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Data Engineering →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to use a staging table for incremental loads with a MERGE operation, combined with a scheduled job to handle late-arriving data. This method minimizes impact on query performance during the day because the staging table isolates the new data, allowing you to perform an upsert (MERGE) into the main table in a single, efficient transaction without locking or blocking concurrent queries. Late-arriving data—files that appear after the daily load—are then merged in a separate batch job, ensuring no records are lost. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of Redshift’s data loading patterns and the trade-offs between batch, streaming, and query-in-place solutions. A common trap is choosing Redshift Spectrum or Glue ETL with overwrite, which either sacrifices query performance or fails to preserve late data. Memory tip: think “Stage, Merge, Catch-up” – stage the new data, merge it cleanly, then catch any stragglers.

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?

Question 1hardmultiple choice

Full question →

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.

Full breakdown with real-world context →

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 A is correct because staging tables allow incremental loads with upsert logic using a staging table, and a late-arriving data process can merge the additional records later without blocking queries. Option B (COPY with auto staging) Redshift Spectrum queries data in S3 without loading, which may be slower for frequent queries. Option C (Kinesis Firehose) is real-time streaming, suitable for near-real-time but not for batched daily loads with late data handling. Option D (Glue ETL with overwrite) overwrites data, losing late-arriving data.

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

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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.

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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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 A is correct because staging tables allow incremental loads with upsert logic using a staging table, and a late-arriving data process can merge the additional records later without blocking queries. Option B (COPY with auto staging) Redshift Spectrum queries data in S3 without loading, which may be slower for frequent queries. Option C (Kinesis Firehose) is real-time streaming, suitable for near-real-time but not for batched daily loads with late data handling. Option D (Glue ETL with overwrite) overwrites data, losing late-arriving data.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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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.