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AWS Certified Data Engineer - Associate - Exam Simulator

DEA-C01

Prepare for your AWS Certified Data Engineer - Associate exam with our practice exam simulator. Featuring real exam scenarios, detailed explanations, and instant feedback to boost your confidence and success rate.

Questions update: Nov 13 2024

Questions count: 3312

Example questions

Domains: 4

Tasks: 17

Services: 70

Difficulty

The AWS Certified Data Engineer - Associate (DEA-C01) exam is recognized as a demanding certification that validates a candidate's expertise in managing and optimizing data-driven workflows within the AWS ecosystem. While this exam is geared towards professionals with a background in data engineering, it is by no means an easy feat and requires a thorough understanding of both foundational and advanced AWS data services.

 

The DEA-C01 exam covers a broad range of topics, including but not limited to, data ingestion, transformation, storage, and visualization within AWS. Candidates must demonstrate a solid grasp of essential AWS data services such as Amazon Redshift, Glue, S3, and Kinesis, along with a deep understanding of how to architect and maintain scalable, secure, and high-performing data pipelines.

 

A critical component of the exam focuses on data processing frameworks like Apache Spark and Hadoop, as well as an understanding of database services, both relational (RDS, Aurora) and non-relational (DynamoDB). Additionally, the exam tests the candidate's ability to apply machine learning workflows using services like Amazon SageMaker within a data engineering context.

Security and compliance are integral to the exam, emphasizing the need for candidates to understand AWS's shared responsibility model and implement best practices for data governance, encryption, and access management. Services such as IAM, KMS, and AWS Lake Formation are frequently tested to ensure candidates can securely manage data at scale.

 

In this exam, candidates are also expected to distinguish between various data storage and retrieval solutions, optimizing them based on specific use cases and cost considerations. This includes knowing when to utilize services like S3 for unstructured data versus using Redshift for structured data analytics.

 

Overall, the questions in the DEA-C01 exam are designed to challenge both theoretical knowledge and practical application, with scenarios that require candidates to design, implement, and optimize complex data architectures. While the exam avoids overly complicated wording, the difficulty lies in the depth of knowledge requi

How AWS Exam Simulator works

The Simulator generates on-demand unique practice exam question sets fully compatible with the selected AWS Official Certificate Exam.

The exam structure, difficulty requirements, domains, and tasks are all included.

Rich features not only provide you with the same environment as your real online exam but also help you learn and pass AWS Certified Data Engineer - Associate - DEA-C01 with ease, without lengthy courses and video lectures.

See all features - refer to the detailed description of AWS Exam Simulator description.

Exam Mode Practice Mode
Questions count651 - 75
Limited exam timeYesAn option
Time limit130 minutes10 - 200 minutes
Exam scope4 domains with appropriate questions ratio Specify domains with appropriate questions ratio
Correct answersAfter exam submissionAfter exam submission or after question answer
Questions typesMix of single and multiple correct answersSingle, Multiple or Both
Question tipNeverAn option
Reveal question domainAfter exam submissionAfter exam submission or during the exam
Scoring15 from 65 questions do not count towards the resultOfficial AWS Method or mathematical mean

Exam Scope

The Practice Exam Simulator questions sets are fully compatible with the official exam scope and covers all concepts, services, domains and tasks specified in the official exam guide.

AWS Certified Data Engineer - Associate - DEA-C01 - official exam guide

For the AWS Certified Data Engineer - Associate - DEA-C01 exam, the questions are categorized into one of 4 domains: Data Ingestion and Transformation, Data Store Management, Data Operations and Support, Data Security and Governance, which are further divided into 17 tasks.

AWS structures the questions in this way to help learners better understand exam requirements and focus more effectively on domains and tasks they find challenging.

This approach aids in learning and validating preparedness before the actual exam. With the Simulator, you can customize the exam scope by concentrating on specific domains.

Exam Domains and Tasks - example questions

Explore the domains and tasks of AWS Certified Data Engineer - Associate - DEA-C01 exam, along with example questions set.

