- A
Use Amazon Kinesis Data Analytics to process the data in real-time and write to Redshift directly.
Why wrong: Kinesis Data Analytics is not designed for direct Redshift writes.
- B
Increase the size of the Redshift cluster to improve load performance.
Why wrong: Redshift load performance is not the primary bottleneck.
- C
Use Amazon Kinesis Data Firehose to ingest the data directly into S3 and then use Redshift Spectrum to query the data without loading.
Firehose can handle high throughput and Redshift Spectrum reduces load time.
- D
Increase the number of DPUs and allocate more memory to the Glue job.
Why wrong: Glue jobs may still fail due to high volume and memory constraints.
Question 1,696 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 logistics company ingests GPS tracking data from thousands of vehicles into Amazon S3 via AWS Direct Connect. Each vehicle sends a message every 5 seconds, resulting in about 200,000 messages per second. Each message is about 200 bytes. The company uses AWS Glue to transform the data into a parquet format and load it into Amazon Redshift for real-time analytics. However, the Glue jobs are failing due to memory issues and the data is not being loaded into Redshift quickly enough. The company needs to reduce the latency of data availability in Redshift. Which action should the data engineer take?
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 Kinesis Data Firehose to ingest the data directly into S3 and then use Redshift Spectrum to query the data without loading.
Option C is correct because Amazon Kinesis Data Firehose can ingest high-throughput streaming data (200,000 messages/sec) and deliver it to S3 in near-real-time (typically under 60 seconds). By using Redshift Spectrum to query the data directly in S3, the company avoids the latency and memory issues associated with AWS Glue batch transformations and Redshift bulk loads. This approach reduces data availability latency significantly. Option A is incorrect because Amazon Kinesis Data Analytics adds processing overhead and does not directly solve the Glue memory issue or reduce latency to Redshift; it is more suitable for real-time streaming analytics, not for minimizing data ingestion latency. Option B is incorrect because increasing the Redshift cluster size improves query performance and load speed but does not address the root cause: the Glue jobs are failing due to memory issues, and the data is not being transformed quickly enough. The bottleneck is upstream of Redshift. Option D is incorrect because increasing DPUs and memory for the Glue job might resolve memory issues but does not significantly reduce latency; Glue batch processing still incurs minutes of delay, whereas Firehose provides near-real-time delivery.
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 Analytics to process the data in real-time and write to Redshift directly.
Why it's wrong here
Kinesis Data Analytics is not designed for direct Redshift writes.
- ✗
Increase the size of the Redshift cluster to improve load performance.
Why it's wrong here
Redshift load performance is not the primary bottleneck.
- ✓
Use Amazon Kinesis Data Firehose to ingest the data directly into S3 and then use Redshift Spectrum to query the data without loading.
Why this is correct
Firehose can handle high throughput and Redshift Spectrum reduces load time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of DPUs and allocate more memory to the Glue job.
Why it's wrong here
Glue jobs may still fail due to high volume and memory constraints.
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.
Visual reference
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 DEA-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 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 Amazon Kinesis Data Firehose to ingest the data directly into S3 and then use Redshift Spectrum to query the data without loading. — Option C is correct because Amazon Kinesis Data Firehose can ingest high-throughput streaming data (200,000 messages/sec) and deliver it to S3 in near-real-time (typically under 60 seconds). By using Redshift Spectrum to query the data directly in S3, the company avoids the latency and memory issues associated with AWS Glue batch transformations and Redshift bulk loads. This approach reduces data availability latency significantly. Option A is incorrect because Amazon Kinesis Data Analytics adds processing overhead and does not directly solve the Glue memory issue or reduce latency to Redshift; it is more suitable for real-time streaming analytics, not for minimizing data ingestion latency. Option B is incorrect because increasing the Redshift cluster size improves query performance and load speed but does not address the root cause: the Glue jobs are failing due to memory issues, and the data is not being transformed quickly enough. The bottleneck is upstream of Redshift. Option D is incorrect because increasing DPUs and memory for the Glue job might resolve memory issues but does not significantly reduce latency; Glue batch processing still incurs minutes of delay, whereas Firehose provides near-real-time delivery.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 2026
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