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Data Ingestion and Transformation →hardMultiple ChoiceObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial services company ingests stock trade data from multiple exchanges into an Amazon S3 bucket (trade-bucket). Each exchange sends a CSV file every 5 minutes. The data must be transformed into Parquet format and partitioned by exchange and date (trade_date) for efficient querying using Amazon Athena. The pipeline must handle late-arriving data (files up to 2 hours late) and ensure exactly-once processing to avoid duplicates. Currently, a scheduled AWS Glue ETL job runs every hour, reads new CSV files, converts them to Parquet, and writes to an output bucket. However, the team is experiencing data duplication: if the job fails midway, upon retry it reprocesses all files in the input folder, causing duplicates in the output. Additionally, the job takes too long because it scans all files each run. The engineer must redesign the pipeline to eliminate duplicates and improve efficiency. What should the engineer do?

A
Use AWS Glue Workflows to orchestrate the job and add a condition to check for duplicates before writing.
Why wrong: Glue Workflows do not provide file-level idempotency; duplicate detection would require additional logic and scans.
B
Set up an S3 event notification to invoke an AWS Lambda function that starts a Glue job with a parameter containing the S3 object key of the new file; modify the Glue job to process only that file and use the file key to avoid duplicates.
This ensures each file is processed exactly once, and the job runs only on new files, improving efficiency.
C
Modify the Glue job to move processed CSV files to an archive folder after successful transformation, and process only unprocessed files.
Why wrong: Moving files does not guarantee exactly-once if the job fails after moving some files; also, moving files adds latency and cost.
D
Replace Glue with Amazon EMR and use Spark Structured Streaming with checkpointing to process files incrementally.
Why wrong: EMR is more complex to manage and may be overkill; checkpointing in streaming would still need file tracking.

Full breakdown with real-world context →

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

✓

Set up an S3 event notification to invoke an AWS Lambda function that starts a Glue job with a parameter containing the S3 object key of the new file; modify the Glue job to process only that file and use the file key to avoid duplicates.

Option B is the best approach. By setting up an S3 event notification to invoke a Lambda function that triggers a Glue job with the new file's S3 key as a parameter, the job processes only that specific file. Using the file key in the job logic ensures idempotency—if the job fails and retries, it reprocesses the same file key, and deduplication can be handled (e.g., by checking if the output partition already contains that file's data or using a job bookmark on the file key). This achieves exactly-once processing and incremental processing (no full scans), improving efficiency. Option A (Glue Workflows) still processes all files each run and doesn't prevent duplicates if a file arrives after the job starts. Option C (moving CSV files to an archive folder) risks race conditions if late-arriving data comes while the job is running, and does not guarantee exactly-once if the job fails mid-way. Option D (EMR with Spark Structured Streaming) is overly complex and expensive for a 5-minute CSV batch ingestion; checkpointing can handle failures but adds significant operational overhead.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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 Workflows to orchestrate the job and add a condition to check for duplicates before writing.
Why it's wrong here
Glue Workflows do not provide file-level idempotency; duplicate detection would require additional logic and scans.
✓
Set up an S3 event notification to invoke an AWS Lambda function that starts a Glue job with a parameter containing the S3 object key of the new file; modify the Glue job to process only that file and use the file key to avoid duplicates.
Why this is correct
This ensures each file is processed exactly once, and the job runs only on new files, improving efficiency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Modify the Glue job to move processed CSV files to an archive folder after successful transformation, and process only unprocessed files.
Why it's wrong here
Moving files does not guarantee exactly-once if the job fails after moving some files; also, moving files adds latency and cost.
✗
Replace Glue with Amazon EMR and use Spark Structured Streaming with checkpointing to process files incrementally.
Why it's wrong here
EMR is more complex to manage and may be overkill; checkpointing in streaming would still need file tracking.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

What to study next

Got this wrong? Here's your next step.

Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Set up an S3 event notification to invoke an AWS Lambda function that starts a Glue job with a parameter containing the S3 object key of the new file; modify the Glue job to process only that file and use the file key to avoid duplicates. — Option B is the best approach. By setting up an S3 event notification to invoke a Lambda function that triggers a Glue job with the new file's S3 key as a parameter, the job processes only that specific file. Using the file key in the job logic ensures idempotency—if the job fails and retries, it reprocesses the same file key, and deduplication can be handled (e.g., by checking if the output partition already contains that file's data or using a job bookmark on the file key). This achieves exactly-once processing and incremental processing (no full scans), improving efficiency. Option A (Glue Workflows) still processes all files each run and doesn't prevent duplicates if a file arrives after the job starts. Option C (moving CSV files to an archive folder) risks race conditions if late-arriving data comes while the job is running, and does not guarantee exactly-once if the job fails mid-way. Option D (EMR with Spark Structured Streaming) is overly complex and expensive for a 5-minute CSV batch ingestion; checkpointing can handle failures but adds significant operational overhead.

What should I do if I get this DEA-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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