How Should MWAA DAGs Orchestrate Daily and Archived S3 Transformations?
A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports. The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing. Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)
Community Votes
100% of anonymous learners picked answer AD. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests cost-aware service selection for mixed batch workloads orchestrated by MWAA; the trap is assuming Amazon Redshift or SageMaker is needed for transformation when transient EMR clusters already cover both the daily 300 GB and the one-time terabyte archive job.
This DEA-C01 scenario asks which two tasks an Amazon MWAA DAG should schedule to handle daily 300 GB transformations and a one-time terabyte-scale archive job in an S3 data lake at the lowest cost. The page establishes that AWS Glue crawlers (A) plus Amazon EMR (D) form the cost-effective combination, with a full breakdown of why Athena, Redshift and SageMaker fall short.
The most common wrong pick is Amazon Redshift (C) for the daily transformations, because candidates equate "reports" with a warehouse—yet a 300 GB daily batch does not justify a persistent Redshift cluster, and EMR handles both workloads more cheaply.
Community Discussion (5 comments)
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Expert Analysis
Why the Answer Is Correct
AWS Glue crawlers (A) are the right task for the daily 300 GB stream because the data arrives in a variety of formats and its schema must be discovered before any transformation; a crawler run costs only DPU-hours and writes table definitions into the Glue Data Catalog that the rest of the pipeline reuses. Amazon EMR (D) performs the actual transformations for both workloads—the daily 300 GB batch and the one-time terabyte-scale archive—using transient clusters that read directly from S3 and terminate when the job finishes. Because the company already orchestrates with Amazon MWAA, both tasks map cleanly to operators inside the same DAG such as GlueCrawlerOperator and EMR job-flow or step operators. Paying only for crawler DPU-hours and EMR job runtime is the cost-effective pattern for batch ETL on an S3 data lake, which is exactly what "MOST cost-effectively" signals. No persistent warehouse or ML endpoint is provisioned for work that finishes in a scheduled run.Why the Other Options Are Wrong
Option B proposes Amazon Athena to scan and identify the schema, but Athena is an interactive query engine billed per TB scanned; it does not maintain or update the Glue Data Catalog the way a crawler does, so it duplicates cataloging work at a higher price. Option C, Amazon Redshift, is a persistent data warehouse whose cluster runs and bills continuously—overkill for a 300 GB daily transformation and unnecessary because the question asks for transformations, not a serving layer for the reports. Option E, Amazon SageMaker, is built for training and hosting ML models, not general-purpose ETL of terabytes of archived data; using it for transformations would add ML infrastructure costs with no analytical benefit. None of B, C or E addresses both the daily and the one-time archive jobs with a single cost-efficient compute engine the way EMR does.Community Comment Notes
The learner vote is unanimous at 100 for AD, and the reasoning in the comments matches the exam doctrine. Ja13 explains that "AWS Glue crawlers are well-suited for scanning and identifying the schema of data in S3" and that EMR is cost-effective for large-scale work, while tgv adds that Glue simplifies metadata management and EMR offers flexibility and cost efficiency. andrologin summarizes the split neatly: "Glue crawlers for identifying the schema, EMR to run batch processing on the data." One comment simply cites ChatGPT as its source, which adds no technical weight, so the verdict rests on service behavior rather than on vote count.Official Reference
Exam Strategy
When a DEA-C01 question pairs MWAA orchestration with mixed batch sizes, default to schema discovery via Glue crawlers and compute via EMR transient clusters—pay-per-job beats always-on warehouses. Watch for the words "one-time" and "terabytes", which rule out a persistent Redshift cluster and ML-focused SageMaker.
Frequently Asked Questions
Why is Amazon Athena wrong for identifying the schema of the daily data?
Athena queries data per TB scanned and does not maintain a Data Catalog; AWS Glue crawlers update schemas in the catalog for DPU-hour pricing, which is cheaper for daily varied-format arrivals.
Why does Amazon EMR cover both the daily 300 GB and the one-time archive transformations?
Transient EMR clusters run Spark or Hive steps only for the job's duration and read directly from S3, so both workloads avoid a continuously running Redshift cluster or SageMaker training infrastructure.
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