Preparing for the AWS Certified Data Engineer – Associate DEA-C01 can feel overwhelming, especially when you’re unsure what kind of questions to expect on exam day. This practice test is designed to give you a realistic preview of the exam format while helping you strengthen your understanding of key concepts. Instead of just memorizing answers, you’ll get a chance to think through scenarios, improve your accuracy, and build confidence. Use this as part of your daily study routine to identify weak areas and gradually improve your performance.
Updated for 2026: This guide provides a structured approach to help you prepare effectively, understand key concepts, and practice real exam-level questions.
How to Use This Practice Test
- Start by reviewing key concepts before attempting questions
- Take the test in a timed environment
- Analyze your mistakes and revisit weak areas
Why This Practice Test Matters
This practice test is designed to simulate the real exam environment and help you identify knowledge gaps, improve accuracy, and build confidence.
| Exam Name | DEA-C01 Practice Exam – AWS Certified Data Engineer Associate (2026 Updated) |
|---|---|
| Exam Provider | Amazon Web Services (AWS) |
| Certification Type | Associate-Level Certification (Data Engineering, ETL Pipelines & Analytics) |
| Total Practice Questions | 150 Advanced MCQs (Scenario-Based + Data Pipelines + Streaming + ETL + Analytics) |
| Exam Domains Covered | • Data Ingestion & Transformation (Kinesis, Firehose, Glue, Lambda) • Data Storage & Data Lakes (S3, Lake Formation, Data Catalog) • Data Processing (Batch vs Streaming, ETL Pipelines, Spark) • Data Analytics (Redshift, Athena, QuickSight) • Data Orchestration (Step Functions, EventBridge) • Data Security & Governance (IAM, Encryption, Access Control) • Performance Optimization & Cost Management (Partitioning, Parquet, Compression) |
| Questions in Real Exam | • Total: ~65 Questions • Scenario-heavy with real-world pipeline design • Focus on service selection, cost optimization, and performance tuning |
| Exam Duration | • Total Time: 130 Minutes • Moderate to fast pace with scenario-based decision questions • Requires strong understanding of AWS data services |
| Passing Score | • Scaled Score: 720 / 1000 • Requires solid knowledge of pipelines, ETL, and analytics services • Emphasis on real-world data engineering decisions |
| Question Format | • Multiple Choice & Multiple Response • Scenario-Based Data Pipeline Questions • ETL & Streaming Use Cases • Cost Optimization & Performance Tuning • Data Governance & Security Questions |
| Difficulty Level | Intermediate to Advanced (Hands-On Data Engineering + Scenario Thinking) |
| Key Knowledge Areas | • Streaming vs batch processing (Kinesis vs Glue vs Lambda) • Data lake design on S3 (partitioning, formats, lifecycle policies) • ETL pipelines (Glue, Spark, transformations) • Analytics tools (Redshift, Athena, QuickSight) • Data orchestration (Step Functions, EventBridge) • Cost optimization (Parquet, compression, partition pruning) • Security (IAM roles, encryption, Lake Formation governance) |
| Common Exam Traps | • Choosing Glue instead of Kinesis for real-time streaming • Ignoring partitioning in S3 (leading to high Athena costs) • Using CSV/JSON instead of Parquet for analytics • Confusing Kinesis Data Streams vs Firehose • Overusing EC2 instead of serverless services • Ignoring data lifecycle policies and storage tiers • Not handling duplicate or late-arriving data properly |
| Skills Developed | • Designing scalable data pipelines on AWS • Building real-time and batch processing systems • Optimizing data storage and query performance • Implementing cost-efficient analytics solutions • Managing data governance and security • Troubleshooting pipeline failures and bottlenecks |
| Study Strategy | • Focus on real-world pipeline scenarios (streaming vs batch) • Practice ETL workflows using Glue and Lambda • Learn differences between AWS data services deeply • Optimize queries using Parquet, partitioning, and compression • Take timed mock exams to improve speed and accuracy • Review explanations to understand hidden exam traps • Strengthen weak areas with targeted practice sets |
| Best For | • Data engineers and analytics engineers • Cloud engineers working with data pipelines • Developers building ETL and streaming solutions • Professionals transitioning into data engineering roles |
| Career Benefits | • Validates real-world data engineering skills on AWS • Opens roles in data engineering, analytics, and big data • Enhances pipeline design and optimization expertise • Increases earning potential in high-demand data roles • Provides strong foundation for advanced AWS certifications |
| Updated | 2026 Latest Version – Based on AWS DEA-C01 Exam Guide & Real Exam Patterns |
1.
A company needs to ingest streaming data in real time. What is BEST?
