1
AWS Certified Data
Engineer – Associate
(DEA-C01): 150 Advanced
Practice Questions a well
detailed one 2025 /
2026 written and graded
A+ upgraded
, 2
Title
AWS Certified Data Engineer – Associate (DEA-C01): Comprehensive Advanced Practice Exam
with 150 High-Difficulty Questions Covering Data Ingestion, Transformation, Storage
Management, Operations, Security, and Governance for Experienced Cloud Data Professionals
Exam Domains Coverage
This question set covers all four domains of the DEA-C01 exam:
• Domain 1: Data Ingestion and Transformation (34% of scored content)
• Domain 2: Data Store Management (26% of scored content)
• Domain 3: Data Operations and Support (22% of scored content)
• Domain 4: Data Security and Governance (18% of scored content)
Questions 1–150
Domain 1: Data Ingestion and Transformation (Questions 1–51)
Question 1
A data engineer is designing a streaming data ingestion pipeline that must ingest millions of
events per second from thousands of IoT devices. The solution must support real-time
processing with sub-second latency and allow replay of data up to 7 days. Which combination of
AWS services should the engineer use?
A. Amazon Kinesis Data Streams with 7-day extended retention and AWS Lambda
B. Amazon SQS FIFO queues with Amazon EC2 Auto Scaling groups
C. Amazon MSK with Apache Kafka and AWS Glue streaming ETL
D. Amazon Kinesis Data Firehose with Amazon S3 and AWS Glue
- detailed answer 100% correct :- A
Rationale: Amazon Kinesis Data Streams supports high-throughput ingestion (millions of events
per second) with extended retention up to 365 days, enabling data replay. AWS Lambda can
, 3
process records with sub-second latency in a serverless model. SQS FIFO has throughput
limitations (300 messages per second). MSK offers similar capabilities but requires more
operational overhead. Kinesis Data Firehose is designed for near-real-time delivery to
destinations, not sub-second processing.
Question 2
A company needs to ingest 50 TB of historical data from on-premises network-attached storage
(NAS) to Amazon S3. The network bandwidth is limited to 100 Mbps, and the data must be
transferred within 72 hours. Which approach should the data engineer recommend?
A. Use AWS DataSync to transfer data over the internet
B. Use AWS Storage Gateway File Gateway to cache data and sync to S3
C. Use AWS Snowball Edge device to ship data physically
D. Use AWS Database Migration Service (DMS) with full load
- detailed answer 100% correct :- C
Rationale: At 100 Mbps, transferring 50 TB would take approximately 50 days, far exceeding the
72-hour requirement. AWS Snowball Edge provides petabyte-scale physical data transport,
making it the only viable option for this timeframe. DataSync would be too slow over limited
bandwidth. Storage Gateway is for ongoing sync, not initial bulk transfer. DMS is for database
migration, not file-based NAS data.
Question 3
A data engineer is building an AWS Glue ETL job that transforms CSV files stored in Amazon S3
into Parquet format partitioned by date. The job must handle schema evolution and process
only new files incrementally. Which configuration best meets these requirements?
A. Use AWS Glue crawlers with update triggers and enable schema evolution in the table
properties
B. Use AWS Glue bookmarks with enableUpdateCatalog set to true and partitionKeys set to date
C. Use AWS Glue DynamicFrame with resolveChoice and AWS Glue workflows with schedule
triggers
D. Use AWS Glue streaming ETL with micro-batches and checkpointing in S3
- detailed answer 100% correct :- B
, 4
Rationale: AWS Glue bookmarks track processed files for incremental processing.
Setting enableUpdateCatalog to true updates the Data Catalog with schema changes.
Configuring partitionKeys to date creates partitioned output. Crawlers are for schema discovery,
not incremental ETL. resolveChoice handles schema mismatches but doesn't track processed
files. Streaming ETL is for continuous data, not batch CSV-to-Parquet transformation.
Question 4
An e-commerce company receives order data in near-real-time through Amazon Kinesis Data
Streams. The data must be enriched with customer profile information from Amazon
DynamoDB before being loaded into Amazon Redshift for analytics. The enrichment must not
significantly increase latency. Which architecture should the engineer implement?
A. Kinesis Data Streams → AWS Lambda (enrichment with DynamoDB) → Kinesis Data Firehose
→ Redshift
B. Kinesis Data Streams → Kinesis Data Analytics → DynamoDB → Redshift
C. Kinesis Data Streams → Amazon EMR (Spark Streaming) → DynamoDB → Redshift
D. Kinesis Data Streams → AWS Glue streaming ETL → DynamoDB → Redshift
- detailed answer 100% correct :- A
Rationale: AWS Lambda can process each record from Kinesis with low latency, perform
DynamoDB lookups for enrichment, and deliver to Kinesis Data Firehose for batch loading into
Redshift. This minimizes latency while enabling enrichment. Kinesis Data Analytics is for SQL-
based stream processing, not lookup enrichment. EMR and Glue streaming introduce higher
latency due to cluster startup and checkpointing overhead.
Question 5
A data engineer needs to orchestrate a complex ETL pipeline with dependencies: (1) extract
from multiple S3 sources, (2) transform using AWS Glue jobs, (3) load to Amazon Redshift, and
(4) trigger a series of post-load validation queries. The pipeline must handle retries on failure
and send notifications. Which AWS service should the engineer use?
