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NEW QUESTION: 1
db1という名前のAzure SQLデータベースに接続するWebアプリがあります。
Query Storeランタイム統計をAzure Log Analyticsに送信するようにdb1を構成する必要があります。
このタスクを完了するには、Microsoft Azureポータルにサインインします。
A. 単一またはプールされたデータベースの診断テレメトリのストリーミングを有効にするには、次の手順に従います。
1. Azure SQLデータベースリソースに移動します。
2.診断設定を選択します。
3.以前の設定が存在しない場合は、[診断をオンにする]を選択するか、[設定の編集]を選択して以前の設定を編集します。最大3つの並列接続を作成して、診断テレメトリをストリーミングできます。
4. [診断設定の追加]を選択して、複数のリソースへの診断データの並列ストリーミングを構成します。

5.参照用に設定名を入力します。
6.ストリーミング診断データの宛先リソースを選択します：ストレージアカウントへのアーカイブ、イベントハブへのストリーミング、またはLog Analyticsへの送信。
7.標準のイベントベースの監視エクスペリエンスの場合、データベース診断ログテレメトリの次のチェックボックスをオンにします。QueryStoreRuntimeStatistics

8.高度な1分ベースの監視エクスペリエンスの場合は、基本メトリックのチェックボックスをオンにします。
9.保存を選択します。
B. 単一またはプールされたデータベースの診断テレメトリのストリーミングを有効にするには、次の手順に従います。
1. Azure SQLデータベースリソースに移動します。
2.診断設定を選択します。
3.以前の設定が存在しない場合は、[診断をオンにする]を選択するか、[設定の編集]を選択して以前の設定を編集します。最大3つの並列接続を作成して、診断テレメトリをストリーミングできます。
4. [診断設定の追加]を選択して、複数のリソースへの診断データの並列ストリーミングを構成します。

5.参照用に設定名を入力します。
6.ストリーミング診断データの宛先リソースを選択します：ストレージアカウントへのアーカイブ、イベントハブへのストリーミング、またはLog Analyticsへの送信。
7.標準のイベントベースの監視エクスペリエンスの場合、データベース診断ログテレメトリの次のチェックボックスをオンにします。QueryStoreRuntimeStatistics

8.高度な1分ベースの監視エクスペリエンスの場合は、基本メトリックのチェックボックスをオンにします。
9.保存を選択します。
Answer: A
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/metrics-diagnostic-telemetry-logging-streaming-export-configure

NEW QUESTION: 2
Event Management, Problem Management, Access Management und Request Fulfillment sind Teil welcher Phase des Service Lifecycle?
A. Serviceübergang
B. Servicebetrieb
C. Kontinuierliche Serviceverbesserung
D. Servicestrategie
Answer: B

NEW QUESTION: 3
Drag and drop each statement about IGMP from the left onto the correct IGMP version on the right

Answer:
Explanation:

