Quick Answer
The answer is AWS Lambda and AWS Glue. Lambda is correct because it can be directly invoked as a transformation function within a Kinesis Data Firehose delivery stream, where Firehose buffers incoming records and calls your specified Lambda function to modify, enrich, filter, or reformat the data before delivery. AWS Glue is also correct, as it can transform data in transit by running streaming ETL jobs against the Firehose stream, allowing schema-on-the-fly and complex data cleansing. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of real-time data preprocessing for machine learning pipelines, often appearing in scenarios where you need to clean or feature-engineer streaming data before storage. A common trap is selecting Amazon EMR or Kinesis Data Analytics, which are not native Firehose transformation integrations. Memory tip: think “Lambda for lightweight, on-the-fly record changes; Glue for schema-based, persistent streaming ETL.”
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.
Which TWO services can be used to transform data in transit within a Kinesis Data Firehose delivery stream? (Choose 2)
AWS Lambda
Firehose can invoke a Lambda function to transform records.
Amazon Kinesis Data Analytics
Why wrong: KDA is for processing streams, not Firehose transformation.
Amazon Athena
Why wrong: Athena is for querying, not real-time transformation.
Amazon S3
Why wrong: S3 is a destination, not a transformation service.
AWS Glue
Firehose can use Glue table schema for format conversion.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Lambda
AWS Lambda is correct because it can be invoked as a transformation function within a Kinesis Data Firehose delivery stream. When you enable data transformation, Firehose buffers incoming records and then calls a Lambda function you specify, passing batches of records for processing. The Lambda function can modify, enrich, filter, or reformat the data before Firehose continues delivering it to the destination.
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.
AWS Lambda
Why this is correct
Firehose can invoke a Lambda function to transform records.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Kinesis Data Analytics
Why it's wrong here
KDA is for processing streams, not Firehose transformation.
Amazon Athena
Why it's wrong here
Athena is for querying, not real-time transformation.
Amazon S3
Why it's wrong here
S3 is a destination, not a transformation service.
AWS Glue
Why this is correct
Firehose can use Glue table schema for format conversion.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse 'transformation' with 'analytics' and select Kinesis Data Analytics, not realizing that Firehose's built-in transformation feature is specifically powered by Lambda, not by a separate analytics engine.
Detailed technical explanation
Under the hood, when you enable Lambda transformation on a Firehose stream, Firehose buffers incoming records up to 3 MB or 60 seconds (whichever is reached) and then invokes your Lambda function synchronously with a payload containing the batch. The Lambda function must return the transformed records in the same order and with the same record ID, or mark records as 'Dropped' by setting the result field to 'Ok' with an empty record. This pattern is commonly used for tasks like converting CSV to Parquet, adding geolocation data, or masking sensitive fields before landing data in S3 or Redshift.
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.
What to study next
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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: AWS Lambda — AWS Lambda is correct because it can be invoked as a transformation function within a Kinesis Data Firehose delivery stream. When you enable data transformation, Firehose buffers incoming records and then calls a Lambda function you specify, passing batches of records for processing. The Lambda function can modify, enrich, filter, or reformat the data before Firehose continues delivering it to the destination.
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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