Amazon Web ServicesAssociate levelDEA-C01

DEA-C01 exam dumps: 549 free AWS Data Engineer Associate practice questions

Free DEA-C01 practice questions for the AWS Certified Data Engineer - Associate exam, with the correct answer and a full explanation for every option. Read the first 10 below, browse all 549 by number, or take a timed practice exam.

Question bank last updated December 2024

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Free DEA-C01 practice questions

Questions 1 to 10 of 549

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

DEA-C01 Question 1

Single answer

Your company is building a real-time analytics platform to process IoT sensor data from thousands of devices. The data needs to be ingested into AWS for further processing and storage. The solution must handle high throughput, ensure low-latency ingestion, and allow for near real-time processing. Which of the following options would best meet these requirements?

A
Use Amazon Kinesis Data Streams to ingest the data and process it in real time.
B
Use AWS Glue to directly ingest and process the data from IoT devices.
C
Use Amazon S3 for real-time ingestion and processing of the data.
D
Use Amazon RDS to directly ingest the IoT data for real-time analytics.

Show answer and explanation

Correct answer: A

Explanation

For real-time ingestion and processing of high-throughput, low-latency data from IoT devices, Amazon Kinesis Data Streams is the ideal choice. It is designed to handle large-scale data ingestion and provides seamless integration with downstream analytics tools for near real-time processing. AWS Glue, Amazon S3, and Amazon RDS are not suitable for this scenario due to their lack of real-time ingestion capabilities or their primary focus on other use cases.

A. Correct.
Amazon Kinesis Data Streams is purpose-built for high-throughput, low-latency ingestion and real-time processing of data streams, making it the best option for this use case.
B. Incorrect.
AWS Glue is primarily used for ETL (Extract, Transform, Load) workflows and is not designed for real-time ingestion of high-throughput IoT data.
C. Incorrect.
Amazon S3 is a storage service and does not natively support real-time ingestion or processing of data streams.
D. Incorrect.
Amazon RDS is a relational database service and is not optimized for ingesting high-throughput, real-time IoT data streams.

DEA-C01 Question 2

Single answer

You are working as a data engineer at a company that collects streaming data from IoT devices and ingests it into AWS for downstream analytics. The data needs to be ingested in near real-time and stored in a scalable and durable format for further processing. Which AWS service or combination of services would you choose to perform this data ingestion effectively?

A
Amazon Kinesis Data Streams
B
AWS Glue
C
Amazon S3 Batch Operations
D
Amazon Redshift

Show answer and explanation

Correct answer: A

Explanation

Amazon Kinesis Data Streams is the best choice for near real-time ingestion of streaming data, such as IoT device data, as it is designed to handle real-time data streams with high scalability and durability. Other services like AWS Glue, S3 Batch Operations, and Amazon Redshift serve different purposes and are not suitable for this use case.

A. Correct.
Amazon Kinesis Data Streams is purpose-built for ingesting and processing real-time streaming data, making it the correct choice for near real-time data ingestion from IoT devices.
B. Incorrect.
AWS Glue is primarily used for ETL (Extract, Transform, Load) operations and metadata management. While it is useful for data transformation, it is not ideal for real-time data ingestion.
C. Incorrect.
Amazon S3 Batch Operations is designed for large-scale operations on existing S3 objects, such as copying or tagging, and is not suitable for real-time or near real-time data ingestion.
D. Incorrect.
Amazon Redshift is a data warehouse service optimized for analytical queries but is not intended for real-time data ingestion.

DEA-C01 Question 3

Single answer

A company wants to ingest large volumes of real-time IoT sensor data into AWS for downstream analytics. The incoming data is highly variable in structure and needs to be processed with minimal latency. Which AWS service or combination of services is the most suitable for this use case?

