CLF-C02 Cloud Technology and Services Practice Question
A company wants to use Amazon S3 as their data lake but needs a way to track, search, and govern all datasets across multiple S3 buckets. Which AWS service provides centralized data catalog management?
⚠ Common exam trap
Candidates often confuse AWS Lake Formation with the Glue Data Catalog because Lake Formation provides a visual interface for managing data lakes and uses the catalog under the hood, but the question specifically asks for the service that provides centralized data catalog management, which is the Glue Data Catalog itself.
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 Data Catalog
AWS Glue Data Catalog is a fully managed, centralized metadata repository that stores table definitions, schema information, and partition details for datasets across multiple S3 buckets. It integrates with AWS Glue ETL, Amazon Athena, and Amazon Redshift Spectrum to provide a unified view for tracking, searching, and governing data lake assets, making it the correct choice for centralized data catalog 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 Macie
Why it's wrong here
Amazon Macie is a managed security service that uses machine learning and pattern matching to automatically discover, classify, and protect sensitive data stored in Amazon S3, such as personally identifiable information (PII) or protected health information (PHI). While Macie generates findings and alerts about data exposure risks, it does not provide a queryable metadata catalog of table schemas, partitions, or data locations. Its purpose is security and compliance, not schema management, so it cannot serve as the central catalog used by Athena, Redshift, or EMR.
- ✓
AWS Glue Data Catalog
Why this is correct
The AWS Glue Data Catalog is a fully managed, centralized metadata repository that stores table definitions, schema information, partition details, and the S3 locations of datasets across AWS analytics services. Glue crawlers automatically infer table schemas by scanning data in S3, and the catalog is natively integrated with Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and AWS Glue ETL jobs for querying and processing data. This makes it the correct answer for a data lake metadata catalog that enables tracking and searching datasets.
- ✗
Amazon S3 Inventory
Why it's wrong here
Amazon S3 Inventory is an S3 feature that generates periodic reports listing the objects in an S3 bucket, including object metadata such as size, storage class, and last modified date, and outputs them as CSV, JSON, or Apache Parquet files. Although the inventory report can be queried with Athena, it contains object-level listing data, not schema information such as column types, table definitions, or partition hierarchies, and it is not a live metadata service. S3 Inventory serves use cases like compliance and lifecycle management, not centralized dataset discovery.
- ✗
AWS Lake Formation
Why it's wrong here
AWS Lake Formation is a data lake governance service that automates the setup, configuration, and centralized permission management of data lakes, but it relies on the AWS Glue Data Catalog as its underlying metadata repository. Lake Formation adds fine-grained access control, row/column-level security, and data ingestion workflows, while the actual table schemas, partitions, and S3 locations remain stored in the Glue Data Catalog. Since Lake Formation consumes and extends the Glue Data Catalog rather than replacing it, it cannot be the metadata catalog itself.
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 |
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JA
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.