The correct approach is to use AWS Glue DynamicFrames to read the CSV files and apply transformations with resolveChoice and applyMapping. This works because DynamicFrames operate on a schema-on-read basis, meaning they infer the structure of each file at runtime and can handle columns that appear or disappear across different CSV files, unlike rigid Spark DataFrames which require a predefined schema. The resolveChoice method lets you explicitly decide how to handle conflicting data types or missing columns, while applyMapping standardizes the output into a consistent Parquet format. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of schema evolution in ETL pipelines—a common trap is assuming crawlers handle transformation logic or that explicit schema mapping can adapt to changing schemas. Remember the memory tip: “DynamicFrames resolve on the fly, DataFrames need a schema to tie.”
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A healthcare company is ingesting patient data from a legacy system into an Amazon S3 data lake using AWS Glue. The legacy system produces CSV files with inconsistent schemas (columns may appear or disappear in different files). The data engineer needs to create a Glue ETL job that can handle schema evolution and transform the data into a standardized parquet format. The job should also be able to process new files as they arrive. Which approach should the data engineer use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
Option C is correct because Glue DynamicFrames can handle schema evolution by allowing schema-on-read and resolving schema inconsistencies. Option A is wrong because crawlers are for cataloging, not ETL. Option B is wrong because explicit schema mapping would fail with evolving schemas. Option D is wrong because Spark DataFrames require a predefined schema.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use AWS Glue crawlers to create a schema in the Data Catalog and then use a standard Spark DataFrame for transformation.
Why it's wrong here
Crawlers may not handle schema evolution well.
✓
Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
Why this is correct
DynamicFrames support schema evolution.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use a Python shell job in Glue to manually parse each file and write to parquet.
Why it's wrong here
Python shell is less efficient and not recommended for ETL.
✗
Use a Glue ETL job with a static schema defined in the script and ignore files that don't match.
Why it's wrong here
This would cause data loss.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping. — Option C is correct because Glue DynamicFrames can handle schema evolution by allowing schema-on-read and resolving schema inconsistencies. Option A is wrong because crawlers are for cataloging, not ETL. Option B is wrong because explicit schema mapping would fail with evolving schemas. Option D is wrong because Spark DataFrames require a predefined schema.
What should I do if I get this DEA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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