MLS-C01 Data Engineering Practice Question
A company is migrating its on-premises Apache Hadoop cluster to AWS. The cluster processes large datasets using Spark jobs. The company wants to minimize operational overhead and use native AWS services. Which combination of services should the company use?
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
✓
Amazon EMR with Spark and Amazon S3
Amazon EMR is a managed Hadoop framework that natively supports Spark jobs, and Amazon S3 provides scalable and durable object storage for the data. This combination minimizes operational overhead as EMR automatically handles cluster provisioning, scaling, and monitoring. Option B is incorrect because Amazon Redshift is a data warehouse, not a Hadoop cluster, and Spectrum is for querying data in S3, not for running Spark jobs. Option C is incorrect because Amazon Athena is a serverless query service for SQL-based analytics, not for executing Spark jobs, and AWS Glue is an ETL service, not a compute engine for Spark. Option D is incorrect because running Apache Spark on EC2 instances requires manual setup, maintenance, and scaling of the cluster, increasing operational overhead compared to using a managed service like EMR.
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 with Spark and Amazon S3
Why this is correct
EMR is a managed service that runs Spark and integrates with S3.
- ✗
Amazon Redshift with Spectrum and Amazon S3
Why it's wrong here
Redshift is a data warehouse, not a Spark cluster.
- ✗
Amazon Athena and AWS Glue
Why it's wrong here
Athena is for SQL queries, not Spark.
- ✗
Amazon EC2 instances with Apache Spark installed and Amazon S3
Why it's wrong here
This requires manual cluster management.
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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