- A
Configure the COPY command with a fixed delimiter (e.g., comma) and manually convert files with pipes before ingestion.
Why wrong: Manual conversion is not efficient and does not scale.
- B
Create an AWS Lambda function triggered by S3 events that reads the first line of each file, detects the delimiter, and runs the COPY command with the appropriate DELIMITER option.
Lambda provides a lightweight, event-driven solution to dynamically detect and handle delimiters.
- C
Use AWS Glue to crawl the S3 bucket and automatically detect the schema and delimiter before writing to Redshift.
Why wrong: Glue can detect delimiters but adds cost and complexity; a simpler solution exists.
- D
Use Amazon Athena to query the files with the OpenCSVSerDe, which automatically detects delimiters, and then write the results to Redshift.
Why wrong: Athena can handle delimiters but adds latency and cost; the COPY command is more direct.
Automatically Detect Delimiters for Redshift COPY Using AWS Lambda
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 data pipeline ingests CSV files from an S3 bucket into a Redshift table using the COPY command. Recently, files with inconsistent column delimiters (some use pipes, others use commas) have been arriving. The pipeline must handle both delimiters without manual intervention. What is the MOST efficient solution?
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
Create an AWS Lambda function triggered by S3 events that reads the first line of each file, detects the delimiter, and runs the COPY command with the appropriate DELIMITER option.
Option B is correct because it provides an automated, event-driven solution that dynamically detects the delimiter of each incoming CSV file and executes the COPY command with the appropriate DELIMITER option. This approach eliminates manual intervention and leverages AWS Lambda's ability to process S3 events in near real-time, making it the most efficient for handling inconsistent delimiters.
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.
- ✗
Configure the COPY command with a fixed delimiter (e.g., comma) and manually convert files with pipes before ingestion.
Why it's wrong here
Manual conversion is not efficient and does not scale.
- ✓
Create an AWS Lambda function triggered by S3 events that reads the first line of each file, detects the delimiter, and runs the COPY command with the appropriate DELIMITER option.
Why this is correct
Lambda provides a lightweight, event-driven solution to dynamically detect and handle delimiters.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Glue to crawl the S3 bucket and automatically detect the schema and delimiter before writing to Redshift.
Why it's wrong here
Glue can detect delimiters but adds cost and complexity; a simpler solution exists.
- ✗
Use Amazon Athena to query the files with the OpenCSVSerDe, which automatically detects delimiters, and then write the results to Redshift.
Why it's wrong here
Athena can handle delimiters but adds latency and cost; the COPY command is more direct.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume AWS Glue or Athena are suitable for dynamic delimiter detection, but they lack the ability to directly and efficiently execute Redshift COPY commands with per-file delimiter customization without significant additional orchestration.
Trap categories for this question
Command / output trap
Athena can handle delimiters but adds latency and cost; the COPY command is more direct.
Detailed technical explanation
How to think about this question
The COPY command in Redshift supports the DELIMITER option to specify the column delimiter, and the Lambda function can use the Python `csv.Sniffer` class to detect the delimiter by analyzing the first line of the file. This approach is efficient because it processes files individually as they land in S3, avoiding batch processing overhead, and the Lambda function can be configured with a timeout and memory sufficient to handle the detection and COPY execution within a few seconds.
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.
TExam Day Tips
- 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
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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: Create an AWS Lambda function triggered by S3 events that reads the first line of each file, detects the delimiter, and runs the COPY command with the appropriate DELIMITER option. — Option B is correct because it provides an automated, event-driven solution that dynamically detects the delimiter of each incoming CSV file and executes the COPY command with the appropriate DELIMITER option. This approach eliminates manual intervention and leverages AWS Lambda's ability to process S3 events in near real-time, making it the most efficient for handling inconsistent delimiters.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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