DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to set up a data ingestion pipeline that reads from Amazon MSK (Managed Streaming for Kafka) and writes to Amazon S3 with transformations. The data is in Avro format and must be converted to Parquet. Which THREE components should be used together? (Choose THREE.)
⚠ Common exam trap
It's easy for candidates to think AWS Glue ETL is required for format conversion in streaming pipelines, but Firehose with Lambda provides a simpler, real-time alternative for Avro-to-Parquet conversion without the overhead of a full ETL job.
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
✓
AWS Lambda function to convert Avro to Parquet as a Firehose transformation
AWS Lambda can be used as a transformation function within a Kinesis Data Firehose delivery stream to convert Avro records to Parquet format before delivery to S3. This is a serverless, real-time approach that integrates directly with Firehose's transformation capabilities, avoiding the need for separate compute resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Lambda function to convert Avro to Parquet as a Firehose transformation
Why this is correct
Lambda can be used in Firehose to perform data transformation.
- ✗
Amazon Athena to convert the data format
Why it's wrong here
Athena is for querying, not for real-time transformation in the pipeline.
- ✓
Amazon Kinesis Data Firehose delivery stream with MSK as source
Why this is correct
Firehose can ingest from MSK and deliver to S3.
- ✓
Amazon MSK cluster as the data source
Why this is correct
MSK is the source of streaming data.
- ✗
AWS Glue ETL job to read from MSK
Why it's wrong here
Glue can read from MSK but not directly as a Firehose source; it would be a separate approach.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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