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NEW QUESTION # 23
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. Using DataBrew recipes, you can easily perform transformations such as renaming columns, removing specific columns, ignoring certain rows, creating new columns, and filtering data based on column values. This solution requires the least development effort because it provides a no-code/low-code interface for performing these tasks.
While AWS Glue Python jobs can handle these transformations, they would require writing custom code, which involves more development effort compared to using DataBrew.
AWS Glue crawlers are used for cataloging data and are not suitable for performing complex transformations like ignoring rows, renaming columns, or creating new columns.
Using an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files adds unnecessary complexity. You would need to orchestrate multiple jobs and workflows, which requires more setup and development compared to using DataBrew for the transformations.
NEW QUESTION # 24
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data.
The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)
Answer: C,E
Explanation:
Choice A ensures that live transactional data from PostgreSQL can be accessed directly within Redshift queries.
Choice C archives historical data in Amazon S3, reducing storage costs in Redshift while still making the data accessible via Redshift Spectrum.
NEW QUESTION # 25
A company has implemented a lake house architecture in Amazon Redshift. The company needs to give users the ability to authenticate into Redshift query editor by using a third-party identity provider (IdP).
A data engineer must set up the authentication mechanism.
What is the first step the data engineer should take to meet this requirement?
Answer: C
Explanation:
To enable users to authenticate into the Amazon Redshift query editor using a third-party identity provider (IdP), the data engineer must first register that IdP within the configuration settings of the Redshift cluster itself.
Amazon Redshift natively supports integrating with external identity providers to manage user authentication. By registering the third-party IdP directly in the Redshift cluster settings, it establishes the trust relationship needed for Redshift to rely on that IdP for authenticating users when they log into the query editor.
NEW QUESTION # 26
Which are the Cloud Platforms that Support Calling an External Function?
Answer: C
NEW QUESTION # 27
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party tool in the on- premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers.
The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
All of the components contained in the outer box (in the image below) appear as a single Amazon MWAA environment in your account. The Apache Airflow Scheduler and Workers are AWS Fargate (Fargate) containers that connect to the private subnets in the Amazon VPC for your environment. Each environment has its own Apache Airflow metadatabase managed by AWS that is accessible to the Scheduler and Workers Fargate containers via a privately-secured VPC endpoint.
https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html
NEW QUESTION # 28
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