A. S3
B. Kinesis Data Streams
C. RDS
D. EBS
Answer: B
Rationale: Kinesis Data Streams enables real-time data ingestion and processing with low latency, making it ideal for streaming use cases like IoT, logs, and clickstream data.
2.
A company wants to transform data using serverless ETL. What is BEST?
A. EC2
B. Glue
C. RDS
D. Lambda
Answer: B
Rationale: AWS Glue is a serverless ETL service that automates data extraction, transformation, and loading with built-in cataloging and schema discovery.
3.
A company wants to query data stored in S3 using SQL. What is BEST?
A. DynamoDB
B. Athena
C. RDS
D. Redshift
Answer: B
Rationale: Athena allows querying data directly in S3 using SQL without managing infrastructure.
4.
A company needs a data warehouse solution. What is BEST?
A. DynamoDB
B. Redshift
C. RDS
D. S3
Answer: B
Rationale: Redshift is optimized for analytics and large-scale data warehousing.
5.
A company wants to process streaming data with transformations. What is BEST?
A. Kinesis Data Analytics
B. S3
C. RDS
D. EBS
Answer: A
Rationale: Kinesis Data Analytics processes streaming data in real time with SQL or Apache Flink.
6.
A company needs scalable object storage for a data lake. What is BEST?
A. EBS
B. S3
C. RDS
D. EC2
Answer: B
Rationale: S3 provides durable, scalable storage for data lakes.
7.
A company wants to orchestrate ETL workflows. What is BEST?
A. Step Functions
B. EC2
C. S3
D. Lambda
Answer: A
Rationale: Step Functions orchestrates workflows across AWS services.
8.
A company needs to catalog metadata for datasets. What is BEST?
A. Glue Data Catalog
B. RDS
C. DynamoDB
D. S3
Answer: A
Rationale: Glue Data Catalog stores metadata and enables schema discovery.
9.
A company wants to ingest data from on-prem systems continuously. What is BEST?
A. Snowball
B. DataSync
C. Lambda
D. S3
Answer: B
Rationale: DataSync transfers data continuously between on-prem and AWS.
10.
A company needs real-time dashboards. What is BEST?
A. QuickSight
B. RDS
C. DynamoDB
D. S3
Answer: A
Rationale: QuickSight provides visualization and dashboards.
11.
A company wants to compress and optimize S3 data for analytics. What is BEST?
A. CSV
B. Parquet
C. JSON
D. TXT
Answer: B
Rationale: Parquet is a columnar format optimized for analytics.
12.
A company needs event-driven processing. What is BEST?
A. Lambda
B. EC2
C. RDS
D. EBS
Answer: A
Rationale: Lambda enables event-driven processing.
13.
A company wants to automate data pipeline triggers. What is BEST?
A. EventBridge
B. EC2
C. S3
D. RDS
Answer: A
Rationale: EventBridge triggers workflows based on events.
14.
A company wants to process batch data. What is BEST?
A. Glue
B. Kinesis
C. DynamoDB
D. RDS
Answer: A
Rationale: Glue is ideal for batch ETL.
15.
A company needs schema evolution support. What is BEST?
A. Glue
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue supports schema evolution.
16.
A company wants to store structured data for analytics. What is BEST?
A. Redshift
B. DynamoDB
C. S3
D. EC2
Answer: A
Rationale: Redshift is optimized for structured analytics.
17.
A company needs low-latency NoSQL storage. What is BEST?
A. RDS
B. DynamoDB
C. Redshift
D. S3
Answer: B
Rationale: DynamoDB provides low-latency NoSQL storage.
18.
A company wants streaming ingestion at scale. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EBS
Answer: A
Rationale: Kinesis scales for streaming ingestion.
19.
A company wants data transformation in real time. What is BEST?
A. Kinesis Data Analytics
B. Glue
C. RDS
D. S3
Answer: A
Rationale: Kinesis Data Analytics processes streaming data.
20.
A company wants to query logs stored in S3. What is BEST?
A. Athena
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Athena queries S3 logs efficiently.
21.
A company needs to orchestrate pipelines. What is BEST?
A. Step Functions
B. EC2
C. S3
D. Lambda
Answer: A
Rationale: Step Functions orchestrates pipelines.
22.
A company wants serverless analytics. What is BEST?
A. Athena
B. RDS
C. EC2
D. DynamoDB
Answer: A
Rationale: Athena provides serverless analytics.
23.
A company wants BI dashboards. What is BEST?
A. QuickSight
B. RDS
C. S3
D. EC2
Answer: A
Rationale: QuickSight provides BI dashboards.
24.
A company needs ETL automation. What is BEST?
A. Glue
B. EC2
C. RDS
D. S3
Answer: A
Rationale: Glue automates ETL.
25.
A company wants data cataloging. What is BEST?
A. Glue Data Catalog
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue Data Catalog manages metadata.
26.
A company needs batch processing. What is BEST?
A. Glue
B. Kinesis
C. DynamoDB
D. Lambda
Answer: A
Rationale: Glue is optimized for batch processing.
27.
A company wants real-time ingestion. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles streaming ingestion.
28.
A company wants structured analytics. What is BEST?
A. Redshift
B. DynamoDB
C. S3
D. EC2
Answer: A
Rationale: Redshift is optimized for analytics.
29.
A company needs workflow automation. What is BEST?
A. Step Functions
B. EC2
C. S3
D. Lambda
Answer: A
Rationale: Step Functions orchestrates workflows.
30.
A company wants serverless ETL. What is BEST?
A. Glue
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: Glue provides serverless ETL.
31.
A company needs to process streaming data and store it in S3 for analytics with minimal latency. What is BEST?
A. Glue
B. Kinesis Data Firehose
C. RDS
D. EC2
Answer: B
Rationale: Kinesis Data Firehose is designed to ingest streaming data and deliver it directly to S3 with minimal operational overhead. It automatically handles scaling, buffering, and delivery, making it ideal for near real-time data pipelines.
32.
A company wants to reduce query cost in Athena. What is BEST?
A. Increase EC2 size
B. Use Parquet format
C. Use CSV
D. Use JSON
Answer: B
Rationale: Athena charges based on data scanned. Using columnar formats like Parquet reduces the amount of data scanned, improving performance and lowering costs significantly compared to row-based formats.
33.
A company needs to orchestrate multiple ETL jobs with dependencies. What is BEST?
A. Lambda
B. Step Functions
C. EC2
D. S3
Answer: B
Rationale: Step Functions allow defining workflows with dependencies and error handling, making it ideal for orchestrating complex ETL pipelines.
34.
A company needs real-time anomaly detection on streaming data. What is BEST?
A. Glue
B. Kinesis Data Analytics
C. RDS
D. S3
Answer: B
Rationale: Kinesis Data Analytics processes streaming data in real time using SQL or Apache Flink, enabling anomaly detection and transformation.
35.
A company wants to store raw data in a data lake. What is BEST?
A. RDS
B. S3
C. DynamoDB
D. EC2
Answer: B
Rationale: S3 is the foundation of AWS data lakes, providing scalable and durable storage for raw and processed data.
36.
A company needs schema discovery for incoming data. What is BEST?
A. Glue Data Catalog
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue Data Catalog automatically discovers schema and stores metadata for datasets.
37.
A company wants to process large-scale batch ETL jobs. What is BEST?
A. Kinesis
B. Glue
C. DynamoDB
D. Lambda
Answer: B
Rationale: Glue is optimized for batch ETL workloads.
38.
A company needs to ingest IoT data at scale. What is BEST?
A. RDS
B. Kinesis Data Streams
C. S3
D. EC2
Answer: B
Rationale: Kinesis Data Streams supports scalable ingestion of IoT data.
39.
A company wants to optimize partitioning for Athena queries. What is BEST?
A. Use single file
B. Partition by date
C. Use CSV
D. Use JSON
Answer: B
Rationale: Partitioning by commonly queried fields (like date) reduces data scanned and improves query performance.
40.
A company wants serverless orchestration of data pipelines. What is BEST?
A. EC2
B. Step Functions
C. RDS
D. DynamoDB
Answer: B
Rationale: Step Functions orchestrate serverless workflows efficiently.
41.
A company needs to transform streaming data before storing. What is BEST?
A. Glue
B. Lambda with Kinesis
C. RDS
D. S3
Answer: B
Rationale: Lambda can process streaming data from Kinesis in real time, enabling transformations before storage.
42.
A company wants to analyze large datasets quickly. What is BEST?
A. DynamoDB
B. Redshift
C. RDS
D. EC2
Answer: B
Rationale: Redshift is optimized for analytics and large-scale queries.
43.
A company needs near real-time dashboards. What is BEST?
A. QuickSight
B. RDS
C. S3
D. EC2
Answer: A
Rationale: QuickSight provides real-time dashboards and visualizations.
44.
A company wants to optimize storage costs in S3. What is BEST?
A. Use Standard only
B. Use lifecycle policies
C. Use EC2
D. Use RDS
Answer: B
Rationale: Lifecycle policies automatically move data to cheaper storage classes.
45.
A company needs event-driven data processing. What is BEST?
A. Lambda
B. EC2
C. RDS
D. EBS
Answer: A
Rationale: Lambda enables event-driven processing.
46.
A company wants to catalog datasets automatically. What is BEST?
A. Glue
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue automates data cataloging.
47.
A company needs batch analytics on S3 data. What is BEST?
