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Data Engineering →easyMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to use S3 Event Notifications to trigger an AWS Lambda function that converts JSON to Parquet and writes to a partitioned S3 location, then query with Athena. This combination is correct because it delivers a fully serverless, event-driven pipeline where Lambda processes each incoming JSON file on arrival, using libraries like PyArrow to transform the data into columnar Parquet format and partition it by date and event type, while Athena directly queries the resulting partitioned S3 structure without any cluster management. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of serverless data transformation patterns for Athena optimization, often appearing as a trap where candidates mistakenly choose AWS Glue or EMR for simplicity—but those introduce job scheduling overhead. Remember the memory tip: “Lambda lands, Parquet partitions, Athena queries” to recall the minimal-operations chain for serverless JSON to Parquet conversion for Athena.

MLS-C01 Data Engineering Practice Question

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 is building a data pipeline to process user clickstream data. The data arrives as JSON files in an S3 bucket. The pipeline must transform the JSON into Parquet format and partition by date and event type, then make the data available for Amazon Athena queries. The engineer needs a fully managed, serverless solution with minimal operational overhead. Which combination of AWS services should the engineer use?

Question 1easymultiple choice

Read the full NAT/PAT explanation →

A
Use Amazon EMR with Spark to read JSON, convert to Parquet, and partition, then query with Athena.
Why wrong: EMR requires cluster management and is not serverless, increasing operational overhead.
B
Use AWS Glue ETL jobs to read JSON from S3, transform to Parquet, and write to a partitioned S3 location, then use Athena.
Why wrong: Glue ETL is serverless but has overhead; for simple transformations, Lambda is more efficient and cheaper.
C
Use S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.
Lambda is serverless, cost-effective for per-file processing, and can partition output easily.
D
Use Amazon Kinesis Firehose to ingest data and convert to Parquet, then write to S3, and query with Athena.
Why wrong: Kinesis Firehose is for streaming data, not batch files; it cannot handle existing S3 objects.

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 S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.

Option C is correct because AWS Lambda triggered by S3 Event Notifications provides a fully serverless, event-driven architecture with minimal operational overhead for converting JSON to Parquet and partitioning by date and event type. Lambda can process each new JSON file as it arrives, perform the transformation in memory (using libraries like PyArrow or Pandas), and write the Parquet output to a partitioned S3 path, which Athena can then query directly. This approach avoids managing any clusters or job scheduling, aligning with the requirement for a fully managed, serverless solution.

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 EMR with Spark to read JSON, convert to Parquet, and partition, then query with Athena.
Why it's wrong here
EMR requires cluster management and is not serverless, increasing operational overhead.
✗
Use AWS Glue ETL jobs to read JSON from S3, transform to Parquet, and write to a partitioned S3 location, then use Athena.
Why it's wrong here
Glue ETL is serverless but has overhead; for simple transformations, Lambda is more efficient and cheaper.
✓
Use S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.
Why this is correct
Lambda is serverless, cost-effective for per-file processing, and can partition output easily.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Kinesis Firehose to ingest data and convert to Parquet, then write to S3, and query with Athena.
Why it's wrong here
Kinesis Firehose is for streaming data, not batch files; it cannot handle existing S3 objects.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the misconception that AWS Glue is the only serverless ETL option, but the trap here is that Lambda with S3 Event Notifications is a simpler, fully serverless alternative for file-based transformations when the workload fits within Lambda's constraints.

Detailed technical explanation

How to think about this question

Under the hood, Lambda can use the `awswrangler` library to read JSON from S3, convert to Parquet with partitioning, and write back to S3 in a single function invocation, leveraging S3's strong consistency for read-after-write. A subtle behavior is that Lambda has a maximum execution time of 15 minutes and a deployment package size limit, so for very large JSON files, the function may timeout or require splitting; in such cases, AWS Glue or EMR would be more appropriate. In a real-world scenario, this pattern is ideal for low-to-medium volume clickstream data where each JSON file is under a few hundred MB, ensuring cost-effective, near-real-time partitioning without idle cluster costs.

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.

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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 S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena. — Option C is correct because AWS Lambda triggered by S3 Event Notifications provides a fully serverless, event-driven architecture with minimal operational overhead for converting JSON to Parquet and partitioning by date and event type. Lambda can process each new JSON file as it arrives, perform the transformation in memory (using libraries like PyArrow or Pandas), and write the Parquet output to a partitioned S3 path, which Athena can then query directly. This approach avoids managing any clusters or job scheduling, aligning with the requirement for a fully managed, serverless solution.

What should I do if I get this MLS-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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