DEA-C01 試験問題を無料オンラインアクセス

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 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.
• C.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.
• D.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.

A company aggregates high-frequency sensor telemetry into an Amazon S3 data lake. Each sensor stream emits structured records every hour. The records include metadata such as sensor category, unit ID, operational state, event timestamp, and site location. The data scales up to millions of records each day. The company runs complex queries each day to uncover performance insights specific to sensor categories. Which solution will meet these requirements with the FASTEST query execution time?

• A.Persist the data in Parquet format. Partition the data by sensor category. Sort the data by date
• B.Persist the data in CSV format. Partition the data by date. Sort the data by operational status.
• C.Persist the data in CSV format. Partition the data by date. Sort the data by sensor category.
• D.Persist the data in Apache ORC format. Partition the data by date. Sort the data by sensor category.

A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)

• A.Use the Amazon Redshift query optimizer to review and select optimizations to implement.
• B.Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
• C.Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.
• D.Use Amazon Redshift Advisor to review and select optimizations to implement.
• E.Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.

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?

• A.Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
• B.Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
• C.Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
• D.Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster.
  Write the logs to a table in the Aurora MySQL database.

A global finance company needs to implement near real-time cross-Region synchronization of trading data between trading centers in the us-east-1 Region, the eu-west-2 Region, and the ap- northeast-1 Region. The company must ensure that data is encrypted in transit. The solution must ensure data ordering and consistency and must support cross-Region disaster recovery.
The solution must provide data latency of less than 500 milliseconds. Which solution will meet these requirements with the LEAST operational effort?

• A.Use Amazon Kinesis Data Streams to receive trading data from each AWS Region. Use Amazon Data Firehose to replicate data between Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in each Region. Configure AWS Key Management Service (AWS KMS) encryption and IAM roles to manage access.
• B.Deploy Apache Kafka Mirror Maker 2.0 in each AWS Region. Set up custom replication policies to handle cross-Region data synchronization. Configure the SSL security protocol.
• C.Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Replicator to establish fully interconnected replication relationships between MSK clusters in the three AWS Regions. Enable TLS encryption and IAM authentication. Set up cross-Region backup configurations.
• D.Deploy Apache Kafka Connect in each AWS Region. Use custom-developed connectors to set up cross-Region data replication. Configure the SSL security protocol.