Amazon DEA C01 Exam Updated Practice Questions in May 2026 for Data Engineer Associate certification.

Amazon DEA-C01 Exam Practice Questions This Amazon DEA-C01 exam PDF provides detailed practice questions, answers, and explanations. These Amazon Amazon DEA-C01 exam practice questions are designed for IT professiona ...

Amazon DEA-C01 Exam Practice Questions This Amazon DEA-C01 exam PDF provides detailed practice questions, answers, and explanations. These Amazon Amazon DEA-C01 exam practice questions are designed for IT professionals, system administrators, and students preparing for Data Engineer Associate certification. Key Features Exam-Oriented Questions: Realistic practice questions that mirror the format and difficulty of actual certification exams. Wide Coverage: Includes cloud computing, networking, security, AI, and enterprise IT management exams. Study-Friendly Format: Organized sections by exam type, enabling focused preparation. Important Note: This material is for personal study purposes only. Please do not redistribute or use for commercial purposes without permission. Share some Amazon DEA-C01 exam online questions below. 1.A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The data stream runs in Account A. The company wants to use an AWS Lambda function in Account B to process the data from the stream. The company creates a Lambda execution role in Account B that has permissions to access data from the stream in Account A. What additional step must the company take to meet this requirement? A. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account A. B. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role. C. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B. D. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function. Answer: B Explanation: To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions. SCPs (A & C) set permissions boundaries, but do not grant access. Option D incorrectly refers to the Lambda function C but the Kinesis resource must allow access. “You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream.” Reference: AWS Documentation C Cross-account Lambda access to Kinesis 2.A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. Which solution will meet these requirements with the LEAST operational overhead? A. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframeWrite a SQL SELECT statement on the dataframe to query the required column. B. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects. C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column. D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column. Answer: B Explanation: Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions. S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources. Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration. Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process. Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics. Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration. Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration. AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide S3 Select and Glacier Select - Amazon Simple Storage Service AWS Lambda - FAQs What Is AWS Glue DataBrew? - AWS Glue DataBrew Populating the AWS Glue Data Catalog - AWS Glue What is Amazon Athena? - Amazon Athena 3.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? A. AWS Step Functions B. AWS Glue workflows C. AWS Glue Studio D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) 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. Reference: AWS Glue Workflows AWS Step Functions AWS Glue Studio Amazon MWAA AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide 4.A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway. Which solution will meet these requirements with the LEAST operational overhead? A. Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster. B. Create an AWS Lambda Python function with provisioned concurrency. C. Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS). D. Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. Answer: B Explanation: AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1. Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2. Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3. The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages: It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code. It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website. It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function. Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster. This solution has the following disadvantages: It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code. It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process. It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture