The answer is Amazon Timestream and Amazon DynamoDB. Timestream is a purpose-built time-series database that automatically scales to handle high-velocity IoT sensor data, using a dual-tier storage architecture—a memory store for recent data and a magnetic store for historical data—to optimize time-range queries with built-in time-based partitioning and SQL-like functions such as BETWEEN and DATE_BIN. DynamoDB, while a key-value and document database, is also suitable when combined with a well-designed partition key and a sort key for timestamp, enabling efficient time-range queries via the Query API on the sort key. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your ability to distinguish between purpose-built services and general-purpose ones that can be adapted for time-series workloads; a common trap is choosing only Timestream and overlooking DynamoDB’s flexibility with proper key design. Remember the mnemonic “Timestream for purpose-built, DynamoDB for adapted” to recall that both can serve time-series IoT storage, but through different architectural approaches.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 is designing a data storage solution for IoT sensor data that is ingested at high velocity. The data is time-series and needs to be queried by time range. Which TWO AWS services are suitable for this use case? (Choose TWO)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Timestream
Amazon Timestream is a purpose-built time-series database that automatically scales to handle high-velocity IoT sensor data, with built-in time-based partitioning and query optimization for time-range queries. It supports SQL-like queries with time-series functions (e.g., `BETWEEN`, `DATE_BIN`) and separates storage into a memory store for recent data and a magnetic store for historical data, enabling efficient querying by time range.
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.
✗
Amazon RDS
Why it's wrong here
RDS is not optimized for time-series data and high-velocity ingestion.
✗
Amazon Redshift
Why it's wrong here
Redshift is for analytical workloads, not real-time ingestion.
✓
Amazon Timestream
Why this is correct
Timestream is a time-series database built for IoT and operational applications.
Related concept
Read the scenario before looking for a memorised answer.
✓
Amazon DynamoDB
Why this is correct
DynamoDB can handle high-velocity writes and supports time-series queries with proper key design.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon S3
Why it's wrong here
S3 is not designed for high-velocity writes and low-latency queries on time-series data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Amazon RDS or Redshift because they are familiar with SQL-based querying, overlooking that Timestream and DynamoDB are purpose-built for high-velocity time-series ingestion and time-range queries, while RDS and Redshift incur performance and cost penalties for such workloads.
Detailed technical explanation
How to think about this question
Amazon Timestream uses a dual-storage architecture: a memory store (SSD-backed) for recent data with millisecond query latency, and a magnetic store (S3-backed) for historical data with sub-second latency, automatically moving data based on a configurable retention policy. It supports automatic time-series data compaction, deduplication, and granularity down to microsecond precision, with built-in functions like `INTERPOLATE_LINEAR` for filling gaps. In real-world IoT scenarios, Timestream can ingest millions of data points per second from devices like smart meters or industrial sensors, while DynamoDB handles the same velocity with its adaptive capacity and TTL-based time-series pattern using a composite sort key (e.g., `device_id#timestamp`).
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.
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.
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Timestream — Amazon Timestream is a purpose-built time-series database that automatically scales to handle high-velocity IoT sensor data, with built-in time-based partitioning and query optimization for time-range queries. It supports SQL-like queries with time-series functions (e.g., `BETWEEN`, `DATE_BIN`) and separates storage into a memory store for recent data and a magnetic store for historical data, enabling efficient querying by time range.
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.
About these practice questions
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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 store streaming data from IoT devices for real-time analytics. The data has a fixed schema and requires low-latency queries. Which AWS service should be used?
easy
A.Amazon DynamoDB
B.Amazon Redshift
C.Amazon S3
✓ D.Amazon Timestream
Why D: Amazon Timestream is a time-series database purpose-built for IoT and operational applications that generate large volumes of time-stamped data. It automatically manages data retention and storage tiers (memory and magnetic) to provide fast query performance for recent data and cost-effective storage for historical data, making it ideal for real-time analytics on streaming IoT data with a fixed schema.
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This DEA-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 DEA-C01 exam.
Question Discussion
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