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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q59-Q64):

NEW QUESTION # 59
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

• A. AWS Direct Connect
• B. AWS DataSync
• C. AWS Glue
• D. Amazon S3 Transfer Acceleration

Answer: B

Explanation:
AWS DataSync is an online data movement and discovery service that simplifies and accelerates data migrations to AWS as well as moving data to and from on-premises storage, edge locations, other cloud providers, and AWS Storage services1. AWS DataSync can copy data to and from various sources and targets, including Amazon S3, and handle files in multiple formats. AWS DataSync also supports incremental transfers, meaning it can detect and copy only the changes to the data, reducing the amount of data transferred and improving the performance. AWS DataSync can automate and schedule the transfer process using triggers, and monitor the progress and status of the transfers using CloudWatch metrics and events1.
AWS DataSync is the most operationally efficient way to transfer the data in this scenario, as it meets all the requirements and offers a serverless and scalable solution. AWS Glue, AWS Direct Connect, and Amazon S3 Transfer Acceleration are not the best options for this scenario, as they have some limitations or drawbacks compared to AWS DataSync. AWS Glue is a serverless ETL service that can extract, transform, and load data from various sources to various targets, including Amazon S32. However, AWS Glue is not designed for large-scale data transfers, as it has some quotas and limits on the number and size of files it can process3. AWS Glue also does not support incremental transfers, meaning it would have to copy the entire data set every time, which would be inefficient and costly.
AWS Direct Connect is a service that establishes a dedicated network connection between your on-premises data center and AWS, bypassing the public internet and improving the bandwidth and performance of the data transfer. However, AWS Direct Connect is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS DataSync, AWS Storage Gateway, or AWS CLI. AWS Direct Connect also has some hardware and location requirements, and charges you for the port hours and data transfer out of AWS.
Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to Amazon S3 over long distances, using the AWS edge locations and optimized network paths. However, Amazon S3 Transfer Acceleration is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS CLI, AWS SDK, or third-party software. Amazon S3 Transfer Acceleration also charges you for the data transferred over the accelerated endpoints, and does not guarantee a performance improvement for every transfer, as it depends on various factors such as the network conditions, the distance, and the object size. Reference:
AWS DataSync
AWS Glue
AWS Glue quotas and limits
[AWS Direct Connect]
[Data transfer options for AWS Direct Connect]
[Amazon S3 Transfer Acceleration]
[Using Amazon S3 Transfer Acceleration]

NEW QUESTION # 60
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?

• A. Add the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.
• B. Add a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.
• C. Add a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.
• D. Add the AWSGlueServiceRole managed policy to the data engineer's IAM user.

Answer: C

Explanation:
This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer's IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. Reference:
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions Troubleshoot Errors - Amazon SageMaker AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess

NEW QUESTION # 61
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

• A. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
• B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
• C. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
• D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.

Answer: B

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross-account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal. You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. References:
Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams

NEW QUESTION # 62
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

• A. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
• B. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements.
  Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
• C. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
• D. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

Answer: B

Explanation:
Option A is the most operationally efficient way to meet the requirements because it minimizes the number of steps and services involved in the data export process. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including Amazon S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. By creating a view in the SQL Server databases that contains the required data elements, the AWS Glue job can select the data directly from the view without having to perform any joins or transformations on the source data. The AWS Glue job can then transfer the data in Parquet format to an S3 bucket and run on a daily schedule.
Option B is not operationally efficient because it involves multiple steps and services to export the data. SQL Server Agent is a tool that can run scheduled tasks on SQL Server databases, such as executing SQL queries.
However, SQL Server Agent cannot directly export data to S3, so the query output must be saved as .csv objects on the EC2 instance. Then, an S3 event must be configured to trigger an AWS Lambda function that can transform the .csv objects to Parquet format and upload them to S3. This option adds complexity and latency to the data export process and requires additional resources and configuration.
Option C is not operationally efficient because it introduces an unnecessary step of running an AWS Glue crawler to read the view. 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. However, in this scenario, the schema and format of the data elements are already known and fixed, so there is no need to run a crawler to discover them. The AWS Glue job can directly select the data from the view without using the Data Catalog. Running a crawler adds extra time and cost to the data export process.
Option D is not operationally efficient because it requires custom code and configuration to query the databases and transform the data. An AWS Lambda function is a service that can run code in response to events or triggers, such as Amazon EventBridge. Amazon EventBridge is a service that can connect applications and services with event sources, such as schedules, and route them to targets, such as Lambda functions. However, in this scenario, using a Lambda function to query the databases and transform the data is not the best option because it requires writing and maintaining code that uses JDBC to connect to the SQL Server databases, retrieve the required data, convert the data to Parquet format, and transfer the data to S3.
This option also has limitations on the execution time, memory, and concurrency of the Lambda function, which may affect the performance and reliability of the data export process.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* AWS Glue Documentation
* Working with Views in AWS Glue
* Converting to Columnar Formats

NEW QUESTION # 63
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

• A. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
• B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
• C. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
• D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.

Answer: B

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross-account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal. You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. Reference:
Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams

NEW QUESTION # 64
......

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