MSP Bank, Limited is a leading Japanese monetary institution that provides a full range of financial products and services to both institutional and individual customers. It is headquartered in Tokyo.MSP Bank is hosting their existing infrastructure on on premise DC and AWS and maintains a hybrid environment.
MSP Bank hosts multiple web applications, CRM and ERP running on premise while moving storage, compute, DWH and AI running out of AWS. Also MSP is launching new applications running on AWS environment. MSP Banks hosts their Development, Testing and Production VPC to maintain different environments and maintains VPN connectivity between on premise DC and AWS.
MSP Bank is planning to build a data lake on all the log files stored in S3, captured from different applications running out of on premise and AWS and also identified data sets captured out of CRM, ERP and other Business applications . MSP Bank is looking at AWS Glue to acts as a fully managed ETL service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. What are the key components and functionalities of AWS Glue? Select 3 options.

Answer options:

A.Crawlers scan data in all kinds of repositories, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Data Catalog
B.AWS Glue autogenerate Scala or PySpark (the Python API for Apache Spark) scripts with AWS Glue extensions that use and modify to perform ETL operations through AWS Glue ETL operations
C.AWS Glue Data Catalog,persistent metadata store that lets store, annotate, and share metadata in the AWS Cloud
D.Crawlers scan data in all kinds of repositories, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Meta Store
E.AWS Glue Meta Store,persistent metadata store that lets store, annotate, and share metadata in the AWS Cloud
F.AWS Glue autogenerate Scala or PySpark (the Python API for Apache Spark) scripts with AWS Glue extensions that use and modify to perform ETL operations through AWS Glue Jobs system

Answer: A, B, C
Option A is correct -AWS Glue also lets you set up crawlers that can scan data in all kinds of repositories, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Data Catalog. From there it can be used to guide ETL operations.
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html
Option B is correct -Using the metadata in the Data Catalog, AWS Glue can autogenerate Scala or PySpark (the Python API for Apache Spark) scripts with AWS Glue extensions that you can use and modify to perform various ETL operations. For example, you can extract, clean, and transform raw data, and then store the result in a different repository, where it can be queried and analyzed.
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html
Option C is correct -The AWS Glue Data Catalog is your persistent metadata store. It is a managed service that lets you store, annotate, and share metadata in the AWS Cloud in the same way you would in an Apache Hive metastore.
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html
Option D is incorrect -AWS Glue also lets you set up crawlers that can scan data in all kinds of repositories, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Data Catalog. From there it can be used to guide ETL operations
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html
Option E is incorrect -The AWS Glue Data Catalog is your persistent metadata store. It is a managed service that lets you store, annotate, and share metadata in the AWS Cloud in the same way you would in an Apache Hive metastore.
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html
Option F is incorrect -Using the metadata in the Data Catalog, AWS Glue can autogenerate Scala or PySpark (the Python API for Apache Spark) scripts with AWS Glue extensions that you can use and modify to perform various ETL operations. For example, you can extract, clean, and transform raw data, and then store the result in a different repository, where it can be queried and analyzed.
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html