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NEW QUESTION: 1
What are two security features enabled by default by the Autonomous Database? (Choose two.)
A. Transparent Data Encryption
B. Read Only access to OS Audit logs
C. Encrypted Database Links
D. SQL Net Encryption
E. One SYSDBA account
Answer: A,D
Explanation:
In Autonomous Database, Oracle encrypt your data everywhere-whether in motion in SQL'Net traffic, and at rest in tablespaces and backups by default, each Autonomous Database service is automatically configured to use industry-standard TLS 1.2 to encrypt data in transit between the database service and clients or applications. Required client certificates and networking information are automatically packaged for the service consumer when the service is provisioned Oracle encrypt data at rest, by default the Autonomous Database is automatically encrypted using Oracle Transparent Data Encryption in tablespaces and backups

NEW QUESTION: 2
次のコードを実行します。

Customersテーブルがdboスキーマ内に作成されたことがわかります。
テーブルをSchema2という名前の別のスキーマに移動するためのコードセグメントを作成する必要があります。
何を作るべきですか？
回答するには、適切なコードセグメントを回答領域の正しい場所にドラッグします。 （答えの選択肢は、一度だけ、二度以上、またはまったく使用されないかもしれません。）

Answer:
Explanation:

Explanation:
http://msdn.microsoft.com/en-us/library/ms173423.aspx

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 create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu