Free DEA-C01 exam questions in PDF & AI Tutor

QUESTION: 25

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?

Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
Use Amazon Athena Federated Query to join the data from all data sources.
Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Answer(s): C

Explanation:

The correct answer is C.
A) An EMR cluster incurs provisioning and ongoing compute costs; for a one-time analysis, it is not the most cost-effective option compared to managed federated querying. B) Copying data into S3 adds ETL/storage costs and time, increasing total cost for a one-time analysis. C) Athena Federated Query enables on-demand, serverless access to multiple data sources (DynamoDB, RDS, Redshift, S3) with pay-per-query pricing, minimizing setup and cost for a one-off analysis. D) Redshift Spectrum can query S3 data and some external sources, but it does not natively federate across DynamoDB and RDS as seamlessly or cost-effectively as Athena Federated Queries for a one-time analysis.

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QUESTION: 26

A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

Use Hadoop Distributed File System (HDFS) as a persistent data store.
Use Amazon S3 as a persistent data store.
Use x86-based instances for core nodes and task nodes.
Use Graviton instances for core nodes and task nodes.
Use Spot Instances for all primary nodes.

Answer(s): B,D

Explanation:

A robust, cost-effective EMR setup uses S3 for persistent storage and Graviton-based core/task nodes for efficiency and price performance.
A) HDFS as persistent storage is discouraged for long-running, cost-optimized EMR workloads because S3 provides durable, scalable object storage with lower management overhead.
B) S3 as persistent data store is correct due to durability, lifecycle management, and lower maintenance for long-running Spark jobs.
C) x86-based instances for core/task is not as cost-efficient as Graviton2/3 for many EMR workloads.
D) Graviton instances offer better price/performance for Spark workloads on EMR, improving TCO.
E) Spot Instances for all primary nodes risks interruption and is unsuitable for continuous, high-reliability workloads.

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QUESTION: 27

A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

Answer(s): C

Explanation:

K) C is correct because Redshift’s external schema (Spectrum-style) allows Redshift to query streaming data proxied through Kinesis Data Streams with a materialized view that auto-refreshes, delivering near-real-time insights with minimal operational overhead. It avoids manual ETL and maintains low latency by directly exposing stream data to Redshift.
A) Incorrect: COPY from S3 introduces latency and batching; not truly real-time and adds unnecessary storage steps.
B) Incorrect: Materialized views on streams are not natively supported for real-time querying in Redshift; auto-refresh on streams is not standard behavior.
D) Incorrect: Firehose+S3/COPY adds extra staging and latency; not as low-overhead for live streaming analytics as an external schema with auto-refresh.

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QUESTION: 28

A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
Increase the AWS Glue instance size by scaling up the worker type.
Convert the AWS Glue schema to the DynamicFrame schema class.
Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
Modify the IAM role that grants access to AWS glue to grant access to all S3 features.

Answer(s): A,B

Explanation:

A) Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
B) Increase the AWS Glue instance size by scaling up the worker type.
C) Convert the AWS Glue schema to the DynamicFrame schema class.
D) Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
E) Modify the IAM role that grants access to AWS glue to grant access to all S3 features.
A) Correct: Partitioning reduces scan scope and speeds queries for large S3 datasets used by Glue ETL and downstream QuickSight. B) Correct: Larger worker type improves parallelism and throughput, reducing job runtimes. C) Incorrect: DynamicFrame vs DataFrame choice affects transformation API, not core performance for partitioned data. D) Incorrect: Fewer runs may delay updates; performance not improved. E) Incorrect: Overly broad IAM permissions do not enhance ETL performance.

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QUESTION: 29

A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

Parallel state
Choice state
Map state
Wait state

Answer(s): C

Explanation:

A Map state
A) Parallel state is for running multiple branches concurrently but does not automatically apply per-item transformation with dynamic collection; it is not designed to iterate over a collection of items.
B) Choice state selects between branches based on conditions, not for per-item processing across a collection.
C) Map state scales per-element processing by applying a defined workflow to each item in an input array, ideal for transforming every file in parallel.
D) Wait state introduces a delay and does not perform any per-item processing or parallel work.

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QUESTION: 30

A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Answer(s): B

Explanation:

A short summary: Using AWS Glue FindMatches ML transform provides deduplication with minimal ops.
A) Incorrect: Pandas drop_duplicates is in-memory and requires custom orchestration, not scalable with large S3 data; increases operational overhead.
B) Correct: AWS Glue FindMatches ML transform identifies duplicates with built-in, serverless deduplication; minimal maintenance and seamless integration with Glue ETL.
C) Incorrect: Python dedupe library requires custom code and management of similarity schemas and performance tuning; higher operational burden.
D) Incorrect: Importing Python dedupe in AWS Glue adds dependency management and custom logic, increasing complexity versus using managed FindMatches.

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QUESTION: 31

A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)

Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.
Use a columnar storage file format.
Partition the data based on the most common query predicates.
Split the data into files that are less than 10 KB.
Use file formats that are not splittable.

Answer(s): B,C

Explanation:

Using a columnar storage file format and partitioning the data by common predicates yields the fastest Redshift Spectrum queries.
A) Not correct: gzip compresses individual files but larger compressed sizes reduce parallelism and do not inherently guarantee faster scans; 1–5 GB per file is not optimal for Spectrum performance.
B) Correct: Columnar formats (e.g., ORC, Parquet) enable predicate pushdown and selective column reading, speeding scans.
C) Correct: Partitioning by common predicates reduces the data scanned and improves query performance via pruning.
D) Not correct: 10 KB files create excessive metadata operations and overhead, hurting performance.
E) Not correct: Non-splittable formats hinder parallelism and slow queries; splittable formats enable efficient parallel reads.

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QUESTION: 32

A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

Turn on the public access setting for the DB instance.
Update the security group of the DB instance to allow only Lambda function invocations on the database port.
Configure the Lambda function to run in the same subnet that the DB instance uses.
Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.
Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

Answer(s): C,D

Explanation:

C) Running the Lambda in the same VPC/subnet as the RDS instance ensures the function’s traffic stays within the private network, enabling private connectivity without internet exposure. D) Attaching the same security group to both Lambda and RDS with self-referencing rules allows intra-security-group communication on the database port, enabling authorized access without additional routing or public endpoints.
A) Turning on public access would expose the DB to the internet, contradicting “private” access. B) Security group on the DB to allow Lambda invocations is vague and not sufficient without correct networking; it also doesn’t guarantee same-subnet routing. E) Modifying NACLs adds unnecessary complexity and is not required when SG-based isolation suffices.

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What the DEA-C01 Exam Tests and How to Pass It

The AWS Certified Data Engineer - Associate DEA-C01 certification is designed for professionals who are responsible for building, maintaining, and managing data pipelines and data stores within the Amazon Web Services ecosystem. This certification validates a candidate's ability to implement data ingestion, transformation, and storage solutions that are secure, scalable, and cost-effective. Organizations hiring for roles such as Data Engineer, ETL Developer, or Cloud Data Architect look for this credential to ensure that potential employees possess the technical proficiency required to handle complex data workflows. By passing this certification exam, you demonstrate that you have the foundational knowledge to support data-driven decision-making processes using Amazon’s suite of data services. It is a critical benchmark for anyone looking to specialize in the cloud data engineering space, as it confirms your capability to operate within the specific constraints and best practices defined by Amazon.

The professional function of a certified AWS Data Engineer involves more than just writing code; it requires a deep understanding of how various AWS services interact to create a cohesive data architecture. Employers value this certification because it serves as a reliable indicator that a candidate understands the nuances of data lifecycle management, from the initial ingestion of raw data to the final delivery of insights. Whether you are working in a startup or a large enterprise, the skills validated by this Amazon certification are essential for maintaining the integrity and availability of data systems. As businesses increasingly rely on cloud-native data solutions, the demand for professionals who can navigate the complexities of AWS data services continues to grow. Achieving this certification provides a clear signal to recruiters and hiring managers that you are prepared to handle the responsibilities of a modern data engineering role.

What the DEA-C01 Exam Covers

The DEA-C01 exam is structured around four primary domains that reflect the day-to-day responsibilities of a data engineer working in the cloud. Candidates must demonstrate proficiency in Data Ingestion and Transformation, which involves moving data from various sources into AWS and processing it for analysis using services like AWS Glue, Amazon Kinesis, and AWS Lambda. Data Store Management is another critical area, requiring knowledge of how to select and configure the right storage solutions, such as Amazon S3, Amazon RDS, or Amazon Redshift, to meet specific performance and cost requirements. Data Operations and Support focuses on the ongoing maintenance of these systems, including monitoring, logging, and automating workflows to ensure reliability. Finally, Data Security and Governance ensures that candidates understand how to protect data at rest and in transit, manage access controls, and comply with organizational policies. Our practice questions are designed to cover these domains comprehensively, allowing you to test your knowledge across the entire spectrum of the exam syllabus.

Among these domains, Data Operations and Support often presents the most significant challenge for candidates because it requires a shift in mindset from building systems to maintaining them under real-world conditions. This area tests your ability to troubleshoot pipeline failures, optimize performance, and implement automated recovery processes using tools like Amazon CloudWatch and AWS EventBridge. To succeed here, you must understand not just how to set up a service, but how to monitor its health and respond to incidents effectively. Candidates need to demonstrate a deep understanding of operational excellence, which includes implementing CI/CD pipelines for data infrastructure and managing the lifecycle of data assets. This requires a level of practical experience that goes beyond theoretical knowledge, making it essential to use high-quality practice questions to simulate the troubleshooting scenarios you will encounter on the actual exam.

Are These Real DEA-C01 Exam Questions?

It is important to clarify that our practice questions are sourced and verified by the community, consisting of IT professionals and recent test-takers who have sat for the actual exam. These individuals contribute their insights to ensure that our questions reflect what appears on the real exam, providing a realistic assessment of your readiness. We prioritize a community-verified approach because it ensures that the content remains relevant and accurate as the exam evolves. If you have been searching for DEA-C01 exam dumps or braindump files, our community-verified practice questions offer something more valuable, each question is verified and explained by IT professionals who recently passed the exam. This method provides a reliable way to gauge your knowledge without relying on unauthorized or leaked materials that often contain incorrect information.

