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NEW QUESTION: 1
Which of the following is the PRIMARY reason to use key control indicators (KCIs) to evaluate control operating effectiveness?
A. To monitor the achievement of set objectives
B. To raise awareness of operational issues
C. To measure business exposure to risk
D. To identify control vulnerabilities
Answer: A

NEW QUESTION: 2
Which statement is true about the NameNode in Hadoop?
A. If the NameNode goes down and a secondary NameNode has not been defined, the cluster is still accessible.
B. When loading data, the NameNode tells the client or program where to write the data.
C. A query in Hadoop requires a MapReduce job to be run so the NameNode gets the location of the data from the JobTracker.
D. All data passes through the NameNode; so if it is not sized properly, it could be a potential bottleneck.
Answer: A
Explanation:
Note that, in an HA cluster, the Standby NameNode also performs checkpoints of the namespace state, and thus it is not necessary to run a Secondary NameNode,
CheckpointNode, or BackupNode in an HA cluster. In fact, to do so would be an error.
In a typical HA cluster, two separate machines are configured as NameNodes. At any point in time, exactly one of the NameNodes is in an Active state, and the other is in a Standby state.
Note that, in an HA cluster, the Standby NameNode also performs checkpoints of the namespace state, and thus it is not necessary to run a Secondary NameNode,
CheckpointNode, or BackupNode in an HA cluster.
References: https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop- hdfs/HDFSHighAvailabilityWithNFS.html

NEW QUESTION: 3
決定木アルゴリズムを使用して分類モデルをトレーニングします。
次のPythonコードを実行して、Estimatorを作成します。変数feature_namesはすべての機能名のリストであり、class_namesはすべてのクラス名のリストです。
からinterpret.ext.blackboxインポートTabularExplainer

すべての機能の重要性を判断することにより、すべてのクラスのモデルによって行われた予測を説明する必要があります。
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Yes
TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).
Box 2: Yes
To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.
Box 3: No
TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml

NEW QUESTION: 4
Which two characteristics apply to an Intrusion Prevention System (IPS) ? Choose two
A. Cannot drop the packet on its own
B. Does not add delay to the original traffic.
C. Cabled directly inline with the flow of the network traffic.
D. Runs in promiscuous mode.
E. Can drop traffic based on a set of rules.
Answer: C,E
Explanation:
Explanation
+ Position in the network flow: Directly inline with the flow of network traffic and every packet goes through the sensor on its way through the network.
+ Mode: Inline mode
+ The IPS can drop the packet on its own because it is inline. The IPS can also request assistance from another device to block future packets just as the IDS does.
Source: Cisco Official Certification Guide, Table 17-2 IDS Versus IPS, p.461