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NEW QUESTION: 1
脅威を緩和する手法を左側から、脅威の種類または攻撃を軽減する攻撃の右側にドラッグアンドドロップします。
Answer:
Explanation:
Explanation
Double-Tagging attack:In this attack, the attacking computer generates frames with two 802.1Q tags. The first tag matches the native VLAN of the trunk port (VLAN 10 in this case), and the second matches the VLAN of a host it wants to attack (VLAN 20).When the packet from the attacker reaches Switch A, Switch A only sees the first VLAN 10 and it matches with its native VLAN 10 so this VLAN tag is removed. Switch A forwards the frame out all links with the same native VLAN 10. Switch B receives the frame with an tag of VLAN 20 so it removes this tag and forwards out to the Victim computer.Note: This attack only works if the trunk (between two switches) has the same native VLAN as the attacker.To mitigate this type of attack, you can use VLAN access control lists (VACLs, which applies to all traffic within a VLAN. We can use VACL to drop attacker traffic to specific victims/servers) or implement Private VLANs.ARP attack (like ARP poisoning/spoofing) is a type of attack in which a malicious actor sends falsified ARP messages over a local area network as ARP allows a gratuitous reply from a host even if an ARP request was not received. This results in the linking of an attacker's MAC address with the IP address of a legitimate computer or server on the network. This is an attack based on ARP which is at Layer 2.Dynamic ARP inspection (DAI) is a security feature that validates ARP packets in a network which can be used to mitigate this type of attack.
NEW QUESTION: 2
All government bonds and the majority of corporate bonds are traded:
A. on regional exchanges.
B. in the over-the-counter market.
C. via electronic communication networks (ECNs).
D. on the floor of the NYSE.
Answer: B
Explanation:
Explanation/Reference:
Explanation: All government bonds and the majority of corporate bonds are traded in the over-the-counter market.
NEW QUESTION: 3
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックス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には1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION: 4
Examine the structure of the INVOICE table:
Exhibit:
Which two SQL statements would execute successfully? (Choose two.)
A. SELECT inv_no,NVL2(inv_amt,inv_amt*.25,'Not Available') FROM invoice;
B. SELECT inv_no,NVL2(inv_date,sysdate-inv_date,sysdate) FROM invoice;
C. SELECT inv_no,NVL2(inv_amt,inv_date,'Not Available') FROM invoice;
D. SELECT inv_no,NVL2(inv_date,'Pending','Incomplete') FROM invoice;
Answer: B,D
Explanation:
The NVL2 Function
The NVL2 function provides an enhancement to NVL but serves a very similar purpose. It evaluates whether a column or expression of any data type is null or not. 5-6 The NVL function\ If the first term is not null, the second parameter is returned, else the third parameter is returned. Recall that the NVL function is different since it returns the original term if it is not null. The NVL2 function takes three mandatory parameters. Its syntax is NVL2(original, ifnotnull, ifnull), where original represents the term being tested. Ifnotnull is returned if original is not null, and ifnull is returned if original is null. The data types of the ifnotnull and ifnull parameters must be compatible, and they cannot be of type LONG. They must either be of the same type, or it must be possible to convert ifnull to the type of the ifnotnull parameter. The data type returned by the NVL2 function is the same as that of the ifnotnull parameter.
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