A. Athena
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Athena queries S3 data efficiently.
48.
A company wants streaming ingestion pipeline. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis enables streaming ingestion.
49.
A company needs ETL automation. What is BEST?
A. Glue
B. EC2
C. RDS
D. S3
Answer: A
Rationale: Glue automates ETL pipelines.
50.
A company wants to optimize query performance. What is BEST?
A. Use Parquet
B. Use CSV
C. Use JSON
D. Use TXT
Answer: A
Rationale: Parquet improves performance by reducing scanned data.
51.
A company needs streaming analytics. What is BEST?
A. Kinesis Data Analytics
B. Glue
C. RDS
D. S3
Answer: A
Rationale: Kinesis Data Analytics processes streams.
52.
A company wants workflow automation. What is BEST?
A. Step Functions
B. EC2
C. S3
D. Lambda
Answer: A
Rationale: Step Functions orchestrate workflows.
53.
A company needs real-time ingestion. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles streaming ingestion.
54.
A company wants serverless analytics. What is BEST?
A. Athena
B. RDS
C. EC2
D. DynamoDB
Answer: A
Rationale: Athena provides serverless analytics.
55.
A company needs BI dashboards. What is BEST?
A. QuickSight
B. RDS
C. S3
D. EC2
Answer: A
Rationale: QuickSight provides dashboards.
56.
A company wants scalable storage. What is BEST?
A. S3
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: S3 provides scalable storage.
57.
A company needs ETL workflows. What is BEST?
A. Glue
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: Glue handles ETL workflows.
58.
A company needs streaming pipeline. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles streaming.
59.
A company wants data catalog. What is BEST?
A. Glue Data Catalog
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue stores metadata.
60.
A company wants analytics platform. What is BEST?
A. Redshift
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Redshift provides analytics.
61.
A company’s Kinesis Data Stream is experiencing shard-level throttling. What is the BEST solution?
A. Increase EC2 size
B. Increase shard count
C. Use S3
D. Use RDS
Answer: B
Rationale: Each shard in Kinesis has fixed throughput limits. Increasing shard count distributes load across more shards, reducing throttling and improving ingestion performance.
62.
A company wants to reduce Athena query costs on large datasets. What is BEST?
A. Use CSV
B. Use Parquet + partitioning
C. Use JSON
D. Use TXT
Answer: B
Rationale: Using Parquet reduces scanned data, and partitioning limits the data accessed by queries, significantly reducing cost and improving performance.
63.
A company needs to handle late-arriving data in streaming pipelines. What is BEST?
A. Ignore data
B. Use windowing in Kinesis Data Analytics
C. Use S3
D. Use RDS
Answer: B
Rationale: Windowing allows processing data within time boundaries, handling late-arriving events correctly.
64.
A company wants to ensure schema consistency across pipelines. What is BEST?
A. Use Glue Data Catalog
B. Use EC2
C. Use RDS
D. Use Lambda
Answer: A
Rationale: Glue Data Catalog maintains schema definitions and ensures consistency across data pipelines.
65.
A company needs fault-tolerant ETL pipelines. What is BEST?
A. EC2
B. Step Functions + retries
C. RDS
D. S3
Answer: B
Rationale: Step Functions provide error handling, retries, and orchestration, improving fault tolerance.
66.
A company wants to process streaming data with minimal operational overhead. What is BEST?
A. Kinesis Data Firehose
B. Kinesis Streams + EC2
C. RDS
D. S3
Answer: A
Rationale: Firehose is fully managed and requires minimal setup.
67.
A company needs to join streaming data with reference data. What is BEST?
A. Kinesis Data Analytics
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis Data Analytics supports joining streams with reference data.
68.
A company wants to optimize Redshift performance. What is BEST?
A. Use row storage
B. Use sort keys and distribution keys
C. Use CSV
D. Use JSON
Answer: B
Rationale: Sort and distribution keys improve query performance.
69.
A company needs near real-time ETL. What is BEST?
A. Glue
B. Lambda + Kinesis
C. RDS
D. S3
Answer: B
Rationale: Lambda processes streaming data in real time.
70.
A company wants to automate data pipeline triggers. What is BEST?
A. EventBridge
B. EC2
C. S3
D. RDS
Answer: A
Rationale: EventBridge triggers workflows based on events.
71.
A company needs to process large batch datasets. What is BEST?
A. Kinesis
B. Glue
C. DynamoDB
D. Lambda
Answer: B
Rationale: Glue is optimized for batch processing.
72.
A company wants to reduce storage costs in S3. What is BEST?
A. Use Standard
B. Lifecycle policies
C. EC2
D. RDS
Answer: B
Rationale: Lifecycle policies move data to cheaper tiers.
73.
