Amazon AWS Certified Data Engineer - Associate DEA-C01 Exam Dumps & Practice Test Questions

Question 1:

A data engineer is configuring an AWS Glue ETL job that runs within a VPC and needs to access data stored in a private Amazon S3 bucket. Although the IAM role and Glue connection appear to be set up correctly, the job fails to execute and returns an error referencing the S3 VPC gateway endpoint. The error suggests that the job cannot reach the S3 bucket, even though it's intended to access it without using the public internet.

What should the data engineer check and correct to allow the Glue job to connect to the S3 bucket via the VPC endpoint?

A. Modify the Glue job's security group to allow inbound access from the S3 VPC endpoint
B. Adjust the S3 bucket policy to allow access from the Glue job's IAM role
C. Validate that the Glue job connection includes the full domain name for S3
D. Ensure the VPC's route table has the correct route for the Amazon S3 VPC gateway endpoint

Correct Answer: D

Explanation:

When AWS Glue jobs are executed within a Virtual Private Cloud (VPC), they rely on the underlying network configuration to access external services like Amazon S3. To ensure secure access to S3 without routing traffic over the internet, AWS supports the use of VPC gateway endpoints for services like S3. These endpoints allow traffic to reach S3 directly from within the VPC.

However, simply creating the S3 gateway endpoint isn't enough. You must also update the route tables associated with the subnets where the Glue job runs. Specifically, there must be a route in the subnet’s route table that points all S3 traffic (identified using the S3 prefix list) to the endpoint. If this route is missing, the job won’t be able to connect to S3, even though IAM roles and permissions may be correctly set.

• Option A is incorrect because gateway endpoints do not require security group changes — they operate at the route table level, not via security groups.

• Option B is incorrect because this is a networking issue, not a permissions issue. While S3 bucket policies are important, they won’t resolve routing failures.
  
  

• Option C is irrelevant. Glue jobs interacting with S3 use internal AWS networking and DNS; they don’t require FQDNs for this use case.

Option D is the correct answer. The route table must direct S3 traffic to the correct gateway endpoint. Without this, S3 remains unreachable from the VPC, resulting in the Glue job's failure. Ensuring this configuration allows private, secure, and seamless S3 access without traversing the public internet.

Question 2:

A global retail company stores all customer records in a centralized Amazon S3 bucket. Analysts from different countries rely on this data for reporting. To meet data privacy and compliance regulations, the company needs to restrict each analyst’s access to only their country's data. For instance, an analyst in France should not see customer data from Spain or Italy.
Which is the most efficient and scalable way to implement this access control with minimal manual effort?

A. Create separate tables for each country's data and assign access accordingly
B. Use AWS Lake Formation to register the S3 bucket and configure row-level access policies
C. Replicate the data to AWS Regions close to each analyst and manage access locally
D. Load the data into Redshift and create country-specific views with matching IAM roles

Correct Answer: B

Explanation:

Managing secure, country-specific access to data stored in Amazon S3 can be challenging without the right tools. AWS Lake Formation offers a robust solution for this scenario, providing fine-grained access control, including row-level security, over data in S3 — all while minimizing operational overhead.

With Lake Formation, the centralized S3 bucket can be registered as a data lake location. The data engineer can then define data catalogs and tables that point to this dataset. Using Lake Formation permissions, row-level access policies can be configured so that each analyst only sees the records relevant to their country — for example, by filtering rows based on a "Country" column.

This centralized model avoids the need to maintain multiple datasets or S3 buckets for each region. Instead of managing a growing list of files, folders, or IAM policies, administrators can manage access through Lake Formation’s permission model. Analysts are granted access to a single table, and the security controls automatically limit their visibility based on their assigned region.

• Option A creates unnecessary complexity by requiring the creation and maintenance of multiple tables and increases the risk of inconsistency.

• Option C involves redundant data storage across regions and doesn’t address fine-grained, row-level controls.

• Option D introduces additional infrastructure (Redshift) and requires separate views and IAM configurations, adding administrative burden.

Option B is the best solution as it is scalable, secure, and efficient. Lake Formation’s built-in integration with AWS Glue, S3, and IAM enables a seamless experience for managing permissions while adhering to privacy regulations. It’s the ideal choice for enforcing country-specific data access at scale with minimal manual intervention.

Question 3:

A media organization is enhancing its personalized content recommendation system. To do this, it plans to supplement its existing analytics platform with valuable third-party datasets, which will allow it to better understand user preferences and behaviors. The team’s top priority is minimizing manual effort and operational complexity during the integration process. They are looking for a scalable and low-maintenance solution that aligns with their AWS infrastructure and supports easy access to external data sources.

Which solution offers the most efficient integration of third-party data with minimal operational overhead?

A. Use API calls to access and integrate third-party datasets from AWS Data Exchange
B. Use API calls to access and integrate third-party datasets from AWS DataSync
C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories
D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR)

Correct Answer: B

Explanation:

When aiming to enrich an existing analytics platform with third-party data while minimizing operational complexity, AWS Data Exchange provides the most streamlined and effective solution. It is a fully managed AWS service that simplifies the discovery, subscription, and usage of third-party datasets—all without requiring heavy manual integration efforts.

Through AWS Data Exchange, customers can subscribe to curated data products published by reputable providers. These data sets can be automatically delivered to Amazon S3, making them directly usable by services like AWS Glue, Amazon Athena, Amazon Redshift, and other analytics tools already in place. This seamless integration greatly reduces the time and resources required to prepare external data for analysis.

In contrast, Option B (AWS DataSync) is designed for transferring large volumes of data between on-premises systems and AWS—not for acquiring or integrating third-party datasets. It’s more of a data movement service than a marketplace integration tool.

Option C proposes accessing datasets through CodeCommit, which is a source control service, not a data repository. It has no relevance in the context of ingesting third-party datasets for analytical enrichment.

Option D similarly falls short: Amazon ECR is a container image registry and is unrelated to data hosting or analytics.

Therefore, Option A, using AWS Data Exchange, aligns perfectly with the need for automated data acquisition, reduced manual overhead, and compatibility with the AWS analytics ecosystem.

Question 4:

A financial services company is building a data mesh architecture to decentralize data ownership while maintaining centralized governance and fine-grained access control. As part of this effort, they want to support distributed data domains, enable secure access to datasets, and perform scalable ETL operations. They’ve chosen AWS Glue to manage ETL jobs and the data catalog.

Which two AWS services should they use alongside AWS Glue to successfully implement a secure and scalable data mesh?

A. Amazon Aurora for data storage and Amazon Redshift provisioned cluster for analysis
B. Amazon S3 for data storage and Amazon Athena for data analysis
C. AWS Glue DataBrew for centralized governance and access control
D. Amazon RDS for data storage and Amazon EMR for analysis
E. AWS Lake Formation for centralized governance and access control

Correct Answers: B and E

Explanation:

To implement a data mesh architecture on AWS, you must support decentralized data domain ownership, central governance, and secure, scalable access to datasets. The best combination of services to support this architecture includes Amazon S3, Amazon Athena, and AWS Lake Formation, working in tandem with AWS Glue.

Amazon S3 serves as a flexible, scalable storage layer for each domain in a data mesh. It supports storing data in open formats such as Parquet or CSV, which promotes data interoperability. Because each domain can manage its own S3 bucket(s), it enables decentralized ownership while ensuring compatibility with analytics tools.

Amazon Athena complements this by enabling serverless, interactive SQL querying directly on data in S3. It supports integration with the AWS Glue Data Catalog, making metadata consistent and queryable across domains without spinning up dedicated infrastructure.

For governance, AWS Lake Formation is essential. It allows the company to define fine-grained access controls (e.g., column-level, row-level permissions) and manage data access policies centrally—even though the data is owned and stored across various domains. It works with Glue, Athena, and Redshift Spectrum to enforce consistent access policies across the ecosystem.

Why the other options fall short:

• Option A (Aurora + Redshift): These are more centralized, managed databases not suited for the decentralized nature of a data mesh.