Explanation

NEW QUESTION: 4
Almost all types of detection permit a system's sensitivity to be increased or decreased during an inspection process. If the system's sensitivity is increased, such as in a biometric authentication system, the system becomes increasingly selective and has the possibility of generating:
A. Higher False Rejection Rate (FRR)
B. Lower False Rejection Rate (FRR)
C. It will not affect either FAR or FRR
D. Higher False Acceptance Rate (FAR)
Answer: A
Explanation:
Explanation/Reference:
Almost all types of detection permit a system's sensitivity to be increased or decreased during an inspection process. If the system's sensitivity is increased, such as in a biometric authentication system, the system becomes increasingly selective and has a higher False Rejection Rate (FRR).
Conversely, if the sensitivity is decreased, the False Acceptance Rate (FRR) will increase. Thus, to have a valid measure of the system performance, the Cross Over Error (CER) rate is used. The Crossover Error Rate (CER) is the point at which the false rejection rates and the false acceptance rates are equal. The lower the value of the CER, the more accurate the system.
There are three categories of biometric accuracy measurement (all represented as percentages):
False Reject Rate (a Type I Error): When authorized users are falsely rejected as unidentified or unverified.
False Accept Rate (a Type II Error): When unauthorized persons or imposters are falsely accepted as authentic.
Crossover Error Rate (CER): The point at which the false rejection rates and the false acceptance rates are equal. The smaller the value of the CER, the more accurate the system.
NOTE:
Within the ISC2 book they make use of the term Accept or Acceptance and also Reject or Rejection when referring to the type of errors within biometrics. Below we make use of Acceptance and Rejection throughout the text for conistency. However, on the real exam you could see either of the terms.
Performance of biometrics
Different metrics can be used to rate the performance of a biometric factor, solution or application. The most common performance metrics are the False Acceptance Rate FAR and the False Rejection Rate FRR.
When using a biometric application for the first time the user needs to enroll to the system. The system requests fingerprints, a voice recording or another biometric factor from the operator, this input is registered in the database as a template which is linked internally to a user ID. The next time when the user wants to authenticate or identify himself, the biometric input provided by the user is compared to the template(s) in the database by a matching algorithm which responds with acceptance (match) or rejection (no match).
FAR and FRR
The FAR or False Acceptance rate is the probability that the system incorrectly authorizes a non- authorized person, due to incorrectly matching the biometric input with a valid template. The FAR is normally expressed as a percentage, following the FAR definition this is the percentage of invalid inputs which are incorrectly accepted.
The FRR or False Rejection Rate is the probability that the system incorrectly rejects access to an authorized person, due to failing to match the biometric input provided by the user with a stored template.
The FRR is normally expressed as a percentage, following the FRR definition this is the percentage of valid inputs which are incorrectly rejected.
FAR and FRR are very much dependent on the biometric factor that is used and on the technical implementation of the biometric solution. Furthermore the FRR is strongly person dependent, a personal FRR can be determined for each individual.
Take this into account when determining the FRR of a biometric solution, one person is insufficient to establish an overall FRR for a solution. Also FRR might increase due to environmental conditions or incorrect use, for example when using dirty fingers on a fingerprint reader. Mostly the FRR lowers when a user gains more experience in how to use the biometric device or software.
FAR and FRR are key metrics for biometric solutions, some biometric devices or software even allow to tune them so that the system more quickly matches or rejects. Both FRR and FAR are important, but for most applications one of them is considered most important. Two examples to illustrate this:
When biometrics are used for logical or physical access control, the objective of the application is to disallow access to unauthorized individuals under all circumstances. It is clear that a very low FAR is needed for such an application, even if it comes at the price of a higher FRR.
When surveillance cameras are used to screen a crowd of people for missing children, the objective of the application is to identify any missing children that come up on the screen. When the identification of those children is automated using a face recognition software, this software has to be set up with a low FRR. As such a higher number of matches will be false positives, but these can be reviewed quickly by surveillance personnel.
False Acceptance Rate is also called False Match Rate, and False Rejection Rate is sometimes referred to as False Non-Match Rate.
crossover error rate

Above see a graphical representation of FAR and FRR errors on a graph, indicating the CER CER
The Crossover Error Rate or CER is illustrated on the graph above. It is the rate where both FAR and FRR are equal.
The matching algorithm in a biometric software or device uses a (configurable) threshold which determines how close to a template the input must be for it to be considered a match. This threshold value is in some cases referred to as sensitivity, it is marked on the X axis of the plot. When you reduce this threshold there will be more false acceptance errors (higher FAR) and less false rejection errors (lower FRR), a higher threshold will lead to lower FAR and higher FRR.
Speed
Most manufacturers of biometric devices and softwares can give clear numbers on the time it takes to enroll as well on the time for an individual to be authenticated or identified using their application. If speed is important then take your time to consider this, 5 seconds might seem a short time on paper or when testing a device but if hundreds of people will use the device multiple times a day the cumulative loss of time might be significant.
Reference(s) used for this question:
Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition ((ISC)2 Press) (Kindle Locations 2723-2731). Auerbach Publications. Kindle Edition.
and
KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 37.
and
http://www.biometric-solutions.com/index.php?story=performance_biometrics