- A
Use Amazon Kinesis Data Firehose to directly load into Redshift.
Why wrong: Firehose delivers to Redshift via S3, but the question asks for methods; this is indirect but acceptable? However, Firehose can load directly into Redshift, but it's not as efficient as COPY for batch loads. I'll mark it wrong because the question asks for 'efficiently' and COPY is preferred.
- B
Use AWS DMS to replicate from S3 to Redshift.
Why wrong: DMS is for database migrations, not S3 to Redshift bulk loads.
- C
Use the Redshift COPY command to load from S3.
COPY is the fastest way to bulk load from S3.
- D
Use a staging table in S3 and then COPY into Redshift.
This is common: COPY from S3 to staging, then INSERT into target.
- E
Use individual INSERT statements in a loop.
Why wrong: INSERT is slow for large datasets.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 is designing a data ingestion pipeline to load JSON files from Amazon S3 into Amazon Redshift. Which TWO methods can be used to load the data efficiently?
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 the Redshift COPY command to load from S3.
Option C is correct because the Redshift COPY command is specifically designed to efficiently load large datasets from Amazon S3 by automatically parallelizing the data across cluster nodes, leveraging the cluster's compute resources for high-throughput ingestion. It supports JSON data natively via the 'json' option, making it ideal for loading JSON files directly from S3 without intermediate transformations.
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 Amazon Kinesis Data Firehose to directly load into Redshift.
Why it's wrong here
Firehose delivers to Redshift via S3, but the question asks for methods; this is indirect but acceptable? However, Firehose can load directly into Redshift, but it's not as efficient as COPY for batch loads. I'll mark it wrong because the question asks for 'efficiently' and COPY is preferred.
- ✗
Use AWS DMS to replicate from S3 to Redshift.
Why it's wrong here
DMS is for database migrations, not S3 to Redshift bulk loads.
- ✓
Use the Redshift COPY command to load from S3.
Why this is correct
COPY is the fastest way to bulk load from S3.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a staging table in S3 and then COPY into Redshift.
Why this is correct
This is common: COPY from S3 to staging, then INSERT into target.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use individual INSERT statements in a loop.
Why it's wrong here
INSERT is slow for large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Amazon Kinesis Data Firehose's ability to 'deliver' to Redshift with the actual loading mechanism, not realizing that Firehose only writes to S3 and then triggers a COPY command, making Option A a distractor for a direct load method.
Detailed technical explanation
How to think about this question
The Redshift COPY command leverages the cluster's massively parallel processing (MPP) architecture by automatically splitting the input data into slices and distributing the load across all compute nodes. For JSON data, the 'auto' option or explicit 'json' path can handle nested structures, but note that Redshift requires JSON to be in a line-delimited format (one JSON object per line) rather than a single JSON array. In real-world scenarios, using a staging table (Option D) is a best practice to validate and transform data before loading into the final target table, reducing the risk of load failures and enabling incremental updates.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the Redshift COPY command to load from S3. — Option C is correct because the Redshift COPY command is specifically designed to efficiently load large datasets from Amazon S3 by automatically parallelizing the data across cluster nodes, leveraging the cluster's compute resources for high-throughput ingestion. It supports JSON data natively via the 'json' option, making it ideal for loading JSON files directly from S3 without intermediate transformations.
What should I do if I get this DEA-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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