A company wants to process events from S3 uploads. What is BEST?
A. Lambda trigger
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: Lambda can be triggered by S3 events.
74.
A company needs high-throughput ingestion. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis supports high throughput ingestion.
75.
A company wants to orchestrate multiple pipelines. What is BEST?
A. Step Functions
B. EC2
C. S3
D. Lambda
Answer: A
Rationale: Step Functions orchestrate workflows.
76.
A company needs schema evolution support. What is BEST?
A. Glue
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue supports schema evolution.
77.
A company wants serverless analytics. What is BEST?
A. Athena
B. RDS
C. EC2
D. DynamoDB
Answer: A
Rationale: Athena provides serverless querying.
78.
A company wants BI dashboards. What is BEST?
A. QuickSight
B. RDS
C. S3
D. EC2
Answer: A
Rationale: QuickSight provides dashboards.
79.
A company wants to optimize ETL performance. What is BEST?
A. Use partitioning
B. Use CSV
C. Use JSON
D. Use TXT
Answer: A
Rationale: Partitioning improves ETL efficiency.
80.
A company needs real-time analytics. What is BEST?
A. Kinesis Data Analytics
B. Glue
C. RDS
D. S3
Answer: A
Rationale: Kinesis processes streaming data.
81.
A company needs data cataloging. What is BEST?
A. Glue Data Catalog
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue manages metadata.
82.
A company needs streaming ingestion. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles streaming ingestion.
83.
A company wants ETL automation. What is BEST?
A. Glue
B. EC2
C. RDS
D. S3
Answer: A
Rationale: Glue automates ETL.
84.
A company needs analytics at scale. What is BEST?
A. Redshift
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Redshift handles analytics.
85.
A company wants workflow automation. What is BEST?
A. Step Functions
B. EC2
C. S3
D. Lambda
Answer: A
Rationale: Step Functions orchestrate workflows.
86.
A company needs scalable storage. What is BEST?
A. S3
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: S3 provides scalable storage.
87.
A company needs data transformation. What is BEST?
A. Glue
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: Glue handles transformation.
88.
A company needs real-time pipeline. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles streaming pipelines.
89.
A company wants query optimization. What is BEST?
A. Parquet
B. CSV
C. JSON
D. TXT
Answer: A
Rationale: Parquet improves performance.
90.
A company needs analytics platform. What is BEST?
A. Redshift
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Redshift is optimized for analytics.
91.
A Kinesis Data Streams consumer falls behind during peak traffic. What is the BEST fix?
A. Increase EC2 size
B. Increase shard count
C. Use S3
D. Use RDS
Answer: B
Rationale: Each shard provides fixed read/write throughput. Increasing shard count raises parallelism and throughput, allowing consumers to keep up with incoming data and reducing iterator age/lag.
92.
Athena queries are slow and expensive due to many small files in S3. What is BEST?
A. Use CSV
B. Compact files into larger Parquet files
C. Increase EC2
D. Use RDS
Answer: B
Rationale: Small files cause high overhead and more data scans. Compaction into larger columnar Parquet files reduces I/O, improves predicate pushdown, and lowers cost and latency.
93.
A company needs exactly-once processing for streaming ETL. What is BEST?
A. Kinesis + Lambda (default)
B. Kinesis Data Analytics (Flink) with checkpoints
C. S3 batch
D. RDS
Answer: B
Rationale: Flink supports stateful processing with checkpointing and exactly-once semantics, ensuring no duplicates during failures and restarts.
94.
A Glue job fails intermittently due to transient errors. What is BEST?
A. Ignore failures
B. Add retries and job bookmarks
C. Use EC2
D. Use RDS
Answer: B
Rationale: Retries handle transient issues, and job bookmarks prevent reprocessing already processed data, improving reliability and idempotency.
95.
A company sees partition skew in S3 data lake queries. What is BEST?
A. Use single partition
B. Redesign partitioning strategy
C. Use CSV
D. Use JSON
Answer: B
Rationale: Skewed partitions cause uneven data distribution and slow queries. Redesigning partitions (e.g., by date + hashed key) balances load and improves performance.
96.
A company wants near real-time delivery of logs to S3 with minimal ops. What is BEST?
A. Kinesis Data Streams + EC2
B. Kinesis Data Firehose
C. Glue
D. RDS
Answer: B
Rationale: Firehose is fully managed, buffers, transforms (optional), and delivers to S3 with minimal configuration and auto-scaling.
97.
A pipeline must enrich streaming data with a reference dataset updated hourly. What is BEST?
A. Hardcode values
B. Kinesis Data Analytics with reference data refresh
C. RDS joins
D. S3 batch
Answer: B
Rationale: Kinesis Data Analytics (Flink/SQL) supports joining streams with reference data that can be periodically refreshed, enabling low-latency enrichment.
