Pass the SAP Certified Associate C_HAMOD_2404 Questions and answers with ExamsMirror

The input parameters of calculation view B might not be proposed as source parameters because they are already mapped in another calculation view. SAP HANA restricts the re-mapping of input parameters that have already been assigned to maintain consistent logic and avoid conflicts. When input parameters are pre-mapped in one calculation view, they cannot be reused as source parameters elsewhere without unlinking the prior mapping.

A build failure with a namespace-related issue often occurs because:

B. The imported calculation view and its data source have different namespaces: Both must match for the view to reference the table correctly.

C. The namespace used in the calculation view to reference the table is different from the actual namespace in the table's identifier: The reference must exactly match the namespace in the table definition.

The issue is unrelated to sub-folder rules (A) or differing.hdinamespacefiles (D).

Avoid defining joins on calculated columns (A):Joins on calculated columns can significantly impact performance because they require calculations before the join operation. It is better to minimize such scenarios.

Include all data flow logic within one calculation view (B):Consolidating logic within a single calculation view reduces complexity and improves maintainability, avoiding unnecessary data movement across multiple views.

Options C and D are less optimal because defining filters or aggregations on calculated columns can increase the computational overhead.

To expose a hierarchy to SQL, the following conditions must be met:

B. The hierarchy must be a shared hierarchy: This ensures the hierarchy is reusable and can be consumed in other scenarios.

D. The hierarchy must be exposed by a CUBE calculation view with a star join: Only a cube view with a star join supports SQL exposure for hierarchies.

Local hierarchies and those defined in non-cube calculation views are not SQL-exposed.

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.

If the account number is not masked in calculation view A, it indicates that masking was not defined in calculation view A. Masking rules must be explicitly defined at every level where data is exposed, as they are not inherited from underlying views.

Anon-equi joinis used when the join condition cannot be represented by equality (=). Instead, the condition may involve other operators, such as<,>,BETWEEN, etc. This type of join is essential when relationships between datasets are based on ranges or non-matching values.

Partition large tables (B):Partitioning tables allows for parallel processing of data subsets, significantly improving query execution times.

Limit the number of stacked calculation views (C):Reducing layers of calculation views minimizes complexity and execution overhead, leading to better performance.

Options A and D are incorrect or less effective. Union pruning should be enabled to improve performance, and while aggregating data early can sometimes be beneficial, it is not universally optimal for performance tuning.

To create a star schema using a calculation view with measures based on columns from two transaction tables:

Combine the transaction tables using a join node in a CUBE calculation view:The join node merges the transaction tables into a unified fact table.

Use a star join node to join the DIMENSIONS to the resulting fact table:The star join node integrates the attributes from the DIMENSION views with the measures in the fact table, completing the star schema design.

This approach adheres to best practices for building efficient star schemas in SAP HANA.(Reference: SAP HANA Star Schema Modeling Guide)