A
Amazon Kinesis Data Streams
B
AWS Glue
C
Amazon S3
D
Amazon Redshift

Show answer and explanation

Correct answer: A

Explanation

For real-time ingestion of large volumes of data, Amazon Kinesis Data Streams is the best choice as it is designed to handle streaming data with low latency. Other services like AWS Glue, Amazon S3, and Amazon Redshift serve complementary purposes but are not suitable for the real-time ingestion requirement specified in the scenario.

A. Correct.
Amazon Kinesis Data Streams is specifically designed for ingesting and processing real-time data with minimal latency, making it ideal for this scenario.
B. Incorrect.
AWS Glue is primarily used for ETL (Extract, Transform, Load) operations and schema generation but is not suitable for real-time data ingestion.
C. Incorrect.
Amazon S3 is a storage service and does not provide real-time ingestion capabilities. It is better suited for storing data after it has been ingested.
D. Incorrect.
Amazon Redshift is a data warehouse and is designed for querying structured data at scale, not for real-time data ingestion.

DEA-C01 Question 4

Single answer

You are building a data ingestion pipeline for a financial analytics application. The pipeline needs to ingest real-time stock market data from an external API into Amazon S3 for further processing. The data needs to be processed with minimal latency. Which solution should you implement to achieve this?

A
Use Amazon Kinesis Data Firehose to directly ingest and deliver the data into an S3 bucket.
B
Use AWS Glue to extract the data from the external API and save it to Amazon S3.
C
Use Amazon Simple Queue Service (SQS) to buffer the data and then write a Lambda function to process and store it in S3.
D
Use an EC2 instance to periodically call the API, process the data locally, and upload it to Amazon S3.

Show answer and explanation

Correct answer: A

Explanation

Amazon Kinesis Data Firehose is the best solution for ingesting real-time data into Amazon S3 with minimal latency. It is a fully managed service designed for real-time data streaming and delivery, making it ideal for use cases like real-time stock market data ingestion. Other options like AWS Glue and SQS are more suited for batch processing or message queuing, and using an EC2 instance adds unnecessary complexity and latency.

A. Correct.
Correct. Amazon Kinesis Data Firehose is designed for real-time data ingestion and can directly deliver data to S3 with low latency.
B. Incorrect.
Incorrect. AWS Glue is primarily used for ETL (Extract, Transform, Load) operations and not ideal for real-time ingestion from an external API.
C. Incorrect.
Incorrect. While SQS can be used for buffering, it introduces additional overhead and latency, making it less suitable for real-time ingestion.
D. Incorrect.
Incorrect. Using an EC2 instance for real-time ingestion is less efficient and introduces higher latency compared to managed services like Kinesis Data Firehose.

DEA-C01 Question 5

Select 3

You are a Data Engineer tasked with building a data ingestion pipeline for a client that collects real-time clickstream data from their website. The client requires the data to be processed with minimal latency and stored in a data lake for future analytics. Which combination of AWS services would you use to build this pipeline?

A
Amazon Kinesis Data Streams for real-time data ingestion
B
AWS Glue for real-time data ingestion
C
Amazon S3 as the data lake storage
D
Amazon Redshift for data lake storage
E
Amazon Kinesis Data Firehose for delivering data to the data lake

Show answer and explanation

Correct answers: A, C, E

Explanation

To meet the client's requirements of real-time ingestion and storing data in a data lake, Amazon Kinesis Data Streams handles the real-time clickstream data ingestion, Amazon Kinesis Data Firehose delivers the data to Amazon S3, and Amazon S3 serves as the data lake. This combination ensures minimal latency and scalable data storage for future analytics.

A. Correct.
Amazon Kinesis Data Streams is designed for real-time data ingestion, making it suitable for handling clickstream data with minimal latency.
B. Incorrect.
AWS Glue is primarily used for ETL (Extract, Transform, Load) tasks and not optimized for real-time data ingestion, so it is not suitable for this use case.
C. Correct.
Amazon S3 is a scalable and cost-effective storage option for a data lake, meeting the client's requirements.
D. Incorrect.
Amazon Redshift is a data warehouse solution and is not typically used as a data lake. It is optimized for analytics rather than scalable storage for raw data.
E. Correct.
Amazon Kinesis Data Firehose can be used to deliver streaming data into Amazon S3, ensuring seamless integration with the data lake.