98.
A company wants to minimize Athena scan size for time-based queries. What is BEST?
A. No partitioning
B. Partition by date and use Parquet
C. Use JSON
D. Use TXT
Answer: B
Rationale: Partition pruning limits scanned data to relevant partitions; Parquet further reduces bytes scanned via columnar storage and compression.
99.
A data pipeline requires idempotent processing for retries. What is BEST?
A. Ignore duplicates
B. Use deterministic keys + upserts
C. Use EC2
D. Use RDS only
Answer: B
Rationale: Idempotency ensures repeated processing yields the same result. Using deterministic keys and upserts (merge) prevents duplicates during retries.
100.
A company needs low-latency transformation on S3 PUT events. What is BEST?
A. Glue batch
B. Lambda triggered by S3
C. EC2
D. RDS
Answer: B
Rationale: S3 event notifications can trigger Lambda for near real-time processing, ideal for lightweight transformations without managing servers.
101.
A Redshift cluster shows skewed data distribution. What is BEST?
A. Use random keys
B. Choose proper distribution key
C. Use CSV
D. Increase nodes only
Answer: B
Rationale: Correct distribution keys colocate related data and balance slices, reducing data movement and improving query performance.
102.
A company wants to manage schema versions across producers and consumers. What is BEST?
A. Hardcode schema
B. Use Glue Schema Registry
C. Use EC2
D. Use RDS
Answer: B
Rationale: Schema Registry manages versions and compatibility, preventing breaking changes in streaming pipelines.
103.
A pipeline must guarantee at-least-once delivery with durability. What is BEST?
A. SQS standard queue
B. SNS
C. Lambda only
D. EC2
Answer: A
Rationale: SQS Standard provides at-least-once delivery and durability, suitable for decoupled, resilient pipelines (handle duplicates downstream).
104.
A company wants to schedule ETL jobs based on cron. What is BEST?
A. EC2 cron
B. EventBridge schedules
C. RDS
D. S3
Answer: B
Rationale: EventBridge supports cron/rate expressions to trigger workflows serverlessly and reliably.
105.
A pipeline suffers from duplicate records due to retries. What is BEST?
A. Ignore duplicates
B. Deduplicate using primary keys/windowing
C. Use EC2
D. Use RDS
Answer: B
Rationale: Deduplication (e.g., windowed de-dupe or primary keys) ensures correctness when at-least-once systems reprocess events.
106.
A company needs to transform petabyte-scale historical data. What is BEST?
A. Lambda
B. Glue/Spark
C. RDS
D. EC2 single node
Answer: B
Rationale: Distributed processing with Spark (Glue) handles large-scale batch transformations efficiently with parallelism.
107.
A company wants to reduce Redshift storage cost while keeping performance. What is BEST?
A. Use row storage
B. Use columnar compression encodings
C. Use JSON
D. Use TXT
Answer: B
Rationale: Columnar compression reduces storage and I/O, improving query speed and lowering cost.
108.
A streaming app needs ordered processing per key. What is BEST?
A. Random shards
B. Partition key in Kinesis
C. S3
D. RDS
Answer: B
Rationale: Kinesis guarantees ordering within a shard; using a partition key ensures all events for a key land in the same shard and maintain order.
109.
A company needs CDC (change data capture) from RDS to S3. What is BEST?
A. Glue batch
B. DMS with CDC to S3
C. Lambda
D. EC2
Answer: B
Rationale: DMS supports ongoing replication (CDC), capturing inserts/updates/deletes and delivering to S3 for downstream processing.
110.
A pipeline needs secure, cross-account data access to S3. What is BEST?
A. Public buckets
B. IAM roles with bucket policies
C. EC2
D. RDS
Answer: B
Rationale: Cross-account IAM roles and bucket policies enable secure access without exposing data publicly.
111.
A company wants near real-time BI on streaming data. What is BEST?
A. Glue only
B. Kinesis + Redshift (streaming ingestion) + QuickSight
C. RDS
D. S3 only
Answer: B
Rationale: Stream into Redshift for low-latency analytics, then visualize with QuickSight for near real-time dashboards.
112.
Athena queries frequently scan unnecessary columns. What is BEST?
A. Use CSV
B. Use Parquet with column pruning
C. Use JSON
D. Use TXT
Answer: B
Rationale: Columnar formats enable column pruning so only needed columns are read, reducing scan size and cost.
113.
A company needs to trigger ETL after file arrival in S3 with dependency checks. What is BEST?
A. EC2 polling
B. S3 events + Step Functions
C. RDS
D. Lambda only
Answer: B
Rationale: S3 events start the workflow; Step Functions manage dependencies, retries, and sequencing for reliable orchestration.