A. AWS Step Functions
B. AWS Glue Workflows
C. Amazon Managed Workflows for Apache Airflow (MWAA)
D. AWS Lambda with Amazon EventBridge
AWS Certified Data
Engineer – Associate
(DEA-C01): 150 Advanced
Practice Questions a well
detailed one 2025 /
2026 written and graded
A+ upgraded
, 2
Title
AWS Certified Data Engineer – Associate (DEA-C01): Comprehensive Advanced Practice Exam
with 150 High-Difficulty Questions Covering Data Ingestion, Transformation, Storage
Management, Operations, Security, and Governance for Experienced Cloud Data Professionals
Exam Domains Coverage
This question set covers all four domains of the DEA-C01 exam:
• Domain 1: Data Ingestion and Transformation (34% of scored content)
• Domain 2: Data Store Management (26% of scored content)
• Domain 3: Data Operations and Support (22% of scored content)
• Domain 4: Data Security and Governance (18% of scored content)
Questions 1–150
Domain 1: Data Ingestion and Transformation (Questions 1–51)
Question 1
A data engineer is designing a streaming data ingestion pipeline that must ingest millions of
events per second from thousands of IoT devices. The solution must support real-time
processing with sub-second latency and allow replay of data up to 7 days. Which combination of
AWS services should the engineer use?
A. Amazon Kinesis Data Streams with 7-day extended retention and AWS Lambda
B. Amazon SQS FIFO queues with Amazon EC2 Auto Scaling groups
C. Amazon MSK with Apache Kafka and AWS Glue streaming ETL
D. Amazon Kinesis Data Firehose with Amazon S3 and AWS Glue
- detailed answer 100% correct :- A
Rationale: Amazon Kinesis Data Streams supports high-throughput ingestion (millions of events
per second) with extended retention up to 365 days, enabling data replay. AWS Lambda can
, 3
process records with sub-second latency in a serverless model. SQS FIFO has throughput
limitations (300 messages per second). MSK offers similar capabilities but requires more
operational overhead. Kinesis Data Firehose is designed for near-real-time delivery to
destinations, not sub-second processing.
Question 2
A company needs to ingest 50 TB of historical data from on-premises network-attached storage
(NAS) to Amazon S3. The network bandwidth is limited to 100 Mbps, and the data must be
transferred within 72 hours. Which approach should the data engineer recommend?
A. Use AWS DataSync to transfer data over the internet
B. Use AWS Storage Gateway File Gateway to cache data and sync to S3
C. Use AWS Snowball Edge device to ship data physically
D. Use AWS Database Migration Service (DMS) with full load
- detailed answer 100% correct :- C
Rationale: At 100 Mbps, transferring 50 TB would take approximately 50 days, far exceeding the
72-hour requirement. AWS Snowball Edge provides petabyte-scale physical data transport,
making it the only viable option for this timeframe. DataSync would be too slow over limited
bandwidth. Storage Gateway is for ongoing sync, not initial bulk transfer. DMS is for database
migration, not file-based NAS data.
Question 3
A data engineer is building an AWS Glue ETL job that transforms CSV files stored in Amazon S3
into Parquet format partitioned by date. The job must handle schema evolution and process
only new files incrementally. Which configuration best meets these requirements?
A. Use AWS Glue crawlers with update triggers and enable schema evolution in the table
properties
B. Use AWS Glue bookmarks with enableUpdateCatalog set to true and partitionKeys set to date
C. Use AWS Glue DynamicFrame with resolveChoice and AWS Glue workflows with schedule
triggers
D. Use AWS Glue streaming ETL with micro-batches and checkpointing in S3
- detailed answer 100% correct :- B
, 4
Rationale: AWS Glue bookmarks track processed files for incremental processing.
Setting enableUpdateCatalog to true updates the Data Catalog with schema changes.
Configuring partitionKeys to date creates partitioned output. Crawlers are for schema discovery,
not incremental ETL. resolveChoice handles schema mismatches but doesn't track processed
files. Streaming ETL is for continuous data, not batch CSV-to-Parquet transformation.
Question 4
An e-commerce company receives order data in near-real-time through Amazon Kinesis Data
Streams. The data must be enriched with customer profile information from Amazon
DynamoDB before being loaded into Amazon Redshift for analytics. The enrichment must not
significantly increase latency. Which architecture should the engineer implement?
A. Kinesis Data Streams → AWS Lambda (enrichment with DynamoDB) → Kinesis Data Firehose
→ Redshift
B. Kinesis Data Streams → Kinesis Data Analytics → DynamoDB → Redshift
C. Kinesis Data Streams → Amazon EMR (Spark Streaming) → DynamoDB → Redshift
D. Kinesis Data Streams → AWS Glue streaming ETL → DynamoDB → Redshift
- detailed answer 100% correct :- A
Rationale: AWS Lambda can process each record from Kinesis with low latency, perform
DynamoDB lookups for enrichment, and deliver to Kinesis Data Firehose for batch loading into
Redshift. This minimizes latency while enabling enrichment. Kinesis Data Analytics is for SQL-
based stream processing, not lookup enrichment. EMR and Glue streaming introduce higher
latency due to cluster startup and checkpointing overhead.
Question 5
A data engineer needs to orchestrate a complex ETL pipeline with dependencies: (1) extract
from multiple S3 sources, (2) transform using AWS Glue jobs, (3) load to Amazon Redshift, and
(4) trigger a series of post-load validation queries. The pipeline must handle retries on failure
and send notifications. Which AWS service should the engineer use?
A. AWS Step Functions
B. AWS Glue Workflows
C. Amazon Managed Workflows for Apache Airflow (MWAA)
D. AWS Lambda with Amazon EventBridge
