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NEW QUESTION: 1
Consider the CHECK TABLE command.
In which two situations should this command be used? (Choose two.)
A. to find out why a query takes a long time to execute on a given table
B. to improve performance by updating index distributing statistics on InnoDB tables
C. to make sure a table has no structural problems
D. to repair table structure problem
E. to make sure that no table indexes are corrupted
Answer: C,E
Explanation:
The CHECK TABLE statement performs an integrity check on table structure and contents.
It works for MyISAM and InnoDB tables. For MyISAM tables, it also updates the index statistics. If the table is a view, CHECK TABLE verifies the view definition. If the output from CHECK TABLE indicates that a table has problems, the table should be repaired.

NEW QUESTION: 2
In a data retention policy, what is perhaps the most crucial element?
Response:
A. Location of the data archive
B. Security controls in long-term storage
C. Frequency of backups
D. Data recovery procedures
Answer: D

NEW QUESTION: 3
The Chamber Health Plan reimburses primary care physicians on a monthly basis by using a simple capitation method. Chamber assumes an annual utilization rate of three visits per year. The FFS rate per office visit is $75, and all plan members are required to make a $10 copayment for each office visit. This information indicates that the capitation rate that Chamber calculates per member per month (PMPM) is equal to:
A. $21.25
B. $6.25
C. $16.25
D. $18.75
Answer: C

NEW QUESTION: 4
Azure Machine Learningワークスペースから実行される実験を取得するコードを記述します。
この実行では、Azure Machine Learningのモデル解釈サポートを使用して、モデルの説明を生成およびアップロードしました。
組織のビジネスマネージャーは、モデルの機能の重要性を確認したいと考えています。
次のような出力で、モデルの機能とそれらの相対的な重要性を印刷する必要があります。

どのようにコードを完成させる必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: from_run_id
from_run_id(workspace, experiment_name, run_id)
Create the client with factory method given a run ID.
Returns an instance of the ExplanationClient.
Parameters
* Workspace Workspace An object that represents a workspace.
* experiment_name str The name of an experiment.
* run_id str A GUID that represents a run.
Box 2: list_model_explanations
list_model_explanations returns a dictionary of metadata for all model explanations available.
Returns
A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time Box 3: explanation Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.explanation_client.explanationclient?view=azure-ml-py