• Option C (Glue DataBrew): While useful for data transformation and preparation, it does not offer governance capabilities or act as a policy enforcement mechanism.

• Option D (RDS + EMR): Like Option A, this introduces centralized storage and complex infrastructure, which runs counter to the lightweight, distributed ethos of a data mesh.

Thus, Amazon S3 + Athena provide storage and analysis, while Lake Formation ensures strong, centralized governance—making B and E the ideal combination.

Question 5:

An AWS data engineer has written custom Python scripts for standardized data formatting that are reused across several AWS Lambda functions. Each time these scripts are updated, every Lambda function must be manually modified, which is inefficient and prone to mistakes. The engineer is searching for a scalable and low-maintenance solution that allows all Lambda functions to consistently access the most recent version of the shared scripts.

What is the best way to enable this functionality across all Lambda functions?

A. Store a reference to the scripts in an Amazon S3 bucket using the execution context.
B. Package the scripts as Lambda layers and attach them to each Lambda function.
C. Store a reference to the scripts in environment variables pointing to an S3 bucket.
D. Use a shared alias for each Lambda function and invoke them by the alias.

Correct Answer: B

Explanation:

The most efficient and AWS-native solution for reusing common code across multiple AWS Lambda functions is to use Lambda layers. Lambda layers provide a streamlined way to manage and distribute libraries, dependencies, and reusable code—such as the custom Python scripts mentioned in this scenario.

A Lambda layer is essentially a zip archive containing code or dependencies that can be shared across multiple functions. By packaging the Python formatting scripts into a Lambda layer, the data engineer only needs to update the code in one place. Once a new version of the layer is published, all Lambda functions that use it can be updated to reference the latest version with minimal effort.

This approach enhances reusability and maintainability, as the shared code resides outside the core Lambda function logic. It also promotes separation of concerns, where the functions focus on business logic and the reusable scripts are managed separately.

Other options fall short for the following reasons:

• A and C: Referencing external scripts stored in S3 introduces additional complexity, security risks, and longer cold start times. This method requires each Lambda function to fetch and execute code dynamically at runtime, which is not a recommended practice.

• D: Lambda aliases are used for version control within a single function, not for sharing common code between different functions. They do not facilitate shared script reuse.

In summary, Lambda layers are purpose-built for this exact scenario, offering an efficient, centralized, and secure way to manage shared Python scripts across multiple Lambda functions.

Question 6:

A company uses AWS Glue to run an ETL pipeline that extracts data from a Microsoft SQL Server database, transforms it, and loads the output into Amazon S3. The data engineer also needs to orchestrate this entire workflow—from the initial data crawl to the final data load. The solution should be AWS-native, cost-effective, and tightly integrated with AWS Glue.

Which AWS feature best supports this requirement?

A. AWS Step Functions
B. AWS Glue workflows
C. AWS Glue Studio
D. Amazon Managed Workflows for Apache Airflow (MWAA)

Correct Answer: B

Explanation:

The ideal solution for orchestrating multiple stages of an AWS Glue ETL pipeline is to use AWS Glue workflows. This feature is specifically designed to coordinate Glue-related components such as jobs, crawlers, and triggers, making it a natural fit for managing the full ETL lifecycle in a single, integrated process.

AWS Glue workflows enable users to build a visual pipeline where each step—such as data crawling, transformation, and loading—can be scheduled and triggered based on success, failure, or time-based events. This orchestration is tightly coupled with the AWS Glue environment, which means there's no need for additional integration layers or services.

One major advantage of Glue workflows is cost-effectiveness. Unlike AWS Step Functions or MWAA, Glue workflows do not incur extra charges for orchestration. Users pay only for the Glue jobs and crawlers executed during the workflow.

Alternative options explained:

• A. AWS Step Functions: While powerful, Step Functions are a more general-purpose orchestration tool and can introduce additional complexity and cost. For workflows limited to Glue jobs and crawlers, this would be an over-engineered solution.