DEA-C01 Question 6

Select 2

You are designing a data pipeline using AWS services to process real-time streaming data from IoT devices. The pipeline must ensure exactly-once processing and be able to handle sudden spikes in data volume. Which combination of AWS services should you use to meet these requirements?

A
Amazon Kinesis Data Streams for ingestion and Amazon Kinesis Data Analytics for processing
B
Amazon Managed Streaming for Apache Kafka (Amazon MSK) for ingestion and Apache Flink for processing
C
Amazon SQS for ingestion and AWS Lambda for processing
D
Amazon Kinesis Data Streams for ingestion and AWS Glue for processing
E
Amazon EventBridge for ingestion and AWS Step Functions for processing

Show answer and explanation

Correct answers: A, B

Explanation

To process real-time streaming data with exactly-once semantics and handle sudden spikes in data volume, you need a combination of services optimized for streaming ingestion and processing. Both Amazon Kinesis Data Streams with Kinesis Data Analytics and Amazon MSK with Apache Flink meet these requirements. Amazon SQS, AWS Glue, and Amazon EventBridge are not suitable for real-time streaming or ensuring exactly-once processing in this context.

A. Correct.
Amazon Kinesis Data Streams provides a durable and scalable solution for ingesting real-time streaming data. Amazon Kinesis Data Analytics enables real-time processing and can ensure exactly-once processing if properly configured.
B. Correct.
Amazon MSK is a managed service for Apache Kafka, which is well-suited for real-time streaming ingestion and can handle high data volumes. Apache Flink is designed for stateful stream processing and supports exactly-once semantics.
C. Incorrect.
Amazon SQS is typically used for queue-based decoupling, but it is not optimized for real-time streaming ingestion or exactly-once semantics in a streaming context.
D. Incorrect.
While Amazon Kinesis Data Streams is a good choice for ingestion, AWS Glue is primarily designed for batch ETL processes, not real-time streaming processing.
E. Incorrect.
Amazon EventBridge is designed for event-driven architectures, not high-throughput real-time streaming. AWS Step Functions can orchestrate workflows but is not suited for real-time stream processing.

DEA-C01 Question 7

Select 3

Your company operates a large-scale data processing pipeline using Amazon S3, AWS Glue, and Amazon Redshift. The team has recently noticed performance issues when querying data in Redshift. Upon investigation, you find that the tables in Redshift are not optimized for the queries being run. What steps should you take to improve query performance while minimizing costs?

A
Analyze the query patterns and apply appropriate distribution styles to the tables
B
Enable Redshift Spectrum to offload all queries to Amazon S3
C
Compress the data in Amazon Redshift tables using columnar encoding
D
Use AWS Glue to crawl the data and reformat it into a smaller file size before loading into Redshift
E
Regularly run the VACUUM and ANALYZE commands on the Redshift tables

Show answer and explanation

Correct answers: A, C, E

Explanation

To improve Amazon Redshift query performance, you should focus on optimizing table layouts and ensuring the database is maintained properly. Analyzing query patterns to determine optimal distribution styles minimizes data movement during queries, significantly improving performance. Similarly, compressing data with columnar encoding reduces I/O overhead. Running VACUUM and ANALYZE commands ensures that the database's internal statistics are up-to-date and that storage is efficiently utilized. These actions together help enhance query execution and minimize costs associated with processing inefficiencies.

