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Start a timed practice session focusing on Data Pipeline Architecture Patterns topics from the DEA-C01 question bank.
Start DEA-C01 Practice Quiz →DEA-C01 Data Pipeline Patterns Question Bank (8 Questions)
Browse all 8 practice questions covering Data Pipeline Architecture Patterns for the DEA-C01 certification exam. Answers are intentionally hidden on this page so you can self-test first before checking results in quiz mode.
- Question 1Data Ingestion and Transformation
A data pipeline processes streaming records that arrive out of order due to network delays. The team needs to process records within a 60-second window and handle late arrivals. Which Kinesis Data Analytics feature addresses this?
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Start DEA-C01 Quiz - Question 2Data Ingestion and Transformation
A data engineer needs to deduplicate records in a streaming pipeline before writing to S3. The data arrives via Kinesis Data Streams. What is the recommended approach?
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Start DEA-C01 Quiz - Question 3Data Operations and Support
Which approach BEST handles late-arriving data in a streaming pipeline using Amazon Kinesis Data Analytics?
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Start DEA-C01 Quiz - Question 4Data Ingestion and Transformation
A company receives millions of IoT sensor readings per day and needs to ingest, transform, and load data into Amazon S3 for analytics. Which AWS service provides a fully managed ETL pipeline?
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Start DEA-C01 Quiz - Question 5Data Operations and Support
A data pipeline writes to Amazon Redshift in small, frequent batches. Performance is degraded because each batch triggers many small INSERT operations. Which feature batches writes for better performance?
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Start DEA-C01 Quiz - Question 6Data Ingestion and Transformation
A company is migrating an on-premises Apache Spark ETL pipeline to AWS. They want minimal code changes. Which service is most appropriate?
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Start DEA-C01 Quiz - Question 7Data Operations and Support
Which AWS Step Functions feature allows parallel execution of multiple ETL tasks within a data pipeline?
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Start DEA-C01 Quiz - Question 8Data Ingestion and Transformation
What is the difference between ETL and ELT?
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Key Data Pipeline Patterns Concepts for DEA-C01
DEA-C01 Data Pipeline Patterns Exam Tips
Data Pipeline Architecture Patterns questions in DEA-C01 are typically scenario-based. Focus on data ingestion, transformation, storage optimization, and governance. Priority concepts: pipeline, batch, streaming, lambda architecture, medallion, elt.
What DEA-C01 Expects
- Anchor your answer in choose scalable data pipeline patterns with clear data quality and security controls.
- Data Pipeline Patterns scenarios for DEA-C01 are frequently mapped to Domain 1 (34%), Domain 3 (22%), so read the objective carefully before picking controls or architecture.
- Expect multi-topic scenarios where Data Pipeline Patterns interacts with IAM, networking, storage, or observability patterns rather than appearing as an isolated question.
- When two options are both technically valid, prefer the choice that best aligns with the exam's operational scope (Associate) and vendor best practices.
High-Value Data Pipeline Patterns Concepts
- Know the core Data Pipeline Patterns building blocks cold: pipeline, batch, streaming, lambda architecture.
- Review the edge-case features and limits for medallion, elt; these details are commonly used to differentiate answer choices.
- Practice service-integration reasoning: how Data Pipeline Patterns pairs with AWS Glue, Kinesis, Step Functions, EMR in real deployment patterns.
- For DEA-C01, explain why the chosen Data Pipeline Patterns design meets reliability, security, and cost expectations better than the alternatives.
Common DEA-C01 Traps
- Watch for ignoring partitioning, schema evolution, or query efficiency.
- Questions in Data Ingestion and Transformation often include distractors that look correct for Data Pipeline Patterns but violate least-privilege, durability, or availability requirements.
- Avoid picking options purely by feature name; validate data path, failure handling, and governance impact before answering.
- If the prompt hints at automation or repeatability, eliminate manual-only operational answers first.
Fast Review Checklist
- Can you compare at least two Data Pipeline Patterns implementation paths and justify which one best fits the scenario?
- Can you map the chosen answer back to Data Ingestion and Transformation (34%) outcomes for DEA-C01?
- Can you explain security and access boundaries for Data Pipeline Patterns without relying on default-open assumptions?
- Can you describe how Data Pipeline Patterns integrates with AWS Glue and Kinesis during failure, scaling, and monitoring events?