• C. AWS Glue Studio: This is a graphical interface for creating and testing individual Glue jobs but does not support orchestration or multi-step coordination.
  
  

• D. Amazon MWAA: Based on Apache Airflow, MWAA is a robust orchestration tool suited for complex workflows involving multiple AWS services. However, it requires setup, management, and incurs higher operational costs—making it excessive for simple Glue-based pipelines.

In summary, AWS Glue workflows provide a purpose-built, low-cost, and AWS-native orchestration solution tailored for Glue-based ETL operations.

Question 7:

A financial services organization uses Amazon Redshift to store vast amounts of financial data for internal reporting and analytics. They are now developing a web-based trading platform that must fetch and display live data from Redshift in real time. The engineering team is tasked with identifying a secure and efficient way for the web application to access Redshift, but without managing database connections or infrastructure. Serverless or managed options are preferred to reduce maintenance.

Which approach should the engineering team choose to enable real-time data access from Redshift with minimal operational complexity?

A. Use WebSocket connections to connect to Amazon Redshift
B. Integrate with Amazon Redshift Data API
C. Connect using JDBC drivers for Redshift
D. Move data to Amazon S3 and query with Amazon S3 Select

Correct Answer: B

Explanation:

To support a real-time web application with minimal setup and maintenance, the Amazon Redshift Data API is the most appropriate choice. This API provides a serverless, HTTP-based way to run SQL queries on Amazon Redshift without needing to manage persistent database connections or install client libraries like JDBC or ODBC.

The Redshift Data API is especially useful for serverless applications or stateless workloads such as those running on AWS Lambda or in web browsers via API Gateway. The API securely handles authentication using AWS IAM, supports query submission and result retrieval, and integrates easily with AWS SDKs.

Key reasons for choosing this solution:

• Connectionless: Unlike JDBC, there’s no need to manage connection pooling or network access.

• IAM-based access: Avoids embedding credentials in the application code.

• Scalability and simplicity: Enables high-concurrency access without connection bottlenecks.

• Serverless-friendly: Ideal for Lambda, API Gateway, and browser-based interactions.

Let’s examine why the other options are less suitable:

• A (WebSocket): Amazon Redshift doesn’t support WebSocket connections; this method is invalid.

• C (JDBC): While JDBC can connect to Redshift, it introduces more complexity through driver management, connection pooling, and VPC configuration.

• D (S3 Select): This is used for querying objects in Amazon S3 and cannot be applied to Redshift tables.

Therefore, the Redshift Data API is the best serverless, low-overhead option for real-time access to Redshift data.

Question 8:

A company uses Amazon Athena to query data stored in Amazon S3 across various departments and applications. To comply with internal security policies, they need to isolate how different teams access Athena so that no team can view or interfere with the query results or history of others. The goal is to maintain separation and enforce fine-grained access control within a single AWS account.

What is the most effective way to enforce query-level separation and control visibility to historical results?

A. Use distinct S3 buckets for each use case and configure bucket policies
B. Create dedicated Athena workgroups for each use case, apply tags, and manage permissions with IAM
C. Assign separate IAM roles for each use case and connect via those roles
D. Apply AWS Glue Data Catalog resource policies to manage access at the table level

Correct Answer: B

Explanation:

The best method for isolating Athena usage across multiple teams is to use Athena Workgroups, each tailored to a specific use case, department, or project. Workgroups allow you to logically divide Athena’s query execution, control output locations, and manage access to query history independently.

By assigning resource tags (e.g., {"Environment": "Team1"}) to each workgroup, you can define IAM policies that grant or restrict access based on these tags. This ensures that users from one team can only access their specific workgroup, preventing cross-team visibility into saved queries, execution results, and query history.

Advantages of this approach:

• Separation of query logs and results by workgroup

• Workgroup-specific settings like output locations and encryption

• Tag-based IAM policies provide granular access control

• Usage tracking and cost control at the workgroup level

Why other choices are not sufficient:

• A (S3 buckets): Managing data access at the S3 level doesn’t control visibility into Athena query execution or history.

