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Data Ingestion and TransformationhardMultiple ChoiceObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

An e-commerce company uses AWS Glue to process clickstream data from its website. The data is stored in Amazon S3 in partitioned Parquet format by date and hour. A recent increase in traffic has caused the Glue job to fail with 'Java heap space' errors. The job runs with 10 DPUs and uses Spark's default configurations. The data engineer needs to resolve the memory issue without modifying the ETL script. What should the data engineer do?

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

Increase the Spark configuration 'spark.sql.shuffle.partitions' to 500.

Increasing 'spark.sql.shuffle.partitions' to 500 reduces the amount of data handled per partition during shuffle operations, which alleviates memory pressure and prevents 'Java heap space' errors. This is a configuration change that does not require modifying the ETL script. Option A is wrong because decreasing partitions increases data per partition, worsening memory issues. Option B is wrong because changing worker type to G.1X (which has more memory per executor) might help but does not address the root cause if the issue is due to too few shuffle partitions; it is also not a direct fix for shuffle memory. Option D is wrong because increasing DPUs adds more executors but does not solve the per-executor memory issue caused by large shuffle partitions.

Answer analysis

Option-by-option breakdown

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

  • Decrease the Spark configuration 'spark.sql.shuffle.partitions' to 50.

    Why it's wrong here

    Fewer partitions increase data per partition, worsening memory issues.

  • Change the worker type to G.1X.

    Why it's wrong here

    G.1X is the default; it may not solve the shuffle memory issue.

  • Increase the Spark configuration 'spark.sql.shuffle.partitions' to 500.

    Why this is correct

    More shuffle partitions reduce the size of data per partition, mitigating memory issues.

  • Increase the number of DPUs to 20.

    Why it's wrong here

    More DPUs add more executors, but the heap space issue may still occur in each executor.

Visual reference

Client Recursive Resolver Root DNS (13 root servers) TLD DNS (.com, .org, …) Authoritative example.com query IP addr answer

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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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.