- A
Use AWS Snowball Edge to physically ship the data.
Snowball Edge provides high-speed local transfer and avoids network bottlenecks.
- B
Use Amazon S3 Transfer Acceleration to speed up the upload.
Why wrong: Transfer Acceleration improves speed but still limited by last-mile bandwidth.
- C
Use AWS DataSync over the existing network connection.
Why wrong: DataSync uses the network; 1 Gbps would take ~5 days under ideal conditions, but may exceed 10 with overhead.
- D
Set up a VPN connection and use multi-part upload directly to S3.
Why wrong: VPN adds overhead; 50 TB over 1 Gbps may take too long.
Question 184 of 1,755
Choosing Snowball for Large Data Transfer to S3 — How to Maximize Speed | AWS Machine Learning Specialty Explained
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.
A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 1 Gbps connection to AWS. The transfer must be completed within 10 days. What is the MOST efficient approach?
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 AWS Snowball Edge to physically ship the data.
Transferring 50 TB over a 1 Gbps connection would take approximately 5.6 days under ideal conditions (50 TB × 8 bits/byte / 1 Gbps / 86400 seconds/day ≈ 4.63 days), but real-world factors like network congestion, TCP overhead, and protocol inefficiencies typically reduce throughput to 50-70% of line rate, pushing the transfer beyond the 10-day window. AWS Snowball Edge provides a physical shipping alternative that bypasses network limitations entirely, making it the most efficient and reliable method for this volume and deadline.
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 Snowball Edge to physically ship the data.
Why this is correct
Snowball Edge provides high-speed local transfer and avoids network bottlenecks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon S3 Transfer Acceleration to speed up the upload.
Why it's wrong here
Transfer Acceleration improves speed but still limited by last-mile bandwidth.
- ✗
Use AWS DataSync over the existing network connection.
Why it's wrong here
DataSync uses the network; 1 Gbps would take ~5 days under ideal conditions, but may exceed 10 with overhead.
- ✗
Set up a VPN connection and use multi-part upload directly to S3.
Why it's wrong here
VPN adds overhead; 50 TB over 1 Gbps may take too long.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates calculate the theoretical maximum transfer time (50 TB / 1 Gbps ≈ 4.6 days) and conclude it fits within 10 days, ignoring real-world network inefficiencies, protocol overhead, and the fact that sustained throughput rarely exceeds 50-70% of line rate, which pushes the actual time beyond the deadline.
Detailed technical explanation
How to think about this question
AWS Snowball Edge uses a ruggedized storage device with 80 TB of usable capacity (for Snowball Edge Storage Optimized) and supports both block and object storage, allowing direct import into S3 via the Snowball client. The device is shipped via a carrier like UPS or FedEx, and the transfer time is dominated by physical shipping (typically 2-5 days) rather than network speed, making it predictable for large datasets. In practice, Snowball Edge also supports local compute for data preprocessing, which can reduce the time needed to prepare data for S3 ingestion.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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
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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 AWS Snowball Edge to physically ship the data. — Transferring 50 TB over a 1 Gbps connection would take approximately 5.6 days under ideal conditions (50 TB × 8 bits/byte / 1 Gbps / 86400 seconds/day ≈ 4.63 days), but real-world factors like network congestion, TCP overhead, and protocol inefficiencies typically reduce throughput to 50-70% of line rate, pushing the transfer beyond the 10-day window. AWS Snowball Edge provides a physical shipping alternative that bypasses network limitations entirely, making it the most efficient and reliable method for this volume and deadline.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
5 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer needs to transfer 50 TB of historical data from an on-premises HDFS cluster to Amazon S3. The company has a 1 Gbps internet connection. Which service would complete the transfer in the shortest time?
easy- ✓ A.AWS Snowball
- B.Amazon S3 Transfer Acceleration
- C.AWS Direct Connect
- D.AWS DataSync
Why A: AWS Snowball is the correct choice because transferring 50 TB over a 1 Gbps internet connection would take approximately 5.5 days (50 TB × 1024 GB/TB × 8 bits/byte ÷ 1 Gbps ÷ 86400 seconds/day), assuming full utilization, which is unrealistic due to overhead and contention. Snowball provides a physical appliance that can be loaded with data locally and shipped to AWS, completing the transfer in a few days including shipping time, making it faster than any network-based method for this volume.