A. Correct.
Analyzing query patterns and applying appropriate distribution styles (e.g., key, even, or all) ensures that data is distributed optimally across nodes, reducing data movement and improving query performance.
B. Incorrect.
While Redshift Spectrum can query data directly in Amazon S3, it is not a solution to improve the performance of queries executed within Redshift itself.
C. Correct.
Compressing data using columnar encoding reduces storage requirements and improves query performance by reducing the amount of data read during query execution.
D. Incorrect.
Using AWS Glue to reformat files before loading into Redshift is not directly related to optimizing performance for queries within Redshift. It is more relevant to ETL processes.
E. Correct.
Running VACUUM and ANALYZE commands helps maintain table health by reclaiming storage and updating query planning statistics, which directly impacts query performance.

DEA-C01 Question 8

Select 2

You are designing a data pipeline to process streaming data from IoT devices. The data needs to be ingested in real-time, stored durably, and then processed for analytics. The processed results should be queried with low latency. Which combination of AWS services meets these requirements?

A
Amazon Kinesis Data Streams for ingestion, Amazon S3 for storage, and Amazon Athena for querying
B
Amazon Kinesis Data Streams for ingestion, Amazon DynamoDB for storage, and Amazon Elasticsearch Service (Amazon OpenSearch Service) for querying
C
Amazon Managed Streaming for Apache Kafka (MSK) for ingestion, Amazon S3 for storage, and Amazon Redshift for querying
D
Amazon Kinesis Data Streams for ingestion, Amazon S3 for storage, and Amazon Redshift for querying
E
Amazon Kinesis Data Streams for ingestion, Amazon DynamoDB for storage, and Amazon QuickSight for querying

Show answer and explanation

Correct answers: B, E

Explanation

To meet the requirements of real-time ingestion, durable storage, and low-latency querying, a combination of Amazon Kinesis Data Streams for ingestion, DynamoDB for storage, and either Amazon OpenSearch Service or Amazon QuickSight for querying is ideal. These services are designed to handle real-time data, ensure durability, and provide efficient querying capabilities.

A. Incorrect.
Amazon Athena is suitable for querying data stored in Amazon S3, but it does not support low-latency analytics for real-time or near real-time queries. This option is not suitable for the requirements.
B. Correct.
Amazon Kinesis Data Streams handles real-time ingestion, DynamoDB provides durable storage with low-latency access, and Amazon OpenSearch Service enables efficient querying of processed data. This option meets the requirements.
C. Incorrect.
Amazon MSK and S3 are valid for ingestion and storage, but Redshift is not ideal for low-latency querying in this scenario as it is optimized for large-scale analytical processing, not real-time analytics.
D. Incorrect.
This combination supports ingestion, durable storage, and analytics, but using Amazon Redshift for querying is not ideal for low-latency requirements. Redshift is better suited for batch analytics.
E. Correct.
Amazon Kinesis Data Streams handles real-time ingestion, DynamoDB provides low-latency durable storage, and Amazon QuickSight can be used for visualizing low-latency query results. This option meets the requirements.

DEA-C01 Question 9

Select 2

A company wants to build a data pipeline that ingests real-time streaming data from IoT devices, processes it for anomaly detection, and stores the processed data for analytics. The data should be processed with minimal latency, and the solution should be scalable and cost-effective. Which combination of AWS services should you use to implement this solution?

A
Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, and Amazon S3 for storage
B
Amazon SQS for ingestion, Amazon EC2 for processing, and Amazon RDS for storage
C
Amazon Kinesis Data Firehose for ingestion, AWS Glue for processing, and Amazon Redshift for storage
D
Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, and Amazon DynamoDB for storage

Show answer and explanation

Correct answers: A, D

Explanation

The correct combination of services depends on the requirements for real-time ingestion, minimal latency, scalability, and cost-effectiveness. Amazon Kinesis Data Streams is ideal for real-time ingestion, AWS Lambda allows serverless and low-latency processing, and both Amazon S3 and DynamoDB can be used for storage depending on the use case. S3 is more suited for cost-effective analytics storage, while DynamoDB is better for real-time queries and NoSQL use cases.

