← MLS-C01·Amazon Web Services

Question 827 of 1,755

Data Engineering →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is AWS Glue with a crawler and ETL job using resolveChoice, as this combination directly addresses the need for a data pipeline to Redshift with schema evolution while minimizing operational overhead. AWS Glue’s crawler automatically infers and updates the schema from CSV files in S3, and its DynamicFrame with the resolveChoice transformation allows new columns to be added gracefully without breaking the pipeline. On the MLS-C01 exam, this scenario tests your understanding of batch ETL versus streaming services and the importance of schema flexibility in machine learning data preparation—a common trap is choosing Kinesis Data Firehose for batch files or assuming Redshift Spectrum handles schema changes automatically. Remember the mnemonic “Glue Resolves New Columns” to recall that Glue’s resolveChoice is the key to handling schema evolution in batch pipelines to 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?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

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.

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

✓

Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns

Option C is correct because AWS Glue can crawl the S3 data to infer schema, and Glue ETL jobs can handle schema evolution using DynamicFrame and resolveChoice. Option A is wrong because Kinesis Data Firehose is for streaming, not batch CSV files. Option B is wrong because Redshift Spectrum does not handle schema evolution automatically. Option D is wrong because Athena is an interactive query engine, not an ETL pipeline.

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 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
Static NAT maps one inside address to one outside address.

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 MLS-C01 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Data Engineering — This question tests Data Engineering — 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 to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns — Option C is correct because AWS Glue can crawl the S3 data to infer schema, and Glue ETL jobs can handle schema evolution using DynamicFrame and resolveChoice. Option A is wrong because Kinesis Data Firehose is for streaming, not batch CSV files. Option B is wrong because Redshift Spectrum does not handle schema evolution automatically. Option D is wrong because Athena is an interactive query engine, not an ETL pipeline.

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

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

About these practice questions

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Same concept, more angles

1 more ways this is tested on MLS-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A team wants to build a data pipeline that processes incoming JSON files from an S3 bucket and loads them into a Redshift table. The pipeline must handle schema evolution and data validation. Which combination of services would be MOST appropriate?

medium

✓ A.Amazon S3 + AWS Glue + Amazon Redshift
B.Amazon S3 + Amazon SQS + Amazon Redshift
C.Amazon S3 + AWS Data Pipeline + Amazon Redshift
D.Amazon S3 + AWS Lambda + Amazon Redshift

Why A: AWS Glue can crawl the S3 data to infer schema, perform ETL transformations, and load into Redshift. SQS is not needed. Lambda is event-driven but lacks built-in schema evolution. Data Pipeline is older and less flexible.

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