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.
A data engineer needs to collect and analyze log data from multiple EC2 instances in real-time. The solution should be serverless and scalable. Which TWO AWS services should be used?
Amazon Kinesis Data Firehose
Firehose can ingest streaming data.
Amazon EMR
Why wrong: EMR is not serverless and is overkill for this use case.
Amazon Athena
Why wrong: Athena is for batch querying.
Amazon OpenSearch Service
OpenSearch Service enables real-time log analytics.
Amazon S3
Why wrong: S3 is storage, not real-time analytics.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service that can capture, transform, and load streaming log data from EC2 instances into destinations like Amazon S3 or Amazon OpenSearch Service in near real-time, with no infrastructure to manage. It automatically scales to handle high-throughput data streams, making it ideal for real-time log analytics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon Kinesis Data Firehose
Why this is correct
Firehose can ingest streaming data.
Related concept
Read the scenario before looking for a memorised answer.
Amazon EMR
Why it's wrong here
EMR is not serverless and is overkill for this use case.
Amazon Athena
Why it's wrong here
Athena is for batch querying.
Amazon OpenSearch Service
Why this is correct
OpenSearch Service enables real-time log analytics.
Related concept
Read the scenario before looking for a memorised answer.
Amazon S3
Why it's wrong here
S3 is storage, not real-time analytics.
Common exam traps
The trap here is that candidates often choose Amazon S3 alone for storage, forgetting that a real-time ingestion layer like Kinesis Data Firehose is required to collect and stream the data from EC2 instances into a queryable destination.
Detailed technical explanation
Kinesis Data Firehose uses a buffering mechanism (default 1 MB or 60 seconds) to batch records before delivery, which introduces a slight latency but ensures efficient writes to destinations like OpenSearch Service. OpenSearch Service provides near-real-time indexing and search capabilities via its RESTful API, allowing log data to be queried within seconds of ingestion. In a real-world scenario, you would configure an EC2 agent (e.g., Fluentd or Kinesis Agent) to send logs to Firehose, which then delivers them to OpenSearch for dashboards and alerting.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Kinesis Data Firehose — Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service that can capture, transform, and load streaming log data from EC2 instances into destinations like Amazon S3 or Amazon OpenSearch Service in near real-time, with no infrastructure to manage. It automatically scales to handle high-throughput data streams, making it ideal for real-time log analytics.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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