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NEW QUESTION: 1
Which statements are true for user-managed preconditions in AWS Data Pipeline? (choose three)
A. You can create, access and manage user-managed preconditions using the AWS Command Line Interface.
B. You can create, access and manage user-managed preconditions using the AWS Management Console.
C. You need to install the AWS SDK to manage user-managed preconditions.
D. User-managed preconditions only run on the computational resource that you have specified.
Answer: A,B,D
Explanation:
AWS Data Pipeline supports two types of preconditions. System-managed preconditions and user- managed preconditions.
System-managed preconditions are run by the AWS Data Pipeline web service on your behalf do not require a computational resource.
User Managed preconditions only run on the computational resource that you have specified.
You can create, access and manage your pipelines using the AWS Management Console.
You can create, access and manage your pipelines use the AWS Command Line Interface.
Reference:http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-managing- pipeline.html
NEW QUESTION: 2
You have been tasked to create an enterprise data warehouse. The data warehouse needs to collect data from each of the three channels' various systems and from public records for weather and economic data. Each data source sends data daily for consumption by the data warehouse.
Because each data source may be structured differently, an extract, transform, and load (ETL) process is performed to reformat the data into a common structure. Then, analytics can be performed across data from all sources simultaneously. Which tools shall you implement?
Choose the correct answer:
A. DynamoDB, Data Pipeline, SQS
B. RDS, EMR, Data Pipeline, Quicksight
C. S3, EMR, Redshift, Quicksight
D. S3, EMR, Data Pipeline, Lambda
Answer: C
Explanation:
The first step in this process is getting the data from the many different sources onto Amazon S3.
Amazon EMR is used to transform and cleanse the data from the source format into the destination and a format. Each transformation job then puts the formatted and cleaned data onto Amazon S3. Amazon Redshift loads, sorts, distributes, and compresses the data into its tables so that analytical queries can execute efficiently and in parallel. For visualizing the analytics, Amazon QuickSight can be used, or one of the many partner visualization platforms via the OBDC/JDBC connection to Amazon Redshift .
Reference:
https://d0.awsstatic.com/whitepapers/Big_Data_Analytics_Options_on_AWS.pdf
NEW QUESTION: 3
What does SSO stand for?
A. Single Screen Output
B. Single Sign On
C. Simple Sign On
D. Single Screen On
Answer: B
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Aalk - 2014-05-05 16:45:18
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