DEA-C01 무료 덤프문제 온라인 액세스

A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
• B.Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
• C.Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.
• D.Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.

A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

• A.Use Hadoop Distributed File System (HDFS) as a persistent data store.
• B.Use Spot Instances for all primary nodes.
• C.Use Graviton instances for core nodes and task nodes.
• D.Use Amazon S3 as a persistent data store.
• E.Use x86-based instances for core nodes and task nodes.

A company is designing a data lake on Amazon S3. To ensure high performance when accessing the data, which best practice should the company adopt in organizing its data in the S3 bucket?

• A.Partition data based on commonly accessed attributes and use a consistent naming scheme for prefixes.
• B.Store all data files as a single large file and use AWS Lambda to parse required data segments.
• C.Use a flat structure by avoiding the creation of any prefix or "folder" hierarchy.
• D.Enable S3 Transfer Acceleration to ensure data is quickly accessible from any location.

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

A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use the streaming ingestion feature of Amazon Redshift.
• B.Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
• C.Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
• D.Use the Amazon Aurora zero-ETL integration with Amazon Redshift.