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NEW QUESTION: 1
小売会社の開発者は、不正検出ソリューションを注文処理ソリューションに統合する必要があります。不正検出ソリューションでは、注文の確認に10分から30分かかります。ピーク時には、Webサイトは1分あたり100の注文を受け取ることができます。
注文処理パイプラインに不正検出ソリューションを追加するための最もスケーラブルな方法は何ですか？
A. すべての新しい注文をAmazon DynamoDBに書き込みます。すべての新しい注文を含めるようにDynamoDBストリームを設定します。
Kinesis Streamから自動的にレコードのバッチを読み取るためのLambda関数を購読します。
Lambda機能は不正検出ソフトウェアを含み、合格または不合格ステータスで注文を更新します。
B. すべての新しい注文をAmazon Kinesis Streamに追加します。 Kinesis Streamから自動的にレコードのバッチを読み取るためのLambda関数を購読します。 Lambda機能は不正検出ソフトウェアを含み、合格または不合格ステータスで注文を更新します。
C. すべての新しい注文をAmazon SQSキューに追加します。複数のAZにまたがる10個のEC2インスタンスをそれらにインストールされた不正検出ソリューションで構成し、このキューから注文を引き出します。
合格または不合格のステータスで注文を更新します。
D. すべての新しい注文をSQSキューに追加します。キュー深度メトリックをスケールの単位として使用するAuto Scalingグループを設定して、複数のAZにまたがる動的サイズのEC2インスタンスを起動し、不正検出ソリューションをインストールしてこのキューから注文を引き出します。合格または不合格のステータスで注文を更新します。
Answer: A

NEW QUESTION: 2
Which of the following attacks could be used to initiate a subsequent man-in-the-middle attack?
A. Replay
B. DoS
C. Brute force
D. ARP poisoning
Answer: A
Explanation:
A replay attack (also known as playback attack) is a form of network attack in which a valid data transmission is maliciously or fraudulently repeated or delayed. This is carried out either by the originator or by an adversary who intercepts the data and retransmits it, possibly as part of a masquerade attack by IP packet substitution (such as stream cipher attack).
For example: Suppose Alice wants to prove her identity to Bob. Bob requests her password as proof of identity, which Alice dutifully provides (possibly after some transformation like a hash function); meanwhile, Eve is eavesdropping on the conversation and keeps the password (or the hash). After the interchange is over, Eve (posing as Alice) connects to Bob; when asked for a proof of identity, Eve sends Alice's password (or hash) read from the last session, which Bob accepts thus granting access to Eve.
Countermeasures: A way to avoid replay attacks is by using session tokens: Bob sends a onetime token to Alice, which Alice uses to transform the password and send the result to Bob (e.g. computing a hash function of the session token appended to the password). On his side Bob performs the same computation; if and only if both values match, the login is successful. Now suppose Eve has captured this value and tries to use it on another session; Bob sends a different session token, and when Eve replies with the captured value it will be different from Bob's computation. Session tokens should be chosen by a (pseudo-) random process. Otherwise Eve may be able to pose as Bob, presenting some predicted future token, and convince Alice to use that token in her transformation. Eve can then replay her reply at a later time (when the previously predicted token is actually presented by Bob), and Bob will accept the authentication. One-time passwords are similar to session tokens in that the password expires after it has been used or after a very short amount of time. They can be used to authenticate individual transactions in addition to sessions. The technique has been widely implemented in personal online banking systems. Bob can also send nonces but should then include a message authentication code (MAC), which Alice should check. Timestamping is another way of preventing a replay attack. Synchronization should be achieved using a secure protocol. For example Bob periodically broadcasts the time on his clock together with a MAC. When Alice wants to send Bob a message, she includes her best estimate of the time on his clock in her message, which is also authenticated. Bob only accepts messages for which the timestamp is within a reasonable tolerance. The advantage of this scheme is that Bob does not need to generate (pseudo-) random numbers, with the trade-off being that replay attacks, if they are performed quickly enough i.e. within that 'reasonable' limit, could succeed.

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You maintain a Microsoft SQL Server instance that contains the following databases SalesDb1, SalesDb2, and SalesDb3. Each database has tabled named Products and Sales. The following table shows the configuration of each database.

The backup strategies for each database are described in the following table.

Each full or differential backup operation writes into a new file and uses a different sequence number. You observe the following database corruption issues.

SalesDb3 reports a number of database corruption issues related to error 823 and 824 when reading data pages.
You must display the following information about the corrupted pages:
* database name
* impacted file id
* impacted file physical name
* impacted page id
* event type that identifies the error type
* error count
Users report performance issues when they run queries against SalesDb2. You plan to monitor query statistics and execution plans for SalesDb2 by using Query Store. The monitoring strategy must meet the following requirements:
* Perform automatic data cleanup when query store disk usage reaches 500 megabyte (MB).
* Capture queries based on resource consumption.
* Use a stale query threshold value of 60 days.
The query optimizer generates suboptimal execution plans for a number of queries on the Sales table in SalesDb2. You will create a maintenance plan that updates statistics for the table. The plan should only update statistics that were automatically created and have not been updated for 30 days. The update should be based on all data in the table.
You need to view the information about the corrupted pages on SalesDb3.
How should you complete the Transact-SQL statement?
Select two.
A. SELECT * FROM msdb.. corrupted_pages
B. SELECT * FROM system..corrupted_pages
C. SELECT * FROM system..suspect_pages
D. WHERE event_type = 2
E. SELECT * FROM msdb..suspect_pages
F. WHERE event_type = 1
G. WHERE event_type = 3
Answer: D,E
Explanation:
Explanation
suspect_pages contains one row per page that failed with a minor 823 error or an 824 error. Pages are listed in this table because they are suspected of being bad, but they might actually be fine. When a suspect page is repaired, its status is updated in the event_type column.
The suspect_pages table resides in the msdb database.
SalesDb3 has pages with checksum errors. Checksum errors have the event_type value 2.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/backup-restore/manage-the-suspect-pages-table-sql-ser

NEW QUESTION: 4
Which statement is correct about automated modeling nodes in IBM SPSS Modeler Professional?
A. The Auto Clustering node generates Neural Net models.
B. The Auto Classifier node supports Cox regression.
C. The Auto Numeric node supports Bayes Net models.
D. The Auto Numeric node generates Linear models.
Answer: D