Free AWS Certified Data Engineer - Associate DEA-C01 Exam Questions and Answers
AWS Certified Data Engineer - Associate DEA-C01 is exam DEA-C01, part of AWS Certification from Amazon Web Services. AWS codes take the form ROLE-Cnn, where the letters name the role and tier and the C-number is the syllabus revision — so SAA-C03 is the third revision of Solutions Architect Associate. Exams are multiple choice and multiple response through Pearson VUE, scored on a 100 to 1000 scale, with the pass mark set by tier: 700 Foundational, 720 Associate, 750 Professional and Specialty.
Candidates comparing DEA-C01 exam dumps, ExamTopics and other DEA-C01 practice tests use this page for the answers and explanations behind each question. Download the free DEA-C01 PDF, then sit the timed DEA-C01 exam simulation before booking with Amazon.
Last updated: September 30, 2026
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Question #1
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. Which solution will meet this requirement MOST cost-effectively?
Correct answer: C
Explanation
Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries. Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source. The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not supportquerying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2. References: • Amazon Athena Federated Query • Redshift Spectrum vs Federated Query
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Question #2
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisionedcapacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL. Which solution will meet these requirements with the LEAST operational overhead?
Correct answer: A
Explanation
The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format. AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog. Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries. Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3. Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena. Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data. Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or RedshiftSpectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References: • What is Amazon Athena? • Data Catalog and crawlers in AWS Glue • AWS Glue Data Catalog • Columnar Storage Formats • AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide • AWS Glue Schema Registry • What is AWS Glue? • Amazon Redshift Serverless • Amazon Redshift provisioned clusters • [Querying external data using Amazon Redshift Spectrum] • [Using stored procedures in Amazon Redshift] • [What is AWS Lambda?] • [PartiQL for Amazon Athena] • [Federated queries in Amazon Athena] • [Amazon Athena pricing] • [Top 10 performance tuning tips for Amazon Athena] • [AWS Glue ETL jobs] • [AWS Lake Formation jobs]
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Question #3
A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns. The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs. Which solution will meet these requirements with the LEAST operational overhead?
Correct answer: D
Explanation
S3 Intelligent-Tiering is a storage class that automatically moves objects between four access tiers based on the changing access patterns. The default access tier consists of two tiers: Frequent Access and Infrequent Access. Objects in the Frequent Access tier have the same performance and availability as S3 Standard, while objects in the Infrequent Access tier have the same performance and availability as S3 Standard-IA. S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost-optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency. References: • S3 Intelligent-Tiering • S3 Storage Lens • S3 Lifecycle policies • [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
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Question #4
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models. The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio. Which change should the engineer make to gain access to SageMaker Studio?
Correct answer: B
Explanation
This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer’s IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References: • Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions • Troubleshoot Errors - Amazon SageMaker • AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess
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Question #5
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints. The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size. Which solution will meet these requirements?
Correct answer: C
Explanation
This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables. However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently orextensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. References: • Choose the best distribution style • Distribution styles • Working with data distribution styles
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Question #6
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. Which solution will capture the changed data MOST cost-effectively?
Correct answer: C
Explanation
An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data. The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it alsocharges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. References: • What is a data lake? • Choosing a data format for your data lake • Using the MERGE INTO command in Delta Lake • [AWS Lambda quotas] • [AWS Database Migration Service quotas]
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Question #7
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline. Which AWS service or feature will meet these requirements MOST cost-effectively?
Correct answer: B
Explanation
AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure. AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines. AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing. Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References: • AWS Glue Workflows • AWS Step Functions • AWS Glue Studio • Amazon MWAA • AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
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Question #8
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format: Which solution will meet this requirement with the LEAST coding effort?
Correct answer: B
Explanation
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not thesame as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References: • 7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns) • NEST TO MAP - AWS Glue DataBrew (Section: Syntax)
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Question #9
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.)
Select 2 answers.
Correct answer: A, D
Explanation
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. References: • AWS Glue triggers • AWS Glue connections • AWS Glue DataBrew • [AWS Lambda functions] • [Redshift Data API]
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Question #10
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time. The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds. Which solution will meet these requirements with the LEAST operational overhead?