114.
A pipeline requires data quality checks before loading to Redshift. What is BEST?
A. Skip checks
B. Glue job with validation steps
C. EC2
D. RDS
Answer: B
Rationale: Implement validation rules (null checks, ranges, schema) in Glue before loading to ensure data integrity.
115.
A company wants to avoid reprocessing old data in Glue. What is BEST?
A. Full reload
B. Use job bookmarks
C. Use CSV
D. Use JSON
Answer: B
Rationale: Job bookmarks track processed data and enable incremental processing, reducing cost and duplication.
116.
A streaming pipeline must scale consumers independently. What is BEST?
A. Single consumer
B. Enhanced fan-out in Kinesis
C. S3
D. RDS
Answer: B
Rationale: Enhanced fan-out provides dedicated throughput per consumer, reducing contention and latency.
117.
A company wants to reduce latency of frequent dimension lookups in ETL. What is BEST?
A. RDS joins
B. Cache in ElastiCache
C. S3
D. EC2
Answer: B
Rationale: Caching hot reference data in memory reduces lookup latency and offloads databases.
118.
A company needs unified governance and access control for data lake tables. What is BEST?
A. IAM only
B. Lake Formation
C. EC2
D. RDS
Answer: B
Rationale: Lake Formation centralizes permissions, governance, and fine-grained access for data lake resources.
119.
A pipeline must handle schema changes without breaking consumers. What is BEST?
A. Strict schema
B. Backward-compatible schema evolution
C. EC2
D. RDS
Answer: B
Rationale: Backward-compatible changes (e.g., adding optional fields) prevent breaking existing consumers in streaming systems.
120.
A company wants cost-efficient long-term storage for infrequently accessed data in a data lake. What is BEST?
A. S3 Standard only
B. S3 lifecycle to Glacier tiers
C. RDS
D. EC2
Answer: B
Rationale: Lifecycle policies transition cold data to Glacier/Deep Archive, reducing storage cost while retaining durability.
121.
A streaming pipeline must handle out-of-order events while maintaining correctness. What is BEST?
A. Ignore late data
B. Use watermarking in stream processing
C. Use S3
D. Use RDS
Answer: B
Rationale: Watermarking allows the system to manage out-of-order events by defining how long to wait for late data, ensuring correctness without indefinite delays.
122.
A company wants to replay historical streaming data for debugging. What is BEST?
A. Delete data
B. Use Kinesis retention increase or S3 archive replay
C. Use RDS
D. Use EC2
Answer: B
Rationale: Increasing retention in Kinesis or storing data in S3 enables replay of historical data, which is critical for debugging and reprocessing pipelines.
123.
A company needs to avoid duplicate processing in a streaming pipeline. What is BEST?
A. Ignore duplicates
B. Use deduplication keys
C. Use EC2
D. Use RDS
Answer: B
Rationale: Deduplication keys ensure that repeated events are identified and ignored, maintaining data integrity.
124.
A company needs low-latency joins between streaming and static datasets. What is BEST?
A. Batch join
B. Stream processing with reference data
C. RDS
D. S3
Answer: B
Rationale: Stream processing engines can join real-time data with static reference datasets efficiently.
125.
A company wants to reduce cold start latency in Lambda for pipelines. What is BEST?
A. Increase memory
B. Use provisioned concurrency
C. Use EC2
D. Use RDS
Answer: B
Rationale: Provisioned concurrency keeps functions warm, reducing cold start latency.
126.
A company needs to manage pipeline retries safely. What is BEST?
A. Ignore failures
B. Idempotent processing
C. Use EC2
D. Use RDS
Answer: B
Rationale: Idempotent processing ensures retries do not cause duplicate results.
127.
A company wants to optimize Redshift query performance. What is BEST?
A. Use random keys
B. Use sort and distribution keys
C. Use CSV
D. Use JSON
Answer: B
Rationale: Proper key design improves performance by minimizing data movement.
128.
A company needs schema evolution in streaming pipelines. What is BEST?
A. Hardcode schema
B. Schema registry with compatibility rules
C. Use EC2
D. Use RDS
Answer: B
Rationale: Schema registry ensures backward/forward compatibility, preventing pipeline breakage when producers evolve schemas.
129.
A company wants near real-time ingestion with buffering and transformation. What is BEST?
A. Kinesis Data Firehose
B. Kinesis Streams + EC2
C. RDS
D. S3
Answer: A
Rationale: Firehose buffers, optionally transforms, and delivers data to destinations with minimal ops.
130.
A company needs to orchestrate complex pipelines with branching logic. What is BEST?
A. Lambda
B. Step Functions
C. EC2
D. S3
Answer: B
Rationale: Step Functions support branching, retries, and state management for complex workflows.