Question

Task 1.1 Perform data ingestion

You are working as a Data Engineer at a healthcare company that captures real-time patient data from numerous medical devices. The data from these devices are streamed continuously to an Amazon Kinesis Data Stream. To ensure low latency and durability, you decide to distribute the data to multiple downstream services for immediate processing. Several machine learning models and storage solutions need to consume this data simultaneously with different processing workloads. Due to the varying requirements, you aim to achieve a fan-out pattern. Furthermore, alerts and notifications need to be sent to healthcare professionals based on specific thresholds. You decide to use Amazon SNS for this purpose. What is the most appropriate way to set up the system to achieve this?

select single answer

Explanation

This solution allows multiple downstream consumers to subscribe to the SNS topic, enabling a fan-out pattern. Each consumer can handle the data independently, meeting different processing workloads.

Explanation

While AWS Lambda can process data from Kinesis, it doesn't inherently provide a fan-out mechanism to distribute data to various other services.

Explanation

While this approach handles data storage and transformation, it introduces unnecessary latency and doesn't directly use SNS for immediate data distribution and notifications.

Explanation

Amazon Kinesis Data Firehose is used for loading data streams into data lakes and other stores but doesn't support complex fan-out mechanisms directly.

Question

Task 1.2 Transform and process data

As an AWS Certified Data Engineer, you have been tasked with creating a data API to make customer data available to other systems in real-time. Your company uses Amazon RDS for PostgreSQL to store customer data. You need to ensure that the data transformation and processing are efficient and that other systems can consume the data with minimal latency. How can you best achieve this within the AWS ecosystem?

select single answer

Explanation

Amazon Kinesis Data Firehose is typically used for streaming data to other AWS services such as S3 or Redshift, and not for exposing APIs for real-time data access from RDS.

Explanation

AWS Lambda can be triggered to access, process, and transform data from Amazon RDS. Amazon API Gateway can then be used to expose this transformed data as APIs, ensuring efficient data access with minimal latency.

Explanation

While AWS Glue is excellent for ETL jobs, it is primarily designed for batch processing, not for real-time data access. Writing the results back to RDS for real-time API access would not be efficient.

Explanation

Directly accessing RDS from multiple other systems would increase load and latency on the database, potentially resulting in performance issues. It also lacks a layer for data transformation and security.

Question

Task 1.3 Orchestrate data pipelines

You are a Data Engineer at a company that processes large volumes of data. As part of your data pipeline, you have a process that ingests data from various sources and transforms it before storing it into your data warehouse. You are using Amazon SQS to manage the ingestion of data by queuing messages that represent the data files to be processed. Due to the need for real-time processing, you must ensure that if any part of the system fails or encounters an issue, the appropriate team is immediately alerted so they can address the problem. Which service would you use to send these notifications, and how would you integrate it with Amazon SQS?

select single answer

Explanation

Amazon CloudWatch Alarms are used to monitor AWS metrics and can trigger actions based on those metrics. While CloudWatch can monitor SQS metrics, it requires integration with SNS to send notifications. CloudWatch Alarms alone do not send notifications on their own.

Explanation

Amazon Simple Notification Service (SNS) is designed to send notifications. By integrating SNS with SQS, you can set up automations to send notifications to the appropriate team whenever a specific event occurs in your data pipeline (e.g., a message is added to a queue).

Explanation

Amazon Kinesis is designed for real-time data processing and analytics, not for sending notifications. Though it can ingest and process data streams, it does not provide the native notification sending functionality needed for alerting teams.

Explanation

AWS Lambda can be triggered by events in SQS and can execute code to process those events. However, Lambda is a compute service and does not natively send notifications. To send notifications, you would still need to integrate Lambda with Amazon SNS.

