CLF-C02 Cloud Technology and Services Practice Question
A data team needs to extract data from S3 and RDS, transform it (clean, enrich, join), and load it into Amazon Redshift for analytics. They want a serverless service that discovers and catalogues data schemas automatically and runs the ETL jobs without provisioning servers. Which AWS service provides this?
⚠ Common exam trap
Many candidates confuse AWS Glue with Amazon EMR because both can run Spark-based ETL, but EMR requires server provisioning and lacks automatic schema discovery, while Glue is fully serverless and includes the Data Catalog.
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
✓
AWS Glue
AWS Glue is a fully managed, serverless ETL service that automatically discovers and catalogs data schemas using its Crawler feature, which populates the AWS Glue Data Catalog. It can extract data from S3 and RDS, transform it (clean, enrich, join), and load it into Amazon Redshift without any server provisioning or management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon EMR
Why it's wrong here
Amazon EMR is a managed big-data platform that runs Apache Spark, Hive, and other open-source frameworks on a cluster of EC2 instances. Even though EMR simplifies deployment, you are still responsible for choosing instance types, setting cluster size, and managing the cluster lifecycle, so it is not serverless. EMR also lacks a built-in data catalog and automatic schema discovery for S3 sources; you would need to configure schemas manually or integrate an external tool like the Glue Data Catalog to achieve the required functionality.
- ✗
AWS Data Pipeline
Why it's wrong here
AWS Data Pipeline is a legacy orchestration service for scheduling data-driven workflows using activities such as Hive, SQL, or Shell commands. It requires you to create pipeline definitions and provision the underlying compute resources (for example, EC2 instances) for each activity, rather than running serverlessly. Data Pipeline does not include a crawler-like mechanism to discover schemas from S3 or a centralized data catalog, and AWS Glue is now the recommended modern ETL service that provides these built-in capabilities.
- ✓
AWS Glue
Why this is correct
AWS Glue is a serverless ETL service that combines schema discovery, a managed data catalog, and Spark-based transformation jobs in one offering. A Glue Crawler automatically scans S3 or databases, infers schemas, and writes table metadata to the Glue Data Catalog, which makes the data immediately queryable by services like Athena and Redshift Spectrum. Glue ETL Jobs run on a managed, auto-scaling Spark environment without any infrastructure provisioning, and can load transformed results directly into Amazon Redshift, exactly matching the serverless and schema-discovery requirements of the scenario.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Amazon Kinesis Data Firehose is a fully managed streaming ingestion service that delivers near-real-time data to destinations such as S3, Redshift, or Splunk. Its built-in transformation capability is limited to invoking a Lambda function for simple data processing, such as CSV-to-Parquet conversion or compression, and it does not support complex, multi-step ETL workflows. Crucially, Firehose cannot automatically infer schemas or register tables in a data catalog, so it does not satisfy the schema-discovery requirement of this scenario.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CLF-C02 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 CLF-C02 exam.