Free C_HAMOD_2601 Exam Questions: Data Engineer - SAP HANA System Tasks [2026]

Step-by-Step Execution:

1. Select the Union node combining your data sources.
2. Add a new target column (e.g., AMOUNT_TYPE) to the union output.
3. Right-click the newly created target column and select Manage Mappings.
4. Enter a distinct constant value for each underlying data source to explicitly tag them.
5. Deploy the view and use the constