easyMultiple Choice
MLA-C01 Practice Question: A company stores its raw IoT sensor data in…
A company stores its raw IoT sensor data in Amazon S3. The data is in CSV format and contains timestamps, sensor IDs, and readings. A data engineer needs to catalog this data for discoverability and querying by other team members. Which AWS service should they use to create a searchable metadata 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 Data Catalog
The AWS Glue Data Catalog is a managed metadata repository that stores table definitions, schema information, and locations. It integrates with other services like Athena, EMR, and Redshift Spectrum for querying.
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 DynamoDB
Why it's wrong here
DynamoDB is a NoSQL key-value database for operational transactions, not a metadata catalog, so it cannot make S3 objects searchable for analytics. It is tempting because DynamoDB is correct when an application needs low-latency item lookups by partition key at scale.
- ✗
Amazon Athena data catalog
Why it's wrong here
Athena is a query engine that reads the AWS Glue Data Catalog; it does not itself create or store the searchable metadata catalog. It is tempting because Athena is correct when the requirement is to run SQL queries directly against CSV files in Amazon S3.
- ✗
Amazon RDS
Why it's wrong here
Amazon RDS is a managed relational database for storing and querying structured rows, not a metadata catalogue; it cannot crawl S3 CSV objects and expose searchable schema metadata. It tempts because the engineer must make data queryable, but that requirement is satisfied by AWS Glue Data Catalog with Athena.
- ✓
AWS Glue Data Catalog
Why this is correct
AWS Glue Data Catalog provides a centralised, searchable metadata repository storing table definitions, schemas and locations for data in Amazon S3. Crawlers infer CSV structure automatically, making the IoT data discoverable and queryable by other team members through Athena and related services.
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
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 665-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.