The community verification process is what makes our platform a trusted resource for your exam preparation. When a question is added or updated, it undergoes a rigorous review where users discuss the answer choices, flag potentially incorrect information, and share context from their own recent exam experiences. This collaborative environment allows you to see different perspectives on how to approach complex scenarios, which is far more beneficial than simply memorizing answers. By engaging with these discussions, you gain a deeper understanding of the underlying concepts, which is the most effective way to prepare for the certification exam. This transparency and collective intelligence ensure that you are studying with high-quality, reliable material that truly helps you prepare for the challenges of the DEA-C01.

How to Prepare for the DEA-C01 Exam

Effective exam preparation requires a combination of hands-on experience and a thorough understanding of AWS documentation. You should spend significant time in a sandbox environment, building and breaking data pipelines to see how different services interact in practice. Do not rely solely on reading; you must actively configure services like Amazon Glue, S3, and Redshift to understand their limitations and best practices. Every practice question on our platform includes a free AI Tutor explanation that breaks down the reasoning behind the correct answer, so you understand the concept, not just the answer. This AI Tutor is designed to help you connect the dots between the theory you read in whitepapers and the practical application required to solve scenario-based questions on the exam.

A common mistake candidates make is attempting to memorize the answers to practice questions rather than understanding the logic behind them. The DEA-C01 exam is heavily scenario-based, meaning you will be presented with complex business problems that require you to apply your knowledge to find the most efficient or secure solution. To avoid this trap, use our practice questions to identify your weak areas and then return to the official AWS documentation to reinforce those specific topics. Additionally, time management is a critical skill; use your study sessions to practice answering questions under timed conditions to ensure you can maintain your pace during the actual exam. By focusing on conceptual understanding and consistent practice, you will build the confidence needed to pass the certification exam on your first attempt.

What to Expect on Exam Day

On the day of your exam, you should be prepared for a rigorous assessment that tests your ability to make architectural and operational decisions in a cloud environment. The exam format typically consists of multiple-choice and multiple-response questions, often presented as complex scenarios that require you to select the best solution based on criteria like cost, performance, security, or operational overhead. These exams are administered in a controlled environment, either at a physical testing center or via an online proctoring service like Pearson VUE. You will have a set amount of time to complete the exam, and it is crucial to manage your time effectively, as some questions may be more time-consuming than others. Familiarizing yourself with the interface and the types of questions beforehand will help reduce anxiety and allow you to focus entirely on the technical content.

Amazon certification exams are designed to be challenging, and you should expect to encounter questions that require you to synthesize information from multiple AWS services. There is no single "trick" to passing; success comes from a comprehensive understanding of the AWS Well-Architected Framework and how it applies to data engineering. You will likely face questions that ask you to choose between two technically viable solutions, where the correct answer depends on a specific constraint mentioned in the scenario. Pay close attention to keywords in the question stem, such as "most cost-effective," "least operational overhead," or "highest availability," as these are the keys to selecting the right answer. By preparing with a focus on these nuances, you will be well-equipped to handle the pressure and complexity of the actual testing environment.

Who Should Use These DEA-C01 Practice Questions

These practice questions are intended for data engineers, ETL developers, and cloud architects who are actively pursuing the AWS Certified Data Engineer - Associate certification. If you have at least one to two years of experience working with AWS data services and are looking to formalize your expertise, this resource is designed for you. The goal of this exam preparation is to bridge the gap between your daily work experience and the specific requirements of the certification exam. Whether you are looking to advance your career, validate your skills for a new role, or simply deepen your understanding of AWS data architecture, these questions provide the structured practice you need. By using these materials, you are taking a proactive step toward achieving a recognized Amazon certification that carries significant weight in the IT industry.

To get the most out of these practice questions, do not simply treat them as a quiz to be completed quickly. Instead, treat each question as a learning opportunity; if you get a question wrong, read the AI Tutor explanation carefully and review the community discussions to understand why your initial reasoning was incorrect. Flag the questions that you find particularly difficult and revisit them periodically to ensure that you have truly mastered the concept. Engaging with the material in this way will help you retain information longer and apply it more effectively in your professional work. Browse the questions above and use the community discussions and AI Tutor to build real exam confidence as you work toward your certification goal.

Amazon DEA-C01 Exam Actual Questions AWS Certified Data Engineer - Associate DEA-C01 (Page 11 )

QUESTION: 25

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QUESTION: 26

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QUESTION: 27

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QUESTION: 28

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QUESTION: 29

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QUESTION: 30

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QUESTION: 31

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QUESTION: 32

Explanation:

What the DEA-C01 Exam Tests and How to Pass It

What the DEA-C01 Exam Covers

Are These Real DEA-C01 Exam Questions?

How to Prepare for the DEA-C01 Exam

What to Expect on Exam Day

Who Should Use These DEA-C01 Practice Questions

Amazon DEA-C01 Exam Actual Questions
AWS Certified Data Engineer - Associate DEA-C01 (Page 11 )