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Question 158 of 1,755

Data Engineering →mediumMultiple ChoiceObjective-mapped

Building a Real-Time Clickstream Analytics Pipeline with Amazon Kinesis

This MLS-C01 practice question tests your understanding of data engineering. 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 data engineering team is building a real-time clickstream analytics pipeline on AWS. They need to ingest millions of events per second from mobile apps and websites, process them with low latency, and store the results in Amazon S3 for downstream analysis. Which combination of AWS services should the team use to minimize operational overhead while meeting these requirements?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Use Amazon MQ to ingest streaming data, AWS Lambda to process each message, and save output to Amazon S3.
Why wrong: Amazon MQ is for message queuing, not high-throughput streaming; Lambda may have concurrency limits and is not ideal for millions of events per second.
B
Use Amazon Kinesis Data Streams to ingest data, Amazon EMR to process with Spark Streaming, and save output to Amazon S3.
Why wrong: EMR is a managed cluster solution; it adds operational overhead compared to serverless options.
C
Use Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose to deliver results to Amazon S3.
This combination provides serverless, low-latency ingestion, processing, and delivery with minimal operational overhead.
D
Use AWS Glue to ingest data into Amazon RDS, then use AWS Glue ETL jobs to transform and load into Amazon S3.
Why wrong: AWS Glue is a batch ETL service; not suitable for real-time ingestion and processing.

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 Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose to deliver results to Amazon S3.

Option C is correct because Amazon Kinesis Data Streams scales to handle millions of events per second with low latency, Kinesis Data Analytics provides real-time processing without managing infrastructure, and Kinesis Data Firehose delivers processed data to Amazon S3 with automatic buffering and compression, minimizing operational overhead. Option A is wrong because Amazon MQ is a managed message broker for standard protocols (e.g., JMS) and does not offer the high-throughput, real-time streaming capabilities required for clickstream analytics. Option B is wrong because, while Kinesis Data Streams works for ingestion, using Amazon EMR with Spark Streaming adds operational overhead for cluster management and scaling, and is less suited for low-latency, serverless processing compared to Kinesis Data Analytics. Option D is wrong because AWS Glue is a batch ETL service, not designed for real-time ingestion, and Amazon RDS is a relational database that cannot handle the throughput and streaming nature of clickstream data; Glue cannot directly ingest streaming data into RDS in real time.

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 MQ to ingest streaming data, AWS Lambda to process each message, and save output to Amazon S3.
Why it's wrong here
Amazon MQ is for message queuing, not high-throughput streaming; Lambda may have concurrency limits and is not ideal for millions of events per second.
✗
Use Amazon Kinesis Data Streams to ingest data, Amazon EMR to process with Spark Streaming, and save output to Amazon S3.
Why it's wrong here
EMR is a managed cluster solution; it adds operational overhead compared to serverless options.
✓
Use Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose to deliver results to Amazon S3.
Why this is correct
This combination provides serverless, low-latency ingestion, processing, and delivery with minimal operational overhead.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use AWS Glue to ingest data into Amazon RDS, then use AWS Glue ETL jobs to transform and load into Amazon S3.
Why it's wrong here
AWS Glue is a batch ETL service; not suitable for real-time ingestion and processing.

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.

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 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 Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose to deliver results to Amazon S3. — Option C is correct because Amazon Kinesis Data Streams scales to handle millions of events per second with low latency, Kinesis Data Analytics provides real-time processing without managing infrastructure, and Kinesis Data Firehose delivers processed data to Amazon S3 with automatic buffering and compression, minimizing operational overhead. Option A is wrong because Amazon MQ is a managed message broker for standard protocols (e.g., JMS) and does not offer the high-throughput, real-time streaming capabilities required for clickstream analytics. Option B is wrong because, while Kinesis Data Streams works for ingestion, using Amazon EMR with Spark Streaming adds operational overhead for cluster management and scaling, and is less suited for low-latency, serverless processing compared to Kinesis Data Analytics. Option D is wrong because AWS Glue is a batch ETL service, not designed for real-time ingestion, and Amazon RDS is a relational database that cannot handle the throughput and streaming nature of clickstream data; Glue cannot directly ingest streaming data into RDS in real time.

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

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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