DEA-C01 試験問題を無料オンラインアクセス

Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?

• A.GRANT USAGE ON DATASHARE salesshare TO NAMESPACES '<account ID 1>' AND
  '<account ID 2>';
• B.GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>';
  GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';
• C.GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 1>';
  GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 2>';
• D.GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS '<account ID 1>' AND '<account ID 2>';

A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead?
(Choose two.)

• A.Update the security group of the DB instance to allow only Lambda function invocations on the database port.
• B.Attach the same security group to the Lambda function and the DB instance. Include a self- referencing rule that allows access through the database port.
• C.Configure the Lambda function to run in the same subnet that the DB instance uses.
• D.Turn on the public access setting for the DB instance.
• E.Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

• A.Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
• B.Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
• C.Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
• D.Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required fields and empty files.

A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys.
Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

• A.Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
• B.Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database.
  Configure the Sqoop job to write the data to Amazon S3 in Parquet format.
• C.Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
• D.Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.

A company uses Amazon SageMaker AI for its machine learning (ML) workflows. The company is organized into several project groups that use sensitive data. The company needs to give the project groups the ability to discover available datasets across different AWS accounts. The solution must maintain access controls and track all data access for compliance purposes.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use Amazon SageMaker Assets to publish, discover, and request access to datasets through the asset catalog with approval workflows that track data access.
• B.Set up Amazon SageMaker Feature Store with cross-account access policies to automatically share data between AWS accounts without requiring approval workflows.
• C.Set up IAM roles for each project group with permissions to access all datasets across all AWS accounts. Use AWS CloudTrail to record data access activity.
• D.Create separate Amazon SageMaker Studio domains for each project group with isolated environments and no ability to share data between domains.