Question

Task 1.4 Apply programming concepts

You are a data engineer working for a media company that processes a large volume of user-uploaded video files. Your task is to create an AWS Lambda function that processes these video files as soon as they are uploaded to an Amazon S3 bucket. You need to ensure that the Lambda function has access to a temporary storage volume for buffering data during processing. Which of the following steps should you take to achieve this?

select single answer

Explanation

AWS Lambda does not support directly attaching EBS volumes to functions. Lambda functions can only use the /tmp directory for temporary storage.

Explanation

While mounting an EFS file system to a Lambda function is possible, it is typically used for shared storage rather than temporary storage. For buffering purposes, the /tmp directory is more appropriate.

Explanation

While Object Lambda access points can simplify accessing S3 objects, this does not address the need for temporary storage during processing. This option deals with storing or accessing data differently, not with temporary buffering.

Explanation

Lambda functions have access to a /tmp directory which can be used as temporary storage. By specifying the amount of /tmp storage and using the AWS SDK to read files from S3, you can ensure the Lambda function has the necessary temporary storage.

Question

Task 2.1 Choose a data store

You are a data engineer for a financial analytics firm, responsible for managing and analyzing large datasets. The firm’s transactional data is stored in an Amazon Aurora MySQL database. The analytical workload has increased significantly, and you need to speed up your data queries. You decide to store a subset of this data in Amazon Redshift for faster querying and reporting. What is the most efficient method to move and keep this data in sync while ensuring minimal impact on the Amazon Aurora database performance?

select single answer

Explanation

Materialized views improve query performance but require periodic manual refreshes, which may not capture real-time changes efficiently and could introduce additional overhead.

Explanation

Amazon Redshift Spectrum is useful for querying large datasets in S3, but exporting Aurora data to S3 could introduce latency and complicate the setup process.

Explanation

Although AWS Data Pipeline can move data between services, it requires setting up complex ETL jobs and might not be the most efficient method for real-time or frequent data synchronization.

Explanation

Amazon Redshift federated queries allow you to query data across operational and analytic databases without moving data, thereby maintaining performance and ensuring up-to-date data.

Question

Task 2.2 Understand data cataloging systems

You are an AWS Certified Data Engineer working for a retail company. Your task is to catalog data stored in Amazon S3 so that it can be queried and analyzed easily. You choose to use AWS Glue for this task. You want to create a new connection in AWS Glue to your Amazon S3 bucket named 'retail-sales-data'. What are the necessary steps you should follow to successfully create this connection and catalog the data? A. Create an AWS Glue Data Catalog, then configure a Crawler to scan the Amazon S3 bucket and store the metadata in the Data Catalog. B. Upload the data to Amazon S3, create an Amazon RDS database, and configure the AWS Glue Crawler to use the RDS instance. C. Use AWS Glue’s ETL (Extract, Transform, Load) feature to transform the data in Amazon S3 and move it to Amazon Redshift. D. Download the AWS Glue data plugin and configure it to connect to the Amazon S3 bucket directly from your local machine.

select single answer

Explanation

While AWS Glue ETL can transform data and move it to Amazon Redshift, the question asks about cataloging the data, not moving or transforming it.

Explanation

AWS Glue is managed through the AWS Management Console, not through downloading a plugin for local use.

Explanation

This option involves creating an Amazon RDS database, which is not necessary for cataloging data inside an Amazon S3 bucket using AWS Glue.

Explanation

AWS Glue Data Catalog is a central repository to store structural and operational metadata for all your data assets. Configuring a Crawler will automatically scan your Amazon S3 bucket and store metadata in the Data Catalog.

Question

Task 2.3 Manage the lifecycle of data

Alex, a Data Engineer at a financial services company, has been tasked with optimizing the storage cost management of their AWS environment. The company uses Amazon S3 for storing large volumes of transaction logs and DynamoDB for storing metadata about these transactions. In order to comply with regulatory requirements, the transaction logs must be kept for a minimum period, but after that, they can be moved to cheaper storage or deleted. Alex implements S3 versioning to protect against accidental deletions or overwrites and uses DynamoDB Time to Live (TTL) to automatically delete entries after their retention period expires. To further optimize storage costs, Alex wants to automate the process of moving older S3 objects to cheaper storage classes and eventually delete them. What should Alex do to optimize the lifecycle of the data in S3?

select single answer

Explanation

While S3 Intelligent-Tiering can automatically move objects between frequent and infrequent access tiers, it does not support transitions to Glacier or automatic deletions, which might be needed for cost optimization in the long term.

