- A
Use AWS Glue ETL to copy the data from S3 to Redshift, overwriting the existing data each day.
Why wrong: Overwriting loses late-arriving data and causes downtime during load.
- B
Use a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.
Staging tables allow incremental upserts and handling of late data without blocking queries.
- C
Stream the data from S3 using Amazon Kinesis Firehose to load into Redshift continuously.
Why wrong: Firehose is for streaming, not for batch daily loads with late-arriving data handling.
- D
Use Amazon Redshift Spectrum to query data directly from S3 and create external tables.
Why wrong: Spectrum queries data in S3 without loading, which can be slower for frequent analytics queries.
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 e-commerce company uses Amazon Redshift for analytics. The data engineering team needs to load daily sales data from an S3 bucket that receives new files every hour. The data must be loaded into Redshift with minimal impact on query performance during the day, and they need to handle late-arriving data (files that appear after the daily load). Which approach should they 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
Use a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.
Option B is correct because it uses a staging table to incrementally load data with a MERGE operation, which minimizes impact on query performance by avoiding full table overwrites. The separate late-arriving data job handles files that appear after the daily load, ensuring completeness without blocking ongoing queries. This approach aligns with Redshift's best practices for incremental loads and late-arriving data handling.
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 ETL to copy the data from S3 to Redshift, overwriting the existing data each day.
Why it's wrong here
Overwriting loses late-arriving data and causes downtime during load.
- ✓
Use a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.
Why this is correct
Staging tables allow incremental upserts and handling of late data without blocking queries.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Stream the data from S3 using Amazon Kinesis Firehose to load into Redshift continuously.
Why it's wrong here
Firehose is for streaming, not for batch daily loads with late-arriving data handling.
- ✗
Use Amazon Redshift Spectrum to query data directly from S3 and create external tables.
Why it's wrong here
Spectrum queries data in S3 without loading, which can be slower for frequent analytics queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse continuous streaming (Option C) with batch incremental loading, not realizing that Kinesis Firehose is optimized for real-time streams, not for handling sporadic late-arriving files in a batch context.
Detailed technical explanation
How to think about this question
Under the hood, the MERGE operation in Redshift performs an atomic upsert by first deleting matching rows from the target table and then inserting new rows from the staging table, which is efficient for incremental loads. Late-arriving data jobs can be scheduled using Amazon EventBridge or AWS Lambda to trigger a separate MERGE for files that appear after the daily load, ensuring data consistency without requiring a full reload. In real-world scenarios, this pattern is critical for e-commerce platforms where sales data may be delayed due to timezone differences or network issues, and query performance must remain stable during business hours.
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
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 a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load. — Option B is correct because it uses a staging table to incrementally load data with a MERGE operation, which minimizes impact on query performance by avoiding full table overwrites. The separate late-arriving data job handles files that appear after the daily load, ensuring completeness without blocking ongoing queries. This approach aligns with Redshift's best practices for incremental loads and late-arriving data handling.
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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