- A
Use AWS Glue to run Spark jobs with data stored in S3
Why wrong: Glue can run Spark, but for large-scale jobs, EMR offers more flexibility and performance tuning.
- B
Use Amazon EMR with S3 as the data store via EMRFS
S3 provides 11 9's durability and is cheaper than EBS. EMRFS seamlessly integrates with Spark.
- C
Use Amazon Redshift Spectrum to query the data directly in S3
Why wrong: Redshift is not designed for Spark-based ETL workloads.
- D
Use Amazon EMR with HDFS on EBS volumes
Why wrong: This replicates the on-premises 3x replication cost on EBS, which is expensive.
Question 1,723 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An organization is migrating its on-premises Hadoop cluster to AWS. The cluster runs Spark jobs that process 50 TB of data daily. The data is stored in HDFS with 3x replication. Which storage option on AWS provides the best price-performance for this workload?
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
✓
Use Amazon EMR with S3 as the data store via EMRFS
Amazon EMR with S3 as the data store via EMRFS provides the best price-performance for this workload because it eliminates the need for 3x replication (S3 is inherently durable and replicated across multiple AZs), reduces storage costs, and allows compute and storage to scale independently. EMRFS enables Spark jobs to read/write directly to S3 with consistency guarantees, matching the throughput requirements of 50 TB daily processing without the overhead of managing HDFS on EBS volumes.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Glue to run Spark jobs with data stored in S3
Why it's wrong here
Glue can run Spark, but for large-scale jobs, EMR offers more flexibility and performance tuning.
- ✓
Use Amazon EMR with S3 as the data store via EMRFS
Why this is correct
S3 provides 11 9's durability and is cheaper than EBS. EMRFS seamlessly integrates with Spark.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon Redshift Spectrum to query the data directly in S3
Why it's wrong here
Redshift is not designed for Spark-based ETL workloads.
- ✗
Use Amazon EMR with HDFS on EBS volumes
Why it's wrong here
This replicates the on-premises 3x replication cost on EBS, which is expensive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume HDFS replication is necessary for durability on AWS, overlooking that S3 provides built-in replication and durability, making EMRFS with S3 the cost-effective and performant choice for Spark workloads.
Detailed technical explanation
How to think about this question
EMRFS uses a consistent view layer that leverages DynamoDB to track S3 object metadata, ensuring read-after-write consistency for Spark jobs, which is critical for workloads that overwrite or append data. S3's 11 nines of durability and automatic replication across at least three Availability Zones make the 3x HDFS replication redundant, reducing storage costs by up to 67% compared to EBS-backed HDFS. In real-world scenarios, organizations processing 50 TB daily can achieve 30-40% cost savings by using EMR with S3 versus EMR with HDFS on EBS, while maintaining similar or better I/O throughput via S3's multipart upload and parallel read capabilities.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon EMR with S3 as the data store via EMRFS — Amazon EMR with S3 as the data store via EMRFS provides the best price-performance for this workload because it eliminates the need for 3x replication (S3 is inherently durable and replicated across multiple AZs), reduces storage costs, and allows compute and storage to scale independently. EMRFS enables Spark jobs to read/write directly to S3 with consistency guarantees, matching the throughput requirements of 50 TB daily processing without the overhead of managing HDFS on EBS volumes.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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