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Question 19 - DAS-C01 discussion

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A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala. Operational management should be limited. Which combination of components can meet these requirements? (Choose three.)

A.
AWS Glue Data Catalog for metadata management
Answers
A.
AWS Glue Data Catalog for metadata management
B.
Amazon EMR with Apache Spark for ETL
Answers
B.
Amazon EMR with Apache Spark for ETL
C.
AWS Glue for Scala-based ETL
Answers
C.
AWS Glue for Scala-based ETL
D.
Amazon EMR with Apache Hive for JDBC clients
Answers
D.
Amazon EMR with Apache Hive for JDBC clients
E.
Amazon Athena for querying data in Amazon S3 using JDBC drivers
Answers
E.
Amazon Athena for querying data in Amazon S3 using JDBC drivers
F.
Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore
Answers
F.
Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore
Suggested answer: B, E, F

Explanation:


Reference: https://d1.awsstatic.com/whitepapers/Storage/data-lake-on-aws.pdf

asked 16/09/2024
Gokul Kalaiselvi Loganathan
47 questions
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