• C (IAM roles): While IAM roles help authenticate users, they don’t control query history or result separation without workgroup isolation.

• D (Glue policies): Glue policies restrict access to table metadata, not query execution or history.

In summary, Athena Workgroups combined with tag-based IAM policies provide the cleanest and most effective way to isolate and secure Athena usage across multiple internal teams.

Question 9:

You are designing a data pipeline using Amazon Kinesis Data Streams to collect and process real-time streaming data from IoT devices. The data must be stored in Amazon S3 for long-term analytics using Amazon Athena. 

What is the most efficient and scalable way to achieve this?

A. Use AWS Lambda to read from Kinesis and write records directly to Amazon S3.
B. Use Amazon Kinesis Data Firehose to deliver the data to Amazon S3.
C. Use AWS Glue to continuously crawl the Kinesis stream and write to S3.
D. Export the Kinesis data manually on a schedule and upload it to S3.

Correct Answer: B

Explanation:

The best solution for streaming data from Amazon Kinesis Data Streams to Amazon S3 for long-term storage and querying with Amazon Athena is to use Amazon Kinesis Data Firehose. This fully managed service is designed specifically for streaming data delivery, allowing you to ingest, buffer, transform, and load streaming data into S3, Redshift, OpenSearch, and other destinations with minimal effort.

Let’s evaluate each option:

• A: While AWS Lambda can be used to read from Kinesis and write to S3, it involves managing function concurrency, retry logic, error handling, and batching manually. This increases operational complexity and doesn't scale as efficiently as Firehose.

• B: Amazon Kinesis Data Firehose is the most efficient and scalable solution. It directly integrates with Kinesis Data Streams and automatically batches, compresses, encrypts, and delivers data to Amazon S3. You can also configure it to transform the data using AWS Lambda if needed, and it has built-in error handling and retry mechanisms.

• C: AWS Glue is better suited for ETL on batch data. It does not directly support continuous crawling or transformation of streaming data. While Glue streaming jobs exist, using them would still not be as streamlined as Firehose for this use case.

• D: Manually exporting data introduces latency, lacks scalability, and defeats the purpose of real-time or near real-time data processing.

Thus, B is the best answer for a scalable, reliable, and minimal-management solution to stream data from Kinesis to Amazon S3.

Question 10:

You have a data lake on Amazon S3 and need to provide business analysts access to query data using Amazon Athena. The analysts report that queries are slow and returning more data than necessary. 

How can you optimize Athena query performance while minimizing costs?

A. Partition the data in Amazon S3 and update the Glue Data Catalog accordingly.
B. Move the data from S3 to Amazon Redshift for faster querying.
C. Compress all data into a single large CSV file.
D. Increase the memory allocated to Athena queries via configuration.

Correct Answer:  A

Explanation:

When working with Amazon Athena to query data in Amazon S3, query performance and cost are directly impacted by the amount of data scanned. One of the most effective optimization techniques is data partitioning.

Here's the breakdown:

• A: Partitioning involves dividing the data into logical parts (e.g., by date, region, or category) and storing it in separate directories. When a query is run with filtering on the partitioned key (e.g., WHERE year = '2024'), Athena only scans relevant partitions instead of the entire dataset, dramatically improving performance and reducing query costs. You must also update the AWS Glue Data Catalog with these partitions to ensure Athena is aware of them.

• B: Amazon Redshift is optimized for complex queries but comes with higher costs and the overhead of managing a data warehouse. If the current setup is a data lake on S3 and Athena is sufficient in terms of SQL capabilities, moving to Redshift introduces unnecessary complexity and cost.

• C: Single large CSV files are inefficient for parallel processing. Athena performs better with columnar storage formats like Parquet or ORC, which allow it to read only the necessary columns. Large monolithic CSVs slow down performance and increase the amount of data scanned.

• D: Athena is a serverless query service and doesn’t allow users to manually configure memory allocation. Performance optimization comes from data layout, format, and query design, not compute resource tweaking.

So, A is the correct answer because it aligns with best practices for Athena performance tuning, ensuring efficient data access and cost savings.