C_HAMOD_2404 Exam Dumps - SAP Certified Associate Questions and Answers

Shared hierarchies in SAP HANA offer the following functionalities:

Provide reusable hierarchies for drilldown in a CUBE with star join:Shared hierarchies can be defined once and reused across multiple calculation views, particularly in CUBE views with star joins. This promotes consistency and efficiency in hierarchical data representation and drilldown capabilities.

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Enable SQL SELECT statements to access hierarchies:Shared hierarchies are accessible via SQL, allowing for direct querying and manipulation using standard SQL commands. This facilitates integration with various applications and reporting tools that utilize SQL for data access.

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Options B and C are not correct:

Access hierarchies created in external schemas:Shared hierarchies are typically managed within the SAP HANA environment and may not directly access hierarchies from external schemas without additional configuration.

Provide reusable hierarchies for drilldown in a CUBE without star join:While shared hierarchies can be used in various calculation views, their primary integration and reuse are optimized for CUBE views with star joins.

Unfolding is a query optimization technique where parts of a calculation view are translated into SQL. Consequences include:

Count-distinct results are incorrect (B):The unfolding process may not preserve distinct counting logic correctly, potentially leading to inaccuracies.

SQL compilation time increases (C):The complexity of unfolded SQL queries can lead to longer compilation times due to the intricate relationships and data structures involved.

Option A is incorrect because unfolding does not relate to static caching, and D is partially correct but not as significant as B and C.

For a calculation view of typeCUBE, the default top view node is theAggregationnode. This node summarizes data based on specified grouping and performs aggregations, making it suitable for analytical purposes and star schemas.

Table partitioning in SAP HANA is used to enhance performance and scalability. The main reasons are:

B. To overcome the 2 billion record limit: Partitioning allows the table to handle more than the 2 billion records that are the limit for non-partitioned tables.

D. To improve query performance when accessing large column tables: Partitioning splits the table into manageable parts, allowing parallel processing and improving query performance for large datasets.