Zusammenfassung der Ressource
Frage 1
Frage
What is a "data lake"?
Antworten
-
A formal database for structured data.
-
A distributed file storage system containing raw, unstructured data.
-
A highly redundant server-based database system.
-
A collection of relational database schemas.
Frage 2
Frage
Which cloud service is commonly used to implement a data lake?
Antworten
-
Amazon RDS
-
Amazon S3
-
Amazon DynamoDB
-
Amazon EC2
Frage 3
Frage
What is the primary purpose of AWS Glue in the context of a data lake?
Antworten
-
To store data redundantly across regions.
-
To provide a SQL interface for querying raw data.
-
To crawl unstructured data and define schemas.
-
To optimize database queries for performance.
Frage 4
Frage
What tool allows SQL queries directly on data stored in Amazon S3?
Antworten
-
Amazon DynamoDB
-
Amazon Athena
-
Amazon ElasticSearch
-
Amazon Lambda
Frage 5
Frage
How does Redshift Spectrum enhance the capabilities of Amazon Redshift?
Antworten
-
By integrating with Amazon Glue to create schemas.
-
By querying data stored directly in Amazon S3.
-
By offering serverless SQL querying capabilities.
-
By storing all data in highly redundant clusters.
Frage 6
Frage
Why is partitioning data important in a data lake?
Antworten
-
To replicate data across regions for redundancy.
-
To organize raw files into predefined schemas.
-
To improve query performance by narrowing data access.
-
To ensure compatibility with Amazon Glue.
Frage 7
Frage
What is a typical partitioning strategy for storing log data?
Frage 8
Frage
How should you approach data lake architecture from a system design perspective?
Antworten
-
Design the data lake structure based on how end-users will query the data.
-
Store all data in a single bucket without structure to maximize flexibility.
-
Focus exclusively on schema design before considering query patterns.
-
Prioritize database migration over partitioning strategies.
Frage 9
Frage
What is one advantage of using off-the-shelf tools like AWS Glue and Amazon Athena?
Antworten
-
They allow complete control over low-level data management.
-
They eliminate the need to think about data structure.
-
They enable scalable and reliable big data solutions with minimal custom design.
-
They prevent redundancy in cloud storage systems.