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Boxi Interview Questions and Answers

Q1): What are the differences between Personal, Shared, and Secured connections?

Ans: 1. A Personal connection is created by one user and cannot be used by other users. The connection details are stored in PDAC.LSI file.
2. A shared connection can be used by other users through a shared server. The connection details are stored in SDAC.LSI file in the Business Objects installation folder. However one cannot set rights and securities on objects in a shared connection. Neither can a Universe to exported to the repository using a shared connection.
3. A secured connection overcomes these limitations. Through it, rights can be set on objects and documents. Universes can be exported to the central repository only through a secured connection. The connection parameters, in this case, are saved in the CMS.

Q2): What are custom hierarchies? How can they be created?

Ans: Custom Hierarchies are defined in a universe in order to facilitate custom drill down between objects from the same or different classes according to user requirements. They can be created from Tools -> Hierarchies in the BO Designer.

Q3): What is a context in-universe? How are they created?

Ans: In a universe, a context defines a particular join path between tables or a specific group of joins for a particular query. Any objects created on a table column that belong to specific contexts are naturally compatible with all other objects from the same contexts. When objects from two or more contexts are used, separate SQL is generated and results are then merged in a micro cube. This makes sure that no incorrect result is generated due to a loop or any other join path issue.
Contexts may be created using detect contexts feature or manually. They are generally created based on logical calculation and business requirements, hence the detect context method is not very effective. To manually create a context Go to Insert à Context, give the context name and select the joins that should be present in the context. For a universe, contexts should be created in a way that all joins(except shortcut joins) fall in at least one context.

[Related Article: SQL Server Tutorial]

Q4): What is a chasm trap? How can it be solved?

Ans: In a dimensional schema-based universe, we may have one dimension table joined with two fact tables such that both of them are one-to-many joins(F >- D - A chasm trap can be solved using 2 methods:
a) In the universe SQL parameters, the option, generate multiple queries for each measure needs to be selected. This will generate separate SQL statements for each measure and give the correct results. However, this method would not work, if a dimension (for example date) occurs multiple times in the result set due to a chasm trap.
b) A better approach is to put the two joins in two different contexts. This will generate two synchronized queries, thus solving the problem.

Q5): What is a fan trap? How can it be solved?

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Ans: In a universe structure, we may have 3 tables joined in such a way that, the 1st table has a one to many join with the 2nd table, which in turn has a one to many join with the 3rd table(A -< B -< C). In such a scenario, if a measure is present in the 2nd table and it is dragged along with any dimension from the 3rd table, the value of the measure will be inflated. Such a condition is known as a fan trap.
A fan trap is solved by creating an alias of the 2nd table and defining contexts such that, the normal table is joined only with the first table, while the alias is joined with both the 1st and the 3rd table. We would take the 2nd table’s measure only from the normal table and other dimensions of the 2nd table from the alias table.

Q6): Should we encounter fan traps in a data warehouse scenario? If so, then how?

Ans: If a data warehouse is based on the Kimball model, it is a dimensional schema. In a universe built on that DW, for a fan trap to occur in such a schema, we require a direct join between two fact tables, which is against the principles of dimensional modeling.
On the other hand in a data warehouse based on the Inmon model, it is a normalized schema. Though in such a case, universes are generally designed on Data Marts, which are dimensional schemas (where fan traps should not occur). However, if a universe is built on the DW (for the purpose of operational reporting), then a fan trap can occur in that universe

Q7): What is aggregate awareness? What is its advantage?

Ans: The aggregate awareness function is used in scenarios where we have the same fact tables in different grains. Using this function we can define only one object for the measures in the fact tables as
@aggregate_aware(highest_level,lower level)
We also need to define dimensions for associated granularities and define their incompatibilities with the corresponding facts through the aggregate navigation. This is accessed through Tools -> Aggregate Navigation
The advantage is that in a Webi or Deski report when one drags the measured object with the dimension object of a particular granularity, the measure column from the Fact table of the corresponding granularity is selected in the BO default Query. If we did not use aggregate awareness, we would need to define separate objects for each of the fact tables which would be difficult to understand from a user’s point of view.

Q8): What are the 2 different approaches to implementing aggregate awareness? Which one is better in terms of performance?

Ans: The 2 approaches are as follows:
1. Aggregate tables are built in the database, which contains the dimension fields(not foreign keys) along with the aggregated measures. In the universe they are present as standalone tables, i.e they are not joined with any dimensions. The aggregate aware function is used to define both the dimensions and measures of such tables.
2. No aggregate tables are built at the database level. They contain the normal fact table at different granularities. In the universe, aggregate awareness is used only to define the measures and aggregate incompatibility is set accordingly.
The first approach is better in terms of performance since for the higher levels of aggregation, all the information is obtained for a single table. However, large-scale implementation of this approach in a dimensional schema is difficult. In most BI projects, the second approach is preferred.

Q9): What is a derived table? What is its utility?

Ans: A derived table is a table created in the universe using an SQL Query from the database level. The columns selected in the query become the columns of the derived table. A derived table can be used for complex calculations, which are difficult to achieve at the report level. Such calculations are done at the query level itself.
Another use of derived tables can be to access tables from a different schema through a blink.

Q10): How is a derived table different from a view? Which one is a preferred solution?

Ans: A derived table is present only at the universe level, while a view is created at the database level. Generally, views are preferred since in its case the onus of calculation remains on the database and it does not load the BO server. However, in cases where developers do not have access to the database, a derived table is the only solution.

Q11): How can we access one derived table from another?

Ans: We can access one derived table from another using the function @derived_table. The syntax is:
@derived_table(Derived Table Name)

Q12): What is Index Awareness? How is it implemented?

Ans: Index awareness is a property of the universe, by means of which values in the filter conditions of the queries/data providers built from the universe, are substituted by their corresponding indexes or surrogate keys. Generally, the values in the filter condition come from a dimension table (like country, etc) and we require a join with the fact table to get this value.
However, if index awareness is implemented, this join is eliminated and the query filter takes the equivalent index value from the fact table itself.
To implement index awareness, one needs to identify the dimension fields which are to be used in the query filter. In the Edit Properties of the object, we get a Keys tab. In this tab, the source primary key of the table from which the object is derived needs to be defined as the primary key, and the database columns for all foreign key relationships with the other tables also need to be defined here. Once this is done for all required dimensions, the universe will become index aware.

Q13): How can we use index awareness in the universe prompt?

Ans: An extended prompt syntax is available since BO 3.1. It is as follows
@Prompt( ’message’, ‘type’, [lov],
mono/multi,
free/constrained/primary_key,
persistent/not_persistent,
{‘default value’:’default key’} )
If the indexes for the dimension object is defined in the universe and we define the prompt condition on the object with the clause ‘primary key’ in place of free or constrained, then the filter condition will convert the prompt values entered to their corresponding indexes and eliminate the join with the dimension table.

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