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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
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AmazonのData-Engineer-Associateの認証試験は現在IT業界でもっとも人気があって、その試験に合格すれば君の生活と仕事にいいです。 JapancertはAmazonのData-Engineer-Associate「AWS Certified Data Engineer - Associate (DEA-C01)」の認証試験の合格率を高めるのウエブサイトで、Japancert中のIT業界の専門家が研究を通じてAmazonのData-Engineer-Associateの認証試験について問題集を研究し続けています。100%合格率は彼らの研究成果でございます。Japancertを選られば、成功しましょう。
質問 # 60
A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?
正解：C
解説：
Problem Analysis:
The company must log all user activities and connection activities in Amazon Redshift for security compliance.
Key Considerations:
Redshift supports audit logging, which can be configured to write logs to an S3 bucket.
S3 provides durable, scalable, and cost-effective storage for logs.
Solution Analysis:
Option A: S3 for Logging
Standard approach for storing Redshift logs.
Easy to set up and manage with minimal cost.
Option B: Amazon EFS
EFS is unnecessary for this use case and less cost-efficient than S3.
Option C: Aurora MySQL
Using a database to store logs increases complexity and cost.
Option D: EBS Volume
EBS is not a scalable option for log storage compared to S3.
Final Recommendation:
Enable Redshift audit logging and specify an S3 bucket as the destination.
Amazon Redshift Audit Logging
Storing Logs in Amazon S3
質問 # 61
An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs.
Which solution will meet these requirements?
正解：D
解説：
Option C is correct because AWS Lambda's advanced logging controls support separate application log level and system log level filtering, but AWS documentation states that for Lambda to filter application logs according to their log level, the function must use JSON formatted logs. AWS also documents that in the advanced logging configuration you can choose a log level such as ERROR for application logs and WARN for system logs. Therefore, configuring the function to use JSON log format is the necessary step that enables the required log-level filtering behavior.
Option A is irrelevant because the function already writes logs successfully. Option B alone is insufficient because changing code-level logging does not configure Lambda's separate system log filtering behavior, and Lambda's application log filtering relies on structured JSON logs. Option D changes the log destination but does not implement the required filtering levels. The official Lambda documentation makes clear that JSON log format is the enabling configuration for this feature, so that is the correct answer.
質問 # 62
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?
正解：C
解説：
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
B . Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
C . Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
D . Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
Reference:
1: AWS Data Exchange User Guide
2: AWS Data Exchange FAQs
3: AWS Glue Developer Guide
: AWS CodeCommit User Guide
: Amazon Kinesis Data Streams Developer Guide
: Amazon Elastic Container Registry User Guide
: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
質問 # 63
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
正解：C
解説：
The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:
Using the Amazon Redshift Data API
Using Amazon EventBridge with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift
質問 # 64
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?
正解：B
解説：
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
質問 # 65
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