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NEW QUESTION: 1
Which three statements are true about the startup and shutdown of multitenant container databases (CDBs) and pluggable databases (PDBs)?
A. When a CDB is open in restricted mode, PDBs must also be opened in restricted mode.
B. When a CDB instance is started, PDBs can be placed in open state by using database triggers or by executing the alter pluggable database command.
C. When a CDB is in mount state, PDBs are automatically placed in mount state.
D. A PDB opened in restricted mode allows only local users to connect.
E. All PDBs must be shut down before shutting down a CDB instance.
Answer: A,B,C
Explanation:
Explanation
Reference https://oracle-base.com/articles/12c/multitenant-startup-and-shutdown-cdb-and-pdb-12cr1
NEW QUESTION: 2
You have a web application running on six Amazon EC2 instances, consuming about 45% of resources on each instance. You are using auto-scaling to make sure that six instances are running at all times. The number of requests this. Which of the following architectural choices should you make?
A. Deploy 3 EC2 instances In one region and 3 other region and use Amazon Elastic Load Balancer
B. Deploy 6 EC2 instances in one availability zone and use Amazon Elastic Load Balancer
C. Deploy 2 EC2 instances in three regions and use Amazon Elastic Load Balancer
D. Deploy 3 EC2 instances in one availability zone and 3 in another availability zone and use Amazon Elastic Load Balancer
Answer: D
NEW QUESTION: 3
Your on-premises network contains a file server named Server1 that stores 500 GB of data.
You need to use Azure Data Factory to copy the data from Server1 to Azure Storage.
You add a new data factory.
What should you do next? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Install a self-hosted integration runtime
The Integration Runtime is a customer-managed data integration infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.
Box 2: Create a pipeline
With ADF, existing data processing services can be composed into data pipelines that are highly available and managed in the cloud. These data pipelines can be scheduled to ingest, prepare, transform, analyze, and publish data, and ADF manages and orchestrates the complex data and processing dependencies References:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-sql-azure-adf