Amazon AWS Certified Data Engineer Data-Engineer-Associate

최신 업데이트버전 Data-Engineer-Associate덤프

저희는 2,3일에 한번씩 Data-Engineer-Associate덤프자료가 업데이트 가능한지 체크하고 있습니다. AWS Certified Data Engineer - Associate (DEA-C01)덤프가 업데이트된다면 업데이트된 버전을 고객님 구매시 사용한 메일주소로 발송해드립니다. Data-Engineer-Associate 덤프 업데이트서비스는 구매일로부터 1년내에 유효함으로 1년이 지나면 Data-Engineer-Associate덤프 업데이트서비스가 자동으로 종료됩니다. AWS Certified Data Engineer - Associate (DEA-C01)덤프 무료 업데이트 서비스를 제공해드림으로 고객님께서 구매하신 Data-Engineer-Associate덤프 유효기간을 최대한 연장해드립니다.

퍼펙트한 서비스 제공

승진이나 연봉인상을 꿈꾸면 승진과 연봉인상을 시켜주는 회사에 능력을 과시해야 합니다. IT인증시험은 국제적으로 승인해주는 자격증을 취득하는 시험입니다. 시험을 패스하여 자격증을 취득하면 회사에서 꽃길만 걷게 될것입니다. Data-Engineer-Associate인증시험덤프 덤프구매전 한국어 온라인 상담서비스부터 구매후 덤프 무료 업데이트버전 제공, Data-Engineer-Associate인증시험 덤프 불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. AWS Certified Data Engineer - Associate (DEA-C01)인증시험 덤프는 인기덤프인데 지금까지 Data-Engineer-Associate덤프를 구매한후 불합격으로 인해 환불신청하신 분은 아직 한분도 없었습니다.

Data-Engineer-Associate시험은 최근 제일 인기있는 인증시험입니다. IT업계에 종사하시는 분들은 자격증취득으로 자신의 가치를 업그레이드할수 있습니다. AWS Certified Data Engineer - Associate (DEA-C01) 시험은 유용한 IT자격증을 취득할수 있는 시험중의 한과목입니다. Amazon AWS Certified Data Engineer 최신버전 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다. Data-Engineer-Associate덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다. Data-Engineer-Associate시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를 공부하고 시험패스하세요.

시험준비시간 최소화

IT업계 엘리트한 강사들이 퍼펙트한 Data-Engineer-Associate시험응시 AWS Certified Data Engineer - Associate (DEA-C01)덤프문제집을 제작하여 디테일한 Data-Engineer-Associate문제와 답으로 여러분이 아주 간단히 AWS Certified Data Engineer - Associate (DEA-C01)시험응시를 패스할 수 있도록 최선을 다하고 있습니다.덤프구매후 2,3일 정도만 공부하시면 바로 시험보셔도 되기에 가장 짧은 시간을 투자하여 시험에서 패스할수 있습니다.

최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제:

1. A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII).
The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A) Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.
B) Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.
C) Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
D) Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

2. A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

A) Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.
B) Create an JAM role for each use case. Assign appropriate permissions to the role for each use case.
Associate the role with Athena.
C) Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
D) Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

3. A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?

A) Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
B) Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
C) Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.
D) Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3.
Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

4. A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A) Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
B) Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
C) Specify a combination of distribution, sort, and partition keys for all tables.
D) Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.

5. A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketingteam. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team.
The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)

A) Create an Amazon Redshift data than that includes the tables that need to be shared.
B) Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
C) Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
D) Create views of the tables that need to be shared. Include only the required columns.
E) Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.

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