- A
Use AWS Glue to create a crawler that updates the schema, then use Redshift Spectrum to query the data directly from S3
Why wrong: Redshift Spectrum queries S3 but does not load into Redshift; schema evolution would require manual table changes.
- B
Use Amazon Kinesis Data Firehose to ingest the files and load into Redshift, with a Lambda function to detect schema changes
Why wrong: Firehose is designed for streaming data, not batch CSV ingestion from S3.
- C
Use Amazon Athena to create external tables with schema-on-read, and insert results into Redshift using INSERT INTO
Why wrong: Athena is not an ETL tool; manual INSERT INTO for each new schema is not minimal overhead.
- D
Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns
Glue handles schema evolution via DynamicFrame and resolveChoice, and loads into Redshift.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 engineer needs to build a pipeline that ingests CSV files from an S3 bucket, validates the schema, and loads the data into an Amazon Redshift cluster. The pipeline must handle schema evolution gracefully by adding new columns as they appear in the source files. Which combination of AWS services and configurations would meet these requirements with minimal operational overhead?
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
Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns
Option D is correct because AWS Glue provides a fully managed ETL service that can automatically detect schema changes via crawlers and handle new columns in CSV files using the 'resolveChoice' transformation. The Glue ETL job can write directly to Amazon Redshift with minimal operational overhead, as it manages schema evolution without requiring manual intervention or additional infrastructure.
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 to create a crawler that updates the schema, then use Redshift Spectrum to query the data directly from S3
Why it's wrong here
Redshift Spectrum queries S3 but does not load into Redshift; schema evolution would require manual table changes.
- ✗
Use Amazon Kinesis Data Firehose to ingest the files and load into Redshift, with a Lambda function to detect schema changes
Why it's wrong here
Firehose is designed for streaming data, not batch CSV ingestion from S3.
- ✗
Use Amazon Athena to create external tables with schema-on-read, and insert results into Redshift using INSERT INTO
Why it's wrong here
Athena is not an ETL tool; manual INSERT INTO for each new schema is not minimal overhead.
- ✓
Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns
Why this is correct
Glue handles schema evolution via DynamicFrame and resolveChoice, and loads into Redshift.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Redshift Spectrum or Athena can load data into Redshift, but they are query engines, not data loading services, and do not handle schema evolution for batch ingestion into a Redshift cluster.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue's 'resolveChoice' transformation allows you to specify how to handle schema conflicts (e.g., make_cols, project, or cast) when new columns appear in source data, enabling dynamic schema evolution. In a real-world scenario, if a CSV file adds a 'discount' column mid-pipeline, Glue can automatically add that column to the target Redshift table without breaking existing data or requiring manual DDL changes.
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.
Visual reference
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 MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns — Option D is correct because AWS Glue provides a fully managed ETL service that can automatically detect schema changes via crawlers and handle new columns in CSV files using the 'resolveChoice' transformation. The Glue ETL job can write directly to Amazon Redshift with minimal operational overhead, as it manages schema evolution without requiring manual intervention or additional infrastructure.
What should I do if I get this MLS-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
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