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NEW QUESTION: 1
Which two statements are true regarding DNS doctoring? (Choose two.)
A. DNS doctoring for IPv4 is supported on SRX devices.
B. DNS doctoring for IPv4 and IPv6 is supported on SRX devices.
C. DNS doctoring translates the DNS CNAME payload.
D. DNS doctoring translates the DNS A-record.
Answer: A,D
Explanation:
Explanation/Reference:
Reference : http://www.juniper.net/techpubs/en_US/junos11.4/information-products/topic- collections/ security/software-all/security/index.html?topic-61847.html

NEW QUESTION: 2
Which of the following layer of an enterprise data flow architecture is concerned with the assembly and preparation of data for loading into data marts?
A. Data Mart layer
B. Data preparation layer
C. Desktop Access Layer
D. Data access layer
Answer: B
Explanation:
Explanation/Reference:
Data preparation layer -This layer is concerned with the assembly and preparation of data for loading into data marts. The usual practice is to per-calculate the values that are loaded into OLAP data repositories to increase access speed.
For CISA exam you should know below information about business intelligence:
Business intelligence(BI) is a broad field of IT encompasses the collection and analysis of information to assist decision making and assess organizational performance. To deliver effective BI, organizations need to design and implement a data architecture. The complete data architecture consists of two components The enterprise data flow architecture (EDFA) A logical data architecture
Various layers/components of this data flow architecture are as follows:
Presentation/desktop access layer - This is where end users directly deal with information. This layer includes familiar desktop tools such as spreadsheets, direct querying tools, reporting and analysis suits offered by vendors such as Congas and business objects, and purpose built application such as balanced source cards and digital dashboards.
Data Source Layer - Enterprise information derives from number of sources:
Operational data - Data captured and maintained by an organization's existing systems, and usually held in system-specific database or flat files.
External Data - Data provided to an organization by external sources. This could include data such as customer demographic and market share information.
Nonoperational data - Information needed by end user that is not currently maintained in a computer accessible format.
Core data warehouse -This is where all the data of interest to an organization is captured and organized to assist reporting and analysis. DWs are normally instituted as large relational databases. A property constituted DW should support three basic form of an inquiry.
Drilling up and drilling down - Using dimension of interest to the business, it should be possible to aggregate data as well as drill down. Attributes available at the more granular levels of the warehouse can also be used to refine the analysis.
Drill across - Use common attributes to access a cross section of information in the warehouse such as sum sales across all product lines by customer and group of customers according to length of association with the company.
Historical Analysis - The warehouse should support this by holding historical, time variant data. An example of historical analysis would be to report monthly store sales and then repeat the analysis using only customer who were preexisting at the start of the year in order to separate the effective new customer from the ability to generate repeat business with existing customers.
Data Mart Layer- Data mart represents subset of information from the core DW selected and organized to meet the needs of a particular business unit or business line. Data mart can be relational databases or some form on-line analytical processing (OLAP) data structure.
Data Staging and quality layer -This layer is responsible for data copying, transformation into DW format and quality control. It is particularly important that only reliable data into core DW. This layer needs to be able to deal with problems periodically thrown by operational systems such as change to account number format and reuse of old accounts and customer numbers.
Data Access Layer -This layer operates to connect the data storage and quality layer with data stores in the data source layer and, in the process, avoiding the need to know to know exactly how these data stores are organized. Technology now permits SQL access to data even if it is not stored in a relational database.
Data Preparation layer -This layer is concerned with the assembly and preparation of data for loading into data marts. The usual practice is to per-calculate the values that are loaded into OLAP data repositories to increase access speed. Data mining is concern with exploring large volume of data to determine patterns and trends of information. Data mining often identifies patterns that are counterintuitive due to number and complexity of data relationships. Data quality needs to be very high to not corrupt the result.
Metadata repository layer - Metadata are data about data. The information held in metadata layer needs to extend beyond data structure names and formats to provide detail on business purpose and context. The metadata layer should be comprehensive in scope, covering data as they flow between the various layers, including documenting transformation and validation rules.
Warehouse Management Layer -The function of this layer is the scheduling of the tasks necessary to build and maintain the DW and populate data marts. This layer is also involved in administration of security.
Application messaging layer -This layer is concerned with transporting information between the various layers. In addition to business data, this layer encompasses generation, storage and targeted communication of control messages.
Internet/Intranet layer - This layer is concerned with basic data communication. Included here are browser based user interface and TCP/IP networking.
Various analysis models used by data architects/ analysis follows:
Activity or swim-lane diagram - De-construct business processes.
Entity relationship diagram -Depict data entities and how they relate. These data analysis methods obviously play an important part in developing an enterprise data model. However, it is also crucial that knowledgeable business operative are involved in the process. This way proper understanding can be obtained of the business purpose and context of the data. This also mitigates the risk of replication of suboptimal data configuration from existing systems and database into DW.
The following were incorrect answers:
Desktop access layer or presentation layer is where end users directly deal with information. This layer includes familiar desktop tools such as spreadsheets, direct querying tools, reporting and analysis suits offered by vendors such as Congas and business objects, and purpose built application such as balanced source cards and digital dashboards.
Data Mart layer - Data mart represents subset of information from the core DW selected and organized to meet the needs of a particular business unit or business line. Data mart can be relational databases or some form on-line analytical processing (OLAP) data structure.
Data access layer - his layer operates to connect the data storage and quality layer with data stores in the data source layer and, in the process, avoiding the need to know to know exactly how these data stores are organized. Technology now permits SQL access to data even if it is not stored in a relational database.
The following reference(s) were/was used to create this question:
CISA review manual 2014 Page number 188

NEW QUESTION: 3
大規模な組織では、復旧時間目標（RTO）を定義することが主に次の責任です。
A. ITマネージャー。
B. シニアマネージャー。
C. ビジネスユニットマネージャー。
D. 情報セキュリティマネージャー。
Answer: B

NEW QUESTION: 4
ケーススタディ2-ContosoLtd
概要概要
既存の環境
Contoso、Ltd。は、屋外機器を製造する製造会社です。Contosoは四半期ごとに取締役会を開催し、財務アナリストがMicrosoft Excelレポートを手動で作成します。これには、会社の4つの事業部門のそれぞれの損益計算書、会社の貸借対照表、純利益の予測が含まれます。次の四半期に向けて。
データとソース
レポートのデータは3つのソースから取得されます。詳細な収益、コスト、および費用のデータは、AzureSQLデータベースから取得されます。要約貸借対照表データは、Microsoft Dynamics 365 BusinessCentralから取得されます。貸借対照表のデータは、両方とも日付に関連していることを除いて、損益の結果には関連していません。
次の四半期の月間収益と費用の予測は、Microsoft SharePointOnlineリストから取得されます。四半期予測は、日付、ビジネスユニット、部門、および製品カテゴリの共有ディメンションを使用して、損益の結果に関連しています。
純利益予測データ
純利益の予測データは、次の表に示す形式でProjectionsという名前のSharePointOnlineリストに保存されます。

収益予測は月次レベルで設定され、四半期の予測を示すために合計されます。
貸借対照表データ
貸借対照表データは、次の表に示す形式で、各アカウントの1か月あたりの最終残高とともにインポートされます。

貸借対照表データには、各月の各勘定科目の行が常にあります。
Dynamics 365 Business Central Data
Business Centralには、製品がビジネスユニットにロールアップする製品カテゴリにロールアップする方法を示す製品カタログが含まれています。収益データは、日付および製品レベルで提供されます。経費データは、日付および部門レベルで提供されます。
ビジネス上の問題
歴史的に、四半期ごとの取締役会のレポートを準備するのに1週間に2人のアナリストがかかりました。また、通常、四半期ごとに少なくとも1つの問題があり、Excel数式のセル参照が正しくないために、レポートの値が間違っています。各ビジネスユニットにロールアップする製品と部門が一貫して定義されていないため、レポートに矛盾する結果が生じる場合があります。
計画された変更
Contosoは、Microsoft Power Blを使用して、四半期ごとのレポートプロセスを自動化および標準化することを計画しています。同社は、レポートの作成にかかる時間を2日未満にしたいと考えています。同社は、取締役会の四半期報告を含むがこれに限定されないすべての報告で使用されるビジネスユニット、製品、および部門の共通ロジックを作成したいと考えています。
技術要件
Contosoは、最小限の手作業でレポートとデータセットを更新することを望んでいます。同社は、カスタムナビゲーションと補足情報へのリンクを含むレポートの単一パッケージをボードに提供したいと考えています。
データとアクセスの手動更新を含むメンテナンスは、可能な限り最小限に抑える必要があります。
セキュリティ要件
レポートは、powerbi.comからボードで利用できるようにする必要があります。メール対応のセキュリティグループは、取締役会と情報を共有するために使用されます。
各ビジネスユニットを担当するアナリストは、取締役会が見るすべてのデータを見る必要があります。ただし、損益データは、ビジネスユニットのデータのみに制限する必要があります。アナリストは、損益データを含むデータセットから新しいレポートを作成できる必要がありますが、アナリストが作成するレポートは、取締役会の四半期レポートに含めないでください。アナリストは、四半期レポートを誰とも共有できないようにする必要があります。
レポート要件
月の最終日に基づいて、貸借対照表をPowerBlの標準の日付テーブルに多対1の関係で関連付けることを計画しています。四半期報告パッケージの貸借対照表レポートの少なくとも1つは、前四半期だけでなく、四半期の期末残高も表示する必要があります。
予測には、収益予測額を含むRevenueProjectionという名前の列が含まれている必要があります。リレーションシップは、次の表に示す列を含むDateという名前のテーブルへのプロジェクションから作成する必要があります。

製品および部門とビジネスユニットとの関係は、すべてのレポートで一貫している必要があります。
取締役会は、四半期報告書から次の情報を入手できなければなりません。
-時間の経過に伴う収益の傾向
-各アカウントの期末残高
-四半期ごとの費用と予測の比較
-前四半期からの固定負債の変化
-四半期収益と同じ四半期の比較
前年
ドラッグアンドドロップの質問
ユーザーが適切なレベルの粒度でのみ予測を表示できるようにするDAXメジャーをデータモデルに作成する必要があります。
どのように対策を完了する必要がありますか？答えるには、適切な値を正しいターゲットにドラッグします。各値は、1回使用することも、複数回使用することも、まったく使用しないこともできます。コンテンツを表示するには、分割バーをペイン間でドラッグするか、スクロールする必要がある場合があります。
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:
Scenario: Revenue projections are set at the monthly level and summed to show projections for the quarter.
Box 1: IF
Box 2: ISFILTERED
ISFILTERED returns TRUE when columnName is being filtered directly. If there is no filter on the column or if the filtering happens because a different column in the same table or in a related table is being filtered then the function returns FALSE.
Box 3: SUM
Reference:
https://docs.microsoft.com/en-us/dax/isfiltered-function-dax