[Free] 2018(Jan) EnsurePass Examcollection Oracle 1z0-531 Dumps with VCE and PDF 11-20

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Oracle Essbase 11 Essentials

Question No: 11

During a multidimensional analysis getting data from a supplemental data source is an example of .

  1. Drill across

  2. Drill Through

  3. Trending

  4. Pivoting

Answer: A

Question No: 12

Identify the two true statements about expense reporting tags.

  1. Provide accurate time balance calculations

  2. Provide accurate variance reporting on revenue and expense accounts

  3. Are assigned to the dimension tagged Time

  4. Are assigned to the dimension tagged Accounts

  5. Are assigned to the Dimension containing variance members.

Answer: B,D

Explanation: B: The variance reporting calculation requires that any item that represents an expense to the company must have an expense reporting tag.

Essbase provides two variance reporting properties: expense and non-expense. The default is non-expense.

Variance reporting properties define how Essbase calculates the difference between actual and budget data in members with the @VAR or @VARPER function in their member formulas.

D: Expense reporting is tagged to the accounts dimension such that variance, profit etc. Member will not show the negative value when we calculate it.

Note: The first, last, average, and expense tags are available exclusively for use with accounts dimension members.

Question No: 13

You are building a sales analysis model. In this model there is no requirement for calculation. The user needs to aggregate data across all dimensions and wants to archive many years of data. Archived data will be analyzed once in while.

What types of cube would you build using Essbase for this kind of requirement?

  1. Block Storage

  2. XOLAP

  3. Aggregate Storage

  4. Virtual Cube

Answer: C

Explanation: Consider using the aggregate storage storage model if the following is true for your database:

*The database is sparse and has many dimensions, and/or the dimensions have many levels of members.

*The database is used primarily for read-only purposes, with few or no data updates. (C)

*The outline contains no formulas except in the dimension tagged as Accounts.

*Calculation of the database is frequent, is based mainly on summation of the data, and does not rely on calculation scripts.

Question No: 14

How are the ASO data files managed?

  1. Page files

  2. Index files

  3. Table spaces

  4. Bin files

Answer: C

Explanation: With regard to ASO, table spaces are comparable to page and index files For ASO – the data is stored in table spaces in a .dat file in the \App\Appname\default folder. Again the format is some type of binary and you cannot open the file and do anything with the contents.


For BSO – the data is stored in the page files (*.pag) in the \App\Appname\DBname folder. The format is proprietary or some kind of binary, point being you can#39;t open a .pag file and do anything with it.

Question No: 15

Given the following information, how many potential blocks?

Ensurepass 2018 PDF and VCE

A. 100

B. 70 C. 560 D. 350 E. 400

F. 50

Answer: F

Explanation: Potential number of blocks: Indicates the maximum possible number of blocks that can exist for the database. The number is derived by multiplying the number of stored members in each sparse dimension.

In this scenario we get: 10 x 5 = 50

Question No: 16

The data block density for a particular BSO database is between 10% and 90%, and data values within the block do not consecutively repeat. Which type of compression would be most appropriate to use?

  1. Bitmap

  2. RLE

  3. ZLIB

  4. No compression required

Answer: A

Explanation: Bitmap is good for non-repeating data. It will use Bitmap or IVP (Index Value Pair).

Note: Bitmap compression, the default. Essbase stores only non-missing values anduses a bitmapping scheme. A bitmap uses one bit for each cell in the data block, whether the cell value is missing or non-missing. When a data block is not compressed, Essbase uses 8 bytes to store every non-missing cell. In most cases, bitmap compression conserves disk space more efficiently. However, much depends on the configuration of the data.

Question No: 17

You need to calculate average units sold by the customer dimension within an ASO database. The member formula should calculate correctly regardless of level within the customer dimension. Identify the correct syntax for the member formula.

  1. @AVG (SKIPBOTH, “Units_Sold”);

  2. Avg(Customer.CurrentMember.Children, [Units_Sold])

  3. Avg([Customer].Children, [Units_Sold]);

  4. Avg(Customer.CurrentMember.Children, [Units_Sold]);

  5. Avg(Customer.Children, [Units.Sold]);

Answer: B

Explanation: A custom rollup technique, custom rollup formulas, lets the cube builder define an MDX formula for each dimension level. Analysis Services uses this formula to determine the value of the dimension level#39;s members. For example, you could use an AVERAGE function rather than a summation to determine all members in one dimension level. If you use the AVERAGE function, the MDX formula for a dimension called Customers would be Avg( Customers.CurrentMember.Children ).

Note: The MultiDimensional eXpressions (MDX) language provides a specialized syntax for querying and manipulating the multidimensional data stored in OLAP cubes. While it is possible to translate some of these into traditional SQL, it would frequently require the synthesis of clumsy SQL expressions even for very simple MDX expressions. MDX has been embraced by a wide majority of OLAP vendors and has become the standard for OLAP systems.

Question No: 18

With an average block density greater than 90%.

  1. You should reconsider the dense and sparse settings

  2. You should consider no compression

  3. You should set Commit blocks to

  4. You should reconsider the outline order of dimensions

Answer: B

Explanation: Hyperion recommends that you should turn compression off if you have a block density gt; 90% (rarely happens) and you should change to RLE compression when the block density is lt; 3% (or if you have all the same values in the database – lots of zeros).

Note: You may want to disable data compression if blocks have very high density (90% or greater) and have few consecutive, repeating data values. Under these conditions, enabling compression consumes resources unnecessarily. Don#39;t use compression if disc space/memory is not an issue compared to your application. It can become a drain on the processor.

Question No: 19

You are performing incremental loads to an ASO database during the day, providing near real time data to the SaleDtl ASO database. Before the incremental load, you need to clear a specific set of data in the fastest amount of time possible.

What is the best solution?

  1. Partial clears are supported for ASO

  2. Perform a logical clear, using MDX to specify the region to be cleared

  3. Perform a physical clear, using MDX to specify the region to be cleared

  4. Run a calc script containing the CLEARDATA command and a set of FIX statements that isolate the desired data set

  5. Run a calc script containing the CLEARBLOCK command and a set of FIX statements that isolate the desired data set

Answer: A,B

Explanation: Within ASO partial clear are supported. Logical clear is faster than physical clear.

Question No: 20

Identify the two true statements about materialization in ASO.

  1. When performing an incremental data load, aggregate views are updated

  2. The database is not available during materialization

  3. Materialization can be tuned via query hints and hard restrictions defined at the database level

  4. Materialization scripts can be saved for future reuse

Answer: A,D

Explanation: The following process is recommended for defining and materializing aggregations:

  • After the outline is created or changed, load data values.

  • Perform the default aggregation. Do not select specify a storage stopping point option.

  • Materialize the suggested aggregate views and save the default selection in an aggregation script.

  • Run the types of queries the aggregation is being designed for.

  • If query time or aggregation time is too long, consider fine-tuning the aggregation as stated below.

  • Save the aggregation selection as an aggregation script (D)

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