Correct answer: C
Explanation
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze streaming data in real time. You can use Kinesis Data Streams to capture sensor data from various sources, such as IoT devices, web applications, or mobile apps. You can create data streams that can scale up to handle any amount of data from thousands of producers. You can also use the Kinesis Client Library (KCL) or the Kinesis Data Streams API to write applications that process and analyze the data in the streams1. Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use DynamoDB to store the sensor data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can use the DynamoDB API or the AWS SDKs to perform queries on the data, such as using key-value lookups, scans, or queries2. The solution that meets the requirements with the least operational overhead is to use Amazon Kinesis Data Streams to capture the sensor data and store the data in Amazon DynamoDB for querying. This solution has the following advantages: • It does not require you to provision, manage, or scale any servers, clusters, or queues, as Kinesis Data Streams and DynamoDB are fully managed services that handle all the infrastructure for you. This reduces the operational complexity and cost of running your solution. • It allows you to ingest sensor data in near real time, as Kinesis Data Streams can capture data records as they are produced and deliver them to your applications within seconds. You can also use Kinesis Data Firehose to load the data from the streams to DynamoDB automatically and continuously3. • It allows you to store the data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB Streams to capturechanges in the data and trigger actions, such as sending notifications or updating other databases. • It allows you to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can also use DynamoDB Accelerator (DAX) to improve the read performance by caching frequently accessed data. Option A is incorrect because it suggests using a self-hosted Apache Kafka cluster to capture the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages: • It requires you to provision, manage, and scale your own Kafka cluster, either on EC2 instances or on-premises servers. This increases the operational complexity and cost of running your solution. • It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency. Option B is incorrect because it suggests using AWS Lambda to process the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages: • It does not allow you to ingest sensor data in near real time, as Lambda is a serverless compute service that runs code in response to events. You need to use another service, such as API Gateway or Kinesis Data Streams, to trigger Lambda functions with sensor data, which may add extra latency and complexity to your solution. • It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency. Option D is incorrect because it suggests using Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data and use AWS Glue to store the data in Amazon RDS for querying. This solution has the following disadvantages: • It does not allow you to ingest sensor data in near real time, as Amazon SQS is a message queue service that delivers messages in a best-effort manner. You need to use another service, such as Lambda or EC2, to poll the messages from the queue and process them, which may add extra latency and complexity to your solution. • It does not allow you to store the data in nested JSON format, as Amazon RDS is a relational database service that supports structured data types, such as tables and columns. You need to use another service, such as AWS Glue, to transform the data from JSON to relational format, which may add extra cost and overhead to your solution. References: • 1: Amazon Kinesis Data Streams - Features • 2: Amazon DynamoDB - Features • 3: Loading Streaming Data into Amazon DynamoDB - Amazon Kinesis Data Firehose • [4]: Capturing Table Activity with DynamoDB Streams - Amazon DynamoDB • [5]: Amazon DynamoDB Accelerator (DAX) - Features • [6]: Amazon S3 - Features • [7]: AWS Lambda - Features • [8]: Amazon Simple Queue Service - Features • [9]: Amazon Relational Database Service - Features • [10]: Working with JSON in Amazon RDS - Amazon Relational Database Service • [11]: AWS Glue - Features
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Discussion
Explain your reasoning, not just the letterOther Amazon certifications
- AWS Certified Solutions Architect - Associate SAA-C03 (opens in a new tab)
- AWS Certified Cloud Practitioner CLF-C02 (opens in a new tab)
- AWS Certified Developer - Associate DVA-C02 (opens in a new tab)
- SCS-C03: AWS Certified Security - Specialty (opens in a new tab)
- SOA-C03: AWS Certified CloudOps Engineer - Associate (opens in a new tab)
- AWS Certified Solutions Architect - Professional SAP-C02: AWS Certified Solutions Architect -Professional SAP-C02 (opens in a new tab)
- AWS Certified DevOps Engineer - Professional DOP-C02: AWS Certified DevOps Engineer -Professional DOP-C02 (opens in a new tab)
- AWS Certified Database - Specialty (opens in a new tab)
- AWS Certified Advanced Networking - Specialty ANS-C01 (opens in a new tab)
- AWS Certified Machine Learning - Specialty (MLS-C01) (opens in a new tab)
- AWS Certified Data Analytics - Specialty (DAS-C01) (opens in a new tab)
- ANS-C00: AWS Certified Advanced Networking - Specialty (opens in a new tab)
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FAQ
Learn More: https://aws.amazon.com/certification/
- Q1: What is the AWS Certified Data Engineer - Associate DEA-C01 exam?