A. Correct.
Correct: Amazon Kinesis Data Streams is a scalable service designed for real-time data ingestion. AWS Lambda provides serverless processing with minimal latency, and Amazon S3 is cost-effective for storing large amounts of processed data.
B. Incorrect.
Incorrect: Amazon SQS is not ideal for real-time streaming data ingestion as it is designed for message queuing. Amazon EC2 requires managing instances, which increases operational overhead, and Amazon RDS is not optimized for handling large-scale real-time analytics.
C. Incorrect.
Incorrect: Amazon Kinesis Data Firehose is designed for near real-time, not real-time, streaming, and it is commonly used for delivering data to storage solutions rather than processing. AWS Glue is better suited for batch processing rather than real-time processing.
D. Correct.
Correct: Amazon Kinesis Data Streams supports real-time ingestion, AWS Lambda enables serverless, low-latency processing, and Amazon DynamoDB is a scalable NoSQL database suitable for storing processed real-time data.

DEA-C01 Question 10

Select 3

You are designing a data pipeline for a retail company that needs to process clickstream data in near real-time. The processed data will be used for customer behavior analysis and stored in a data warehouse for further querying. Which AWS services should you use to build this pipeline efficiently and cost-effectively?

A
Amazon Kinesis Data Streams for real-time data ingestion
B
AWS Glue for real-time data transformation
C
Amazon Redshift for storing the processed data
D
Amazon S3 for storing raw and processed data
E
Amazon EMR for running Apache Spark jobs in real-time

Show answer and explanation

Correct answers: A, C, D

Explanation

To process clickstream data in near real-time, Amazon Kinesis Data Streams can be used for data ingestion. Processed data can be stored in Amazon Redshift to enable efficient querying for customer behavior analysis. Amazon S3 is ideal for storing both raw and processed data due to its scalability and cost-effectiveness. AWS Glue and Amazon EMR are not optimal for this pipeline as they are more suited for batch processing rather than real-time processing.

A. Correct.
Amazon Kinesis Data Streams is designed for real-time data ingestion, making it suitable for processing clickstream data in near real-time.
B. Incorrect.
AWS Glue is primarily used for ETL processes and does not support true real-time transformations. It is designed for batch transformations.
C. Correct.
Amazon Redshift is a data warehouse service optimized for analytical queries, making it ideal for storing processed data for customer behavior analysis.
D. Correct.
Amazon S3 is a cost-effective option for storing raw clickstream data and processed data, enabling a durable and scalable storage layer.
E. Incorrect.
Amazon EMR is better suited for large-scale batch processing and is not the most efficient option for real-time data processing.

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What the DEA-C01 exam covers

Official AWS Certified Data Engineer - Associate exam domains and weightings.

Data Ingestion and Transformation
34% of exam
Data Store Management
26% of exam
Data Operations and Support
22% of exam
Data Security and Governance
18% of exam

DEA-C01 practice questions 1 to 100 of 549

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 6 pages of up to 100 questions.

DEA-C01 exam dumps FAQ

Are these DEA-C01 dumps real exam questions?

No. These are original practice questions written to the AWS Certified Data Engineer - Associate exam objectives, not questions copied from a live exam. Memorising leaked questions violates Amazon Web Services's candidate agreement and stops working the moment the question pool rotates. Use this bank to check your understanding of each domain and to find the topics you still need to study.

How many DEA-C01 practice questions are there?

549 questions, each with the correct answer, an explanation of the answer, and a note on why every other option is wrong. The first 10 are on this page and every question has its own page linked below.

Are the DEA-C01 exam dumps free?

Yes. Every question, answer and explanation on this page and the linked question pages is free to read without an account. A free HydraNode account adds timed practice exams, scoring and progress tracking across attempts.

How do I take a timed DEA-C01 practice test?

Sign in and start the AWS Certified Data Engineer - Associate exam on HydraNode. A session gives you 65 questions drawn from this bank in 130 minutes, then a score report with a per-question review.

What topics does the DEA-C01 exam cover?

The official exam domains are: Data Ingestion and Transformation; Data Store Management; Data Operations and Support; Data Security and Governance.

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