Explanation

Amazon S3 Object Lifecycle Policies help in automating the transition of objects to cheaper storage classes like Infrequent Access, Glacier, or Glacier Deep Archive, and can also delete them after a specified period, optimizing the storage cost.

Explanation

S3 versioning helps in protecting against accidental deletions, but it does not provide automated transitions or deletions based on object age or other criteria.

Explanation

Manually moving objects is error-prone and not a scalable solution for a large number of objects. It also requires continuous monitoring and manual intervention.

Question

Task 2.4 Design data models and schema evolution

As a data engineer, you've been tasked with setting up a data pipeline to process and transform large sets of data from various sources. The data needs to be analyzed and used to train machine learning models in Amazon SageMaker. To design your data models and manage schema evolution effectively, you must establish data lineage throughout the pipeline to ensure data quality and traceability. Which AWS service should you use to automate the extraction, transformation, and loading (ETL) of data while also providing built-in capabilities for tracking data lineage?

select single answer

Explanation

AWS Glue is a managed ETL service that automatically discovers and profiles your data, generates the code to transform it, and tracks the data lineage, making it well-suited for establishing data lineage in data pipelines.

Explanation

Amazon Kinesis is designed for real-time data processing but does not provide built-in ETL capabilities or data lineage tracking.

Explanation

Amazon Redshift is a data warehousing service that stores and queries large datasets efficiently but does not provide ETL capabilities or data lineage tracking by itself.

Explanation

AWS Data Pipeline is used for data-driven workflows and orchestration but does not offer built-in functionality for ETL processes and detailed data lineage tracking like AWS Glue.

Question

Task 3.1 Automate data processing by using AWS services

You are a data engineer at a company that collects large volumes of data from IoT devices. The data is streamed into an S3 bucket in near real-time. Your task is to automate the processing of this data using AWS services. The processing needs to be triggered at the arrival of new files in the S3 bucket, and you have decided to use AWS Batch for the processing jobs. Which combination of AWS services can be used to automate this data processing workflow?

select single answer

Explanation

Amazon EventBridge can capture S3 events and invoke an AWS Batch job to process the new data, effectively automating the data processing workflow.

Explanation

AWS Step Functions can orchestrate complex workflows, but it does not inherently detect new files in S3. EventBridge is more suited for event-based triggering on S3 file arrivals.

Explanation

Amazon SQS can be used for queuing messages, but it is not intended for directly detecting new files in an S3 bucket. Additionally, AWS Lambda may not be suitable for long-running batch processing jobs involving large datasets.

Explanation

Amazon CloudWatch Events (now part of EventBridge) can monitor S3, but AWS Glue is better suited for ETL processes rather than batch processing jobs where AWS Batch is more appropriate.

Question

Task 3.2 Analyze data by using AWS services

You are a data engineer at a retail company, and you are conducting an analysis of customer purchase data stored in Amazon S3. You decide to use AWS Glue to clean and catalogue the data and then explore it using Amazon Athena notebooks powered by Apache Spark. You want to aggregate customer purchase amounts per region and create visual insights on customer spending trends. Which of the following steps should you follow to achieve your goal efficiently?

select single answer

Explanation

Amazon DynamoDB and CloudWatch are not suitable for this type of analytical task. The scenario requires using AWS Glue and Athena notebooks with Spark, not DynamoDB or Lambda.

Explanation

QuickSight is used for data visualization, but it bypasses the steps involving AWS Glue for cataloging and the specified use of Athena notebooks with Apache Spark for analysis, which is a key part of the scenario.

Explanation

While AWS Glue can process data and you can visualize using Redshift, the scenario specifies using Athena notebooks with Spark for analysis. Redshift would not be the tool for this requirement.

