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NEW QUESTION: 1

A. Option B
B. Option C
C. Option D
D. Option A
E. Option E
Answer: A,B

NEW QUESTION: 2



Which three statements are correct regarding the various multicast groups? (Choose three.)
A. CE5 has joined the 224.0.1.40 multicast group
B. PE5 has a Null OILforthe (*,224.0.1.40) entry
C. CE5 has a Null OILforthe (*,224.1.1.1) entry
D. PE5 has a Null OILforthe (*,224.1.1.1) entry
E. Currently there is no source sending traffic to the 224.1.1.1 multicast group
Answer: A,C,D
Explanation:
#show ip mroute

NEW QUESTION: 3
Which of the following would be the best criterion to consider in determining the classification of an information asset?
A. Age
B. Personal association
C. Useful life
D. Value
Answer: D
Explanation:
Information classification should be based on the value of the information to the organization and its sensitivity (reflection of how much damage would accrue due to disclosure).
Age is incorrect. While age might be a consideration in some cases, the guiding principles should be value and sensitivity.
Useful life. While useful lifetime is relevant to how long data protections should be applied, the classification is based on information value and sensitivity.
Personal association is incorrect. Information classification decisions should be based on value of the information and its sensitiviry.
References:
CBK, pp. 101 - 102.

NEW QUESTION: 4
DRAG DROP
Case Study #2
This is a case study. Case studies are not limited separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Background
Wide World Importers has multidimensional cubes named SalesAnalysis and ProductSales. The SalesAnalysis cube is refreshed from a relational data warehouse. You have a Microsoft SQL Server Analysis Services instance that is configured to use tabular mode. You have a tabular data model named CustomerAnalysis.
Sales Analysis
The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes. The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
The SalesAnalysis model contains tables from a SQL Server database named SalesDB. You set the DirectQueryMode option to DirectQuery. Data analyst access data from a cache that is up to 24 hours old.
Data analyst report performance issues when they access the SalesAnalysis model.
When analyzing sales by customer, the total of all sales is shown for every customer, instead of the customer's sales value. When analyzing sales by product, the correct totals for each product are shown.
Customer Analysis
You are redesigning the CustomerAnalysis tabular data model that will be used to analyze customer sales.
You plan to add a table named CustomerPermission to the model. This table maps the Active Directory login of an employee with the CustomerId keys for all customers that the employee manages.
The CustomerAnalysis data model will contain a large amount of data and needs to be shared with other developers even if a deployment fails. Each time you deploy a change during development, processing takes a long time.
Data analysts must be able to analyze sales for financial years, financial quarters, months, and days. Many reports are based on analyzing sales by month.
Product Sales
The ProductSales cube allows data analysts to view sales information by product, city, and time. Data analysts must be able to view ProductSales data by Year to Date (YTD) as a measure. The measure must be formatted as currency, associated with the Sales measure group, and contained in a folder named Calculations.
Requirements
You identify the following requirements:
Data available during normal business hours must always be up-to-date.

Processing overhead must be minimized.

Query response times must improve.

All queries that access the SalesAnalysis model must use cached data by default.

Data analysts must be able to access data in near real time.

You need to configure the SalesAnalysis cube to correct the sales analysis by customer calculation.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

Answer:
Explanation:

Explanation/Reference:
Step 1: Open the cube editor, and open the Dimension Usage tab.
Step 2: Configure a relationship between the Customer dimension and the Sales measure group. Use Day as the granularity.
From scenario: The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes. The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
Step 3: Reprocess the cube.
Step 4: Deploy the project changes.