Quick Answer
The answer is AWS Lambda, AWS Glue, and Amazon Redshift. Lambda is correct because it can be triggered by an S3 event when a CSV file lands, performing lightweight data transformation—such as parsing rows or filtering columns—before invoking a load into Redshift via the AWS SDK, while AWS Glue handles heavier, schema-on-read transformations and can run serverless Spark jobs to clean and prepare the data at scale before the final load into Redshift. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your ability to distinguish between short-running, event-driven transformations (Lambda) and longer, complex ETL jobs (Glue) within a serverless pipeline; a common trap is assuming Lambda alone can handle large CSV files or complex joins, which would exceed its 15-minute timeout and memory limits. Remember the memory tip: “Lambda for light lifts, Glue for heavy hauls”—use Lambda for quick S3-to-Redshift triggers and Glue for serious data wrangling in your serverless ETL pipeline.
This SAP-C02 practice question tests your understanding of design for new solutions. 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 company wants to implement a serverless data processing pipeline on AWS. The pipeline reads CSV files from Amazon S3, transforms the data, and loads it into Amazon Redshift. Which THREE AWS services should be used to build this pipeline?
AWS Database Migration Service (DMS)
Why wrong: DMS is for migrating databases, not for data processing.
Amazon EC2
Why wrong: EC2 is not serverless; it requires management.
AWS Lambda
Lambda can be triggered by S3 events to start the pipeline.
AWS Glue
Glue performs serverless ETL transformations.
Amazon Redshift
Redshift is the target data warehouse for loading transformed data.
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 triggered by S3 events when a CSV file is uploaded, and it can execute lightweight data transformation logic (e.g., parsing CSV rows, filtering, or converting formats) before loading the data into Amazon Redshift. Lambda is serverless, scales automatically, and integrates natively with S3 and Redshift via the AWS SDK, making it ideal for event-driven, short-running transformations in a serverless pipeline.
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 Database Migration Service (DMS)
Why it's wrong here
DMS is for migrating databases, not for data processing.
Amazon EC2
Why it's wrong here
EC2 is not serverless; it requires management.
AWS Lambda
Why this is correct
Lambda can be triggered by S3 events to start the pipeline.
Related concept
Read the scenario before looking for a memorised answer.
AWS Glue
Why this is correct
Glue performs serverless ETL transformations.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Redshift
Why this is correct
Redshift is the target data warehouse for loading transformed data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse AWS DMS (option A) as a data loading tool for Redshift, but DMS is for database migration, not for serverless file transformation and loading from S3.
Detailed technical explanation
Under the hood, Lambda functions have a maximum execution timeout of 15 minutes and a payload size limit of 6 MB for synchronous invocation, so for large CSV files, you would typically use S3 event notifications to trigger Lambda, which then streams the file line-by-line or uses the S3 Select API to filter columns before writing to Redshift via the COPY command or a JDBC connection. In a real-world scenario, if the CSV files exceed 50 MB, you might combine Lambda with AWS Glue (option D) to handle the heavy lifting, as Glue can run Spark jobs for larger datasets while Lambda handles the orchestration.
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
Design for New Solutions — This question tests Design for New Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Lambda — AWS Lambda is correct because it can be triggered by S3 events when a CSV file is uploaded, and it can execute lightweight data transformation logic (e.g., parsing CSV rows, filtering, or converting formats) before loading the data into Amazon Redshift. Lambda is serverless, scales automatically, and integrates natively with S3 and Redshift via the AWS SDK, making it ideal for event-driven, short-running transformations in a serverless pipeline.
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
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