Here you have the best Amazon AWS Certified Data Engineer - Associate DEA-C01 practice exam questions
A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint.
The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.
Which solution will meet this requirement?
When dealing with VPC gateway endpoints for Amazon S3, it is crucial to verify that the VPC's route table includes the necessary routes for the gateway endpoint. This ensures that traffic intended for the S3 bucket is properly routed through the VPC endpoint instead of going out to the internet. Therefore, verifying and potentially updating the VPC's route table to include routes for the Amazon S3 VPC gateway endpoint is the correct solution to resolve the connection issue faced by the AWS Glue job.
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?
The best solution for ensuring that data analysts can access data only for customers within the same country with the least operational effort is to register the S3 bucket as a data lake location in AWS Lake Formation. This service provides powerful data governance and fine-grained access control capabilities, including row-level security. By using Lake Formation’s row-level security features, you can easily define and enforce policies that restrict access to data based on specific conditions such as the country of the customer. This approach avoids the need for creating separate tables, views, or managing regional data migrations, thereby reducing operational overhead and complexity.
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
To minimize effort and time required to incorporate third-party datasets into an existing analytics platform, it is best to use API calls to access and integrate these datasets. AWS Data Exchange is designed specifically for discovering and subscribing to third-party datasets, providing direct and easy integration. This service effectively reduces operational overhead because it directly supports these types of integrations without the need for complex configurations or additional infrastructure. AWS DataSync, on the other hand, is primarily intended for data transfer between on-premises storage and AWS storage services, not for directly accessing third-party datasets through APIs.
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)
To implement a data mesh that supports centralized data governance, data analysis, and data access control, the most appropriate combination of AWS services would be Amazon S3 for data storage and Amazon Athena for data analysis, along with AWS Lake Formation for centralized data governance and access control. Amazon S3 provides a scalable and cost-effective storage solution that is well-suited for large datasets, while Amazon Athena allows for serverless querying of data stored in S3 using SQL, facilitating data exploration and analysis without the need for managing infrastructure. AWS Lake Formation enhances governance by allowing the establishment of fine-grained access controls and ensuring data quality and compliance, aligning with the need for centralized governance in a data mesh.
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?
Package the custom Python scripts into Lambda layers and apply these layers to the Lambda functions. Lambda layers allow for centralized management of shared code across multiple functions. Once the layer is updated with the new scripts, all the Lambda functions that use that layer will automatically inherit the updates, thus reducing manual efforts and ensuring consistency.