131.
A company wants to reduce S3 storage cost for old data. What is BEST?
A. Delete data
B. Lifecycle policies to Glacier
C. Use EC2
D. Use RDS
Answer: B
Rationale: Lifecycle policies automatically transition data to cheaper storage classes like Glacier.
132.
A company needs to query nested JSON efficiently in S3. What is BEST?
A. CSV
B. Parquet with flattening
C. TXT
D. EC2
Answer: B
Rationale: Converting JSON to Parquet with flattened schema improves performance and reduces scan cost.
133.
A company wants exactly-once ETL semantics. What is BEST?
A. Lambda default
B. Flink with checkpointing
C. EC2
D. RDS
Answer: B
Rationale: Flink ensures exactly-once processing with state checkpoints.
134.
A company needs secure access to S3 data lake across accounts. What is BEST?
A. Public access
B. IAM roles + bucket policies
C. EC2
D. RDS
Answer: B
Rationale: IAM roles and bucket policies provide secure cross-account access.
135.
A company wants real-time anomaly detection. What is BEST?
A. Glue
B. Kinesis Data Analytics
C. RDS
D. S3
Answer: B
Rationale: Kinesis Data Analytics processes streams in real time.
136.
A company needs to reduce Athena query latency. What is BEST?
A. Use CSV
B. Use partitioning + Parquet
C. Use JSON
D. Use TXT
Answer: B
Rationale: Partitioning and Parquet reduce scan size and improve performance.
137.
A company needs reliable event-driven pipelines. What is BEST?
A. EC2
B. EventBridge + Lambda
C. RDS
D. S3
Answer: B
Rationale: EventBridge triggers Lambda reliably for event-driven workflows.
138.
A company wants to avoid small file issues in S3. What is BEST?
A. Use more small files
B. File compaction
C. Use JSON
D. Use TXT
Answer: B
Rationale: Compaction reduces overhead and improves query performance.
139.
A company needs high-throughput ingestion. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles high throughput streaming ingestion.
140.
A company needs to track data lineage. What is BEST?
A. Glue Data Catalog + Lake Formation
B. EC2
C. RDS
D. S3
Answer: A
Rationale: Glue and Lake Formation provide metadata and lineage tracking.
141.
A company wants automated ETL scheduling. What is BEST?
A. EC2 cron
B. EventBridge
C. RDS
D. S3
Answer: B
Rationale: EventBridge schedules ETL workflows.
142.
A company needs streaming joins with low latency. What is BEST?
A. Batch processing
B. Kinesis Data Analytics
C. RDS
D. S3
Answer: B
Rationale: Kinesis enables low-latency joins.
143.
A company wants to reduce pipeline cost. What is BEST?
A. Use EC2
B. Use serverless services
C. Use RDS
D. Use DynamoDB
Answer: B
Rationale: Serverless services reduce operational cost.
144.
A company needs scalable analytics. What is BEST?
A. Redshift
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Redshift handles analytics.
145.
A company wants BI dashboards. What is BEST?
A. QuickSight
B. RDS
C. S3
D. EC2
Answer: A
Rationale: QuickSight provides visualization.
146.
A company needs ETL automation. What is BEST?
A. Glue
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: Glue automates ETL.
147.
A company needs data cataloging. What is BEST?
A. Glue Data Catalog
B. RDS
C. DynamoDB
D. EC2
Answer: A
Rationale: Glue manages metadata.
148.
A company needs streaming pipeline. What is BEST?
A. Kinesis
B. S3
C. RDS
D. EC2
Answer: A
Rationale: Kinesis handles streaming.
149.
A company wants query optimization. What is BEST?
A. Parquet
B. CSV
C. JSON
D. TXT
Answer: A
Rationale: Parquet improves performance.
150.
A company needs analytics platform. What is BEST?
A. Redshift
B. DynamoDB
C. RDS
D. EC2
Answer: A
Rationale: Redshift is optimized for analytics.
Frequently Asked Questions
Is this AWS Certified Data Engineer – Associate DEA-C01 practice test similar to the real exam?
Yes, this practice test is designed to reflect real exam patterns, structure, and difficulty level to help you prepare effectively.
What is the best way to use this AWS Certified Data Engineer – Associate DEA-C01 test for preparation?
Take the test in a timed setting, review your answers carefully, and focus on improving weak areas after each attempt.
Can I retake this AWS Certified Data Engineer – Associate DEA-C01 practice test multiple times?
Yes, repeating the test helps reinforce concepts, improve accuracy, and build confidence for the actual exam.
Who should use this AWS Certified Data Engineer – Associate DEA-C01 practice test?
This practice test is suitable for both beginners and retakers who want to improve their understanding and performance.