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NEW QUESTION: 1
You are administering a multitenant container database (CDB) that contains multiple pluggable databases (PDBs). RMAN is connected to CDB$ROOT.
Examine the command:
RMAN> LIST FAILURE;
Which statement is true about this command?
A. It lists failures only for the CDB root database.
B. It lists failures only if RMAN is connected to a PDB.
C. It lists failures for a PDB only when RMAN is connected to a recovery catalog.
D. It lists failures for the CDB and all the PDBs in this CDB.
Answer: A
Explanation:
Explanation/Reference:
In the current release, Data Recovery Advisor can only be used to diagnose and repair data corruptions in non-CDBs and the root of a multitenant container database (CDB). Data Recovery Advisor is not supported for pluggable databases (PDBs).
Note: You can run the LIST FAILURE command to show all known failures.
References: https://docs.oracle.com/database/121/BRADV/rcmrepai.htm

NEW QUESTION: 2
You are planning the creation of a new multitenant container database (CDB) and want to store the ROOT and SEED container data files in separate directories.
You plan to create the database using SQL statements.
Which three techniques can you use to achieve this?
A. Specify all files in the CREATE DATABASE statement without using Oracle managed Files (OMF).
B. Specify the PDB_FILE_NAME_CONVERT initialization parameter.
C. Use Oracle Managed Files (OMF).
D. Specify the DB_FILE_NAMECONVERT initialization parameter.
E. Specify the SEED FILE_NAME_CONVERT clause.
Answer: B,C,E
Explanation:
You must specify the names and locations of the seed's files in one of the following ways:
*(A) Oracle Managed Files
*(B) The SEED FILE_NAME_CONVERT Clause
*(C) The PDB_FILE_NAME_CONVERT Initialization Parameter

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to CSV module to the Azure ML experiment and then open the module output in a new notebook.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Access intermediate datasets from Machine Learning experiments
The following steps show an example that creates an experiment, runs it and accesses the intermediate dataset.
1. Create a new experiment.
2. Insert an Adult Census Income Binary Classification dataset module.
3. Insert a Split module, and connect its input to the dataset module output.
4. Insert a Convert to CSV module and connect its input to one of the Split module outputs.
5. Save the experiment, run it, and wait for it to finish running.
6. Click the output node on the Convert to CSV module.
7. When the context menu appears, select Generate Data Access Code
8. Select the code snippet and copy it to your clipboard from the window that appears..
9. Paste the code in your notebook.
Note: After an experiment is run in the Machine Learning Studio, it is possible to access the intermediate datasets from the output nodes of modules. Intermediate datasets are data that has been created and used for intermediate steps when a model tool has been run.
Intermediate datasets can be accessed as long as the data format is compatible with the Python client library.
The following formats are supported (constants for these are in the azureml.DataTypeIds class):
PlainText

GenericCSV

GenericTSV

GenericCSVNoHeader

GenericTSVNoHeader

References: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python- data-access

NEW QUESTION: 4
A public cloud service provider is facing several challenges in its IT operations:
1 - Rapidly increasing storage capacity needs, forcing frequent data migrations
2 - Transition to a converged network infrastructure with FCoE
3 - Deployment of several types of hypervisors simultaneously in production
4 - Long-term archival of very large volumes of tenant data for compliance
5 - In-flight encryption for tenants with stringent compliance requirements
Which challenge(s) would VPLEX address?
A. 1 and 2
B. 4 and 5
C. 3 only
D. 1 only
Answer: D