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Amazon Amazon-DEA-C01 Dumps - Pass AWS Certified Data Engineer - Associate Exam in 2026
The Amazon-DEA-C01 exam is the AWS Certified Data Engineer - Associate certification exam from Amazon Web Services. It is designed for professionals who work with data pipelines, analytics workloads, and cloud-based data solutions. This certification validates your ability to build, manage, secure, and support data workflows on AWS. It is a valuable credential for data engineers, analytics engineers, and cloud professionals who want to prove practical AWS data engineering skills.
| # | Exam Topics | Sub-Topics | Approximate Weightage (%) |
|---|---|---|---|
| 1 | Data Ingestion and Transformation | Batch and streaming ingestion, ETL and ELT workflows, data transformation logic, pipeline orchestration | 30% |
| 2 | Data Store Management | Data lake and warehouse concepts, storage selection, partitioning and optimization, lifecycle management | 25% |
| 3 | Data Operations and Support | Monitoring and troubleshooting, job reliability, logging and alerting, operational best practices | 25% |
| 4 | Data Security and Governance | Access control, encryption, data classification, governance and compliance controls | 20% |
The exam tests whether candidates can apply AWS data engineering concepts in realistic scenarios, not just remember definitions. You need a practical understanding of ingestion, storage, operations, and governance across AWS services and workflows. Strong problem-solving skills, attention to data quality, and the ability to choose the right design for each use case are important for success.
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QA4Exam.com provides Exam PDF material with actual questions and answers, plus an Online Practice Test for the Amazon Amazon-DEA-C01 exam. The content is designed to help you study with realistic exam simulation, so you can get comfortable with the format and question style before test day. The questions are updated to reflect current exam needs, and the verified answers help you review concepts with more confidence. The practice test also helps you improve time management, identify weak areas, and build the speed needed to pass on your first attempt. With both formats, you can prepare in a focused and efficient way.
Frequently Asked Questions
Who should take the AWS Certified Data Engineer - Associate exam?
This exam is for data engineers, analytics engineers, and cloud professionals who work with AWS data solutions and want to validate practical skills in data ingestion, storage, operations, and governance.
Is the Amazon-DEA-C01 exam difficult?
It can be challenging because it focuses on applied knowledge and scenario-based questions. Candidates who understand AWS data workflows and practice with exam-style questions are usually better prepared.
Can I pass with only braindumps?
Braindumps alone are not the best approach. You should combine practice questions with real understanding of the exam topics so you can handle scenario-based questions and make the right choices under pressure.
Do I need hands-on experience to pass?
Hands-on experience is very helpful because the exam tests practical AWS data engineering skills. Even if you study from dumps and a practice test, real experience makes it easier to understand the scenarios in the exam.
Are the QA4Exam.com dumps enough or do I need other resources?
The Exam PDF and Online Practice Test from QA4Exam.com are strong preparation tools, but combining them with topic review and hands-on practice gives you a better chance of passing on the first attempt.
How do the QA4Exam.com practice test and PDF help with first-attempt success?
They help you understand the question pattern, review verified answers, practice under time limits, and identify weak areas before the real exam. This makes your preparation more focused and improves confidence on exam day.
Can I retake the exam if I do not pass?
Retake policies are set by the exam provider, so you should check the latest Amazon Web Services exam rules before scheduling or rescheduling another attempt.
The questions for Amazon-DEA-C01 were last updated on Jul 20, 2026.
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A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day's data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company.Reference:
Using streaming ingestion with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)
The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs.Reference:
AWS Glue triggers
AWS Glue connections
AWS Glue DataBrew
[AWS Lambda functions]
[Redshift Data API]
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based on the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
For query workloads on S3 data that depend on date-based filters, partitioning by order date optimizes performance and cost because Athena reads only the relevant partitions.
Athena scales automatically and doesn't degrade with increasing data size when partitions are managed efficiently.
''Partitioning data in Amazon S3 based on query predicates such as order date improves Athena query performance and reduces scanned data volume.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
This is the most cost-effective and scalable option for date-based queries.
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data. The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D) would not work, as the data on the instance store volume would be lost if the instance is terminated.Reference:
Amazon EC2 Instance Store
Amazon EBS Volumes
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2
An ecommerce company collects daily customer transaction logs in CSV format and stores the logs in Amazon S3. The company uses Amazon Athena to scan a subset of attributes from the logs on the same day the company receives each log.
Query times are increasing because of increasing transaction volume. The company wants to improve query performance.
Which solution will meet these requirements with the SHORTEST query times?
Amazon Athena achieves the fastest query performance when data is stored in columnar formats such as Apache Parquet and when queries can take advantage of partition pruning and predicate pushdown.
Converting CSV files to Parquet significantly reduces the amount of data scanned because Parquet stores data in a column-oriented layout. Since Athena queries only a subset of attributes, it reads only the required columns instead of scanning entire rows, which dramatically improves performance. Predicate pushdown further reduces query time by filtering data at the storage layer.
Partitioning the data by date ensures that Athena scans only the relevant partitions for same-day queries, minimizing unnecessary data reads. Storing one Parquet file per day is efficient and avoids the overhead of managing excessive small files.
ORC is also a columnar format, but Parquet is more commonly optimized and recommended for Athena workloads in AWS exam guidance. JSON and Avro are row-based or semi-row-based formats and result in larger scan sizes and slower query execution.
Therefore, Option D provides the shortest query times and aligns with Athena performance best practices.
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