increase parallelism
  • B.Disable S3 server-side encryption
  • C.Concatenate small files into larger files
  • D.Partition the data by a frequently filtered column
  • E.Convert files from CSV to JSON

Why C: Concatenating small files into larger files reduces the overhead of file listing and task scheduling, improving query performance. Option D is correct because partitioning the data by a frequently filtered column allows Athena to use partition pruning, scanning only relevant partitions and reducing the amount of data read. Option A is incorrect because increasing the number of small files increases overhead and worsens performance. Option B is incorrect because disabling S3 server-side encryption does not affect query performance. Option E is incorrect because converting from CSV to JSON does not improve query performance; columnar formats like Parquet or ORC would be beneficial.

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.