Variation 2. A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 100 Mbps connection to AWS. The transfer must be completed within one week. Which approach should the engineer use?
medium- ✓ A.Use AWS Snowball Edge device to physically transfer the data.
- B.Use Amazon S3 Transfer Acceleration.
- C.Use AWS DataSync to transfer the data over the network.
- D.Use multiple concurrent AWS CLI copy commands over VPN.
Why A: The on-premises network has a 100 Mbps connection, which yields a theoretical maximum transfer of about 1.08 TB per day (100 Mbps * 86400 seconds / 8 bits per byte / 1024^4 bytes per TB). To transfer 50 TB within 7 days, the required throughput is approximately 7.14 TB per day, far exceeding the available bandwidth. AWS Snowball Edge provides a physical shipping method that bypasses network constraints entirely, making it the only viable option for this volume and timeline.
Variation 3. A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 100 Mbps internet connection and the data must be transferred within 5 days. Which AWS service is best suited for this task?
easy- A.AWS DataSync
- ✓ B.AWS Snowball Edge
- C.Amazon S3 Transfer Acceleration
- D.AWS Storage Gateway
Why B: Option B is correct because AWS Snowball Edge is a physical device designed for large-scale data transfer, ideal for moving 50 TB of data when network bandwidth (100 Mbps) is insufficient to meet the 5-day deadline. Option A is wrong because AWS DataSync is a network-based service; transferring 50 TB over 100 Mbps would take approximately 50 days, far exceeding 5 days. Option C is wrong because Amazon S3 Transfer Acceleration improves transfer speeds over the internet but cannot overcome the 100 Mbps bandwidth limitation; it would still take too long. Option D is wrong because AWS Storage Gateway is for hybrid cloud storage with a local cache, not for one-time bulk data migration.
Variation 4. A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 1 Gbps internet connection and wants to complete the transfer within 5 days. What is the MOST cost-effective and reliable solution?
medium- ✓ A.Use AWS Snowball Edge device to physically ship the data
- B.Use S3 multipart upload over the internet
- C.Set up AWS Direct Connect and transfer over the dedicated line
- D.Use S3 Transfer Acceleration to speed up the transfer
Why A: AWS Snowball Edge is the most cost-effective and reliable solution because transferring 50 TB over a 1 Gbps internet connection would take approximately 5.5 days under ideal conditions (50 TB * 1024 GB/TB * 8 bits/byte / (1 Gbps * 86400 seconds/day) ≈ 4.74 days, but real-world overhead, congestion, and retransmissions push it beyond 5 days). Snowball Edge provides a physical appliance that can be shipped, avoiding network bandwidth limitations entirely, and is designed for large-scale data transfers where internet speeds are insufficient.
Variation 5. A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 100 Mbps internet connection and a tight deadline of two weeks. Which AWS service should the engineer use to transfer the data most efficiently?
easy- A.AWS Storage Gateway (Volume Gateway)
- ✓ B.AWS Snowball Edge
- C.Amazon S3 Transfer Acceleration
- D.AWS DataSync over the internet
Why B: B is correct because transferring 50 TB over a 100 Mbps connection would take approximately 48 days (50 TB * 8 / 100 Mbps / 86400 seconds/day), far exceeding the two-week deadline. AWS Snowball Edge is a physical data transport device that can securely transfer petabytes of data offline, bypassing network bandwidth constraints entirely. For large datasets and tight deadlines, Snowball Edge is the most efficient AWS service.
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Last reviewed: Jul 4, 2026
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