This MLS-C01 practice question tests your understanding of data engineering. 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 company needs to build a data lake on AWS for analytics. The data includes structured, semi-structured, and unstructured data. The solution must support schema-on-read, provide fine-grained access control, and be cost-effective for storing rarely accessed data. Which THREE services should be used? (Choose THREE)
AWS Glue Data Catalog for schema-on-read.
Glue enables schema-on-read for analytics.
Amazon Redshift for data warehousing.
Why wrong: Redshift is a warehouse, not a data lake.
Amazon S3 as the primary storage layer.
S3 is the foundation of a data lake.
Amazon EMR for data processing.
Why wrong: EMR is for processing, not storage.
S3 Lifecycle policies to transition data to Glacier.
Lifecycle policies reduce cost for old data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Glue Data Catalog for schema-on-read.
AWS Glue Data Catalog is correct because it provides a centralized metadata repository that enables schema-on-read for data stored in Amazon S3. It allows you to define table schemas and partitions without transforming the underlying data, so analytics tools like Amazon Athena and Amazon EMR can query the data with the schema applied at read time.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS Glue Data Catalog for schema-on-read.
Why this is correct
Glue enables schema-on-read for analytics.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Redshift for data warehousing.
Why it's wrong here
Redshift is a warehouse, not a data lake.
Amazon S3 as the primary storage layer.
Why this is correct
S3 is the foundation of a data lake.
Related concept
Read the scenario before looking for a memorised answer.
Amazon EMR for data processing.
Why it's wrong here
EMR is for processing, not storage.
S3 Lifecycle policies to transition data to Glacier.
Why this is correct
Lifecycle policies reduce cost for old data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse Amazon Redshift as a data lake storage layer due to its analytics capabilities, but it is a data warehouse with schema-on-write and higher costs for infrequently accessed data, making it unsuitable for the described requirements.
Detailed technical explanation
Schema-on-read in AWS Glue Data Catalog works by storing table definitions and partition metadata in the Hive Metastore-compatible catalog, allowing query engines like Athena to infer schema from the data at query time using SerDe libraries. S3 Lifecycle policies automate transitions to S3 Glacier or Glacier Deep Archive based on age or access patterns, reducing storage costs for rarely accessed data while retaining the ability to restore for analytics. Fine-grained access control is achieved through S3 bucket policies, IAM roles, and AWS Lake Formation, which integrates with Glue Data Catalog to manage permissions at the table and column level.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Glue Data Catalog for schema-on-read. — AWS Glue Data Catalog is correct because it provides a centralized metadata repository that enables schema-on-read for data stored in Amazon S3. It allows you to define table schemas and partitions without transforming the underlying data, so analytics tools like Amazon Athena and Amazon EMR can query the data with the schema applied at read time.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This MLS-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 MLS-C01 exam.
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