Explanation

AWS Glue provides the necessary tools for transforming and cataloguing the data, and Athena notebooks with Spark offer the ability to write SQL queries and perform interactive data analysis with visualization capabilities.

Question

Task 3.3 Maintain and monitor data pipelines

You are a data engineer at a company that processes massive amounts of log data generated by various microservices. The log data is stored in Amazon OpenSearch Service for real-time analysis and monitoring. Recently, you have observed that the OpenSearch cluster is running slower because it is overwhelmed with the growing number of logs. You need a solution to maintain and monitor your data pipeline effectively ensuring better performance of the OpenSearch cluster. What should you do?

select single answer

Explanation

Index State Management (ISM) allows you to define policies to automate the management of indices' lifecycle, which helps to keep the OpenSearch cluster performant by controlling the size and age of indices.

Explanation

Amazon Athena is not designed to query data stored in OpenSearch Service directly. Athena is optimized for querying data stored in Amazon S3.

Explanation

Adding more nodes will temporarily alleviate the issue, but it does not address the root cause, which is the need for better index management to handle log data effectively.

Explanation

While this might work, it is not a scalable or efficient solution. Manual solutions are prone to human error and do not provide the automation needed for a large-scale operation.

Question

Task 3.4 Ensure data quality

During a routine audit in your data processing pipeline, you discovered inconsistencies in the data stored in your Amazon S3 buckets. You suspect that the data transformations performed by AWS Glue DataBrew might be the root cause of the inconsistencies. As a Data Engineer, you need to investigate and ensure data quality in your pipeline. Which of the following approaches would be the most effective to identify and rectify the data inconsistencies?

select single answer

Explanation

AWS CloudTrail logs S3 API calls and can help identify unauthorized or unexpected access patterns, but it doesn't provide the capability to profile or validate the actual data content for inconsistencies.

Explanation

Amazon S3 Inventory provides a flat-file list of the objects in an S3 bucket, but it does not provide insights into the data quality or consistency within the objects themselves.

Explanation

While enabling versioning can help track changes and restore previous versions of data objects, it does not directly address or identify inconsistencies within the data itself.

Explanation

AWS Glue DataBrew allows you to create profiling jobs to analyze data and understand its structure, patterns, and anomalies. Additionally, it supports data quality rules that can be applied to detect and rectify inconsistencies.

Question

Task 4.1 Apply authentication mechanisms

You are a data engineer at a financial services company and are responsible for setting up a secure data pipeline. You've been asked to grant access to a specific Amazon S3 bucket only to your analytics team so they can run queries for the quarterly financial reports. The team members should only have read access to the bucket. You already have an IAM group named 'AnalyticsTeam'. To ensure security, you decide to use AWS PrivateLink to keep the data transfer within the AWS network. What is the best way to achieve this?

select single answer

Explanation

An admin IAM policy grants too many permissions, violating the principle of least privilege. AWS Direct Connect is not necessary for keeping data within the AWS network; AWS PrivateLink would be more appropriate here.

Explanation

While it follows the least privilege principle, it is less efficient than attaching the policy to the group. AWS Direct Connect is not the right service for keeping data within the AWS network; AWS PrivateLink should be used.

Explanation

Simply adding users to the group without attaching any policies does not grant any permissions. Additional IAM policies are necessary to manage access rights.

Explanation

This solution uses IAM policies to define the necessary read permissions and employs AWS PrivateLink to secure the data transfer by keeping it within the AWS network.

Question

Task 4.2 Apply authorization mechanisms

You are a data engineer at a company that uses Amazon Redshift, Amazon EMR, and Amazon S3 for big data analytics. To enhance your data security and governance, you are considering using AWS Lake Formation for managing data access permissions. A specific requirement is to ensure that the data engineers can run jobs on Amazon EMR clusters and have restricted access to only specific datasets stored in Amazon S3. How should you configure Lake Formation to meet this requirement?

select single answer

Explanation

While AWS Glue Data Catalog helps with schema management, it does not provide sufficient permission management. Lake Formation is specifically designed for fine-grained access control.

Explanation

Bucket policies are less fine-grained compared to Lake Formation permissions and don't integrate well with EMRFS. This approach fails to leverage the full set of permission controls available through Lake Formation.

Explanation

Lake Formation permissions provide a fine-grained access control to datasets in Amazon S3, and EMRFS can leverage these permissions to access data securely on an EMR cluster.

Explanation

IAM policies alone do not provide the fine-grained access control that Lake Formation offers. This approach does not leverage Lake Formation capabilities for managing data access permissions.

Question

Task 4.3 Ensure data encryption and masking

Acme Corp is migrating its data pipeline to AWS and wants to ensure that all data is encrypted in transit to comply with regulatory requirements. They have a stringent security policy that mandates the use of IAM policies to enforce encryption settings on all data transfers. As the lead Data Engineer, you need to configure IAM policies to ensure all S3 buckets can only be accessed via HTTPS. Which of the following IAM policy statements would best meet these requirements?

select single answer

Explanation

This policy allows S3 actions without secure transport, which fails to meet the requirement of encryption in transit. Allowing with a `false` condition compromises security.

Explanation

This policy correctly denies any S3 action that does not use secure transport (HTTPS), thereby enforcing encryption in transit.

Explanation

This policy inadvertently denies any S3 action that uses secure transport, which is the opposite of the goal. The `true` condition should not trigger a deny.

Explanation

While this policy denies all actions without secure transport, it's overly broad and can unintentionally affect other AWS services beyond the S3 bucket. Specificity to S3 resources is required.

Question

Task 4.4 Prepare logs for audit

John is a data engineer responsible for managing log data generated by an Amazon EMR cluster running various data processing workloads. The cluster generates a high volume of log data that needs to be securely stored and retrieved for audit purposes. John also needs to ensure that only specific team members have access to these logs and that all access is properly logged for further auditing. He has decided to use Amazon S3 as the storage solution for the logs and needs to make sure that the security policies are set correctly using AWS Identity and Access Management (IAM). Which combination of IAM policies and AWS services should John implement to achieve the above requirements?

select single answer

Explanation

This is insecure because storing logs on an EC2 instance with open access violates security best practices. It fails to control access strictly and is less reliable for long-term storage.

Explanation

This approach ensures that logs are securely stored with encryption, access is restricted to specific individuals, and all access attempts are logged for auditing purposes.

Explanation

Not using encryption for sensitive log data is a security vulnerability. Granting public read-only access could lead to data breaches. While CloudWatch can monitor metrics, it does not log access activities for audit.

Explanation

Amazon RDS is not optimized for storing and querying high volumes of log data efficiently. Additionally, it would require more complex maintenance and doesn't directly integrate IAM policies for fine-grained access control and audit logging as seamlessly as S3 with CloudTrail.

Question

Task 4.5 Understand data privacy and governance

You have recently joined a company as a data engineer. The company has a strict policy for data governance and data privacy. As part of the policy, all configuration changes within the AWS account need to be monitored and recorded meticulously. The company uses AWS Config for this purpose. Recently, several configuration changes were made to various AWS resources, and you need to verify if these changes comply with the company's policies. Which service or feature can you use to view the chronological order of configuration changes along with their details?

select single answer

Explanation

AWS CloudTrail logs API calls and user activities but is not specifically designed for tracking configuration changes in detail. It primarily focuses on audit trails for API activity.

Explanation

Amazon CloudWatch is used for monitoring and logging performance metrics, but it does not provide a detailed chronological view of configuration changes.

Explanation

AWS Trusted Advisor provides optimization recommendations for AWS resources, but it is not meant for viewing the historical configuration changes in chronological order.

Explanation

AWS Config Timeline allows you to view the historical changes and configuration details of supported AWS resources in chronological order, which is essential for verifying compliance with data governance policies.

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