MLS-C01 Data Engineering Practice Question
A company uses Amazon S3 to store log files from various applications. The logs are in JSON format and are appended to existing files every few minutes. A data analyst wants to run SQL queries on the logs using Amazon Athena. However, queries return incomplete results because Athena does not support modifying data. The team needs to enable querying of the latest log data with minimal changes to the existing ingestion process. Which solution should the team implement?
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
✓
Create an Athena table using the Hive JSON SerDe that reads the logs directly from the existing S3 bucket.
Athena supports reading JSON data with the Hive JSON SerDe. By creating a table with the appropriate SerDe, the analyst can query the JSON logs directly without modifying the ingestion process. Option A is incorrect because converting to Parquet would require changing the ingestion process. Option B is incorrect because using Kinesis Data Firehose would require altering the ingestion pipeline. Option D is incorrect because loading into Redshift adds complexity and latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the logs to Parquet format using a scheduled AWS Glue job and store them in a separate S3 bucket.
Why it's wrong here
This changes the existing process and adds complexity.
- ✗
Stream the logs to Amazon Kinesis Data Firehose, which writes the data to S3 in Parquet format.
Why it's wrong here
This requires modifying the log delivery mechanism.
- ✓
Create an Athena table using the Hive JSON SerDe that reads the logs directly from the existing S3 bucket.
Why this is correct
Athena can query JSON logs with the correct SerDe without changing the ingestion.
- ✗
Use AWS Glue to load the JSON logs into Amazon Redshift and query using Redshift.
Why it's wrong here
This adds a new data store and maintenance overhead.
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 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.