- A: AWS Certified Data Engineer - Associate DEA-C01 is a Amazon certification exam. Judging by the questions in our bank, it concentrates on amazon, engineer, redshift, athena and bucket.
- Q2: What topics does the AWS Certified Data Engineer - Associate DEA-C01 exam cover?
- A: Questions in our AWS Certified Data Engineer - Associate DEA-C01 bank cluster around amazon, engineer, redshift, athena, bucket, overhead, apache and glue. Working through the full set is the quickest way to find which of these you are weakest on.
- Q3: How should I prepare for AWS Certified Data Engineer - Associate DEA-C01?
- A: Work through the AWS Certified Data Engineer - Associate DEA-C01 practice questions here, checking your answer on each one, then sit the practice test to rehearse the exam under timed conditions before the real thing.
- Q4: Are these real AWS Certified Data Engineer - Associate DEA-C01 exam questions?
- A: They are drawn from officially released past questions and from community members who have sat AWS Certified Data Engineer - Associate DEA-C01. Answers are verified and updated weekly.
- Q5: Where do I register for the AWS Certified Data Engineer - Associate DEA-C01 exam?
- A: Register through Amazon directly at https://aws.amazon.com/certification/. Exampractice is not affiliated with Amazon and does not administer the exam.
- Q6: Is there a free AWS Certified Data Engineer - Associate DEA-C01 sample?
- A: Yes. Every AWS Certified Data Engineer - Associate DEA-C01 page shows a free sample of real questions. Upgrading opens the full bank and the practice test.
- Q7: What are Amazon Certification Exams?
- A: Amazon Certification Exams validate your expertise in Amazon Web Services (AWS), covering a range of cloud computing skills, including architecture, development, operations, and data analytics. These certifications demonstrate your proficiency in designing, deploying, and managing applications on the AWS platform.
- Q8: Why should I pursue Amazon Certification?
- A: Amazon Certification enhances your professional credibility, showcasing your skills and knowledge in AWS services. This can lead to better job opportunities, higher salaries, and career advancement in the cloud computing and IT industry.
- Q9: What are the benefits of Amazon Certification?
- A: Benefits include recognition as a certified cloud professional, improved job performance, access to exclusive resources, continuing education opportunities, and staying current with the latest AWS technologies and best practices.
- Q10: Who should take Amazon Certification Exams?
- A: IT professionals, cloud architects, developers, system administrators, data analysts, and anyone involved in designing, implementing, and managing cloud solutions on AWS should consider these certifications to validate their expertise and advance their careers.
- Q11: What types of Amazon Certification Exams are available?
- A: Amazon offers various certification paths, including Foundational Level (AWS Certified Cloud Practitioner), Associate Level (AWS Certified Solutions Architect, AWS Certified Developer, AWS Certified SysOps Administrator), Professional Level (AWS Certified Solutions Architect – Professional, AWS Certified DevOps Engineer – Professional), and Specialty Certifications (Security, Big Data, Advanced Networking, and more).
- Q12: How do I prepare for Amazon Certification Exams?
- A: Preparation can include official AWS training courses, study guides, practice exams, online tutorials, and hands-on experience with AWS services and solutions.
- Q13: Where can I take Amazon Certification Exams?
- A: Amazon Certification Exams can be taken online or at authorized testing centers worldwide, providing flexibility to fit your schedule and location.
- Q14: How do Amazon Certifications impact my career?
- A: Amazon Certifications significantly boost your career by demonstrating your expertise to employers, making you a more competitive candidate for advanced roles and promotions in the cloud computing and IT industry.
- Q15: Are there any prerequisites for Amazon Certification Exams?
- A: Some exams may have prerequisites, such as foundational knowledge or prior certifications. Check the specific requirements for each certification path on the AWS Certification website.
- Q16: How often do I need to recertify for Amazon Certifications?
- A: AWS Certifications typically require recertification every three years to ensure that certified professionals stay updated with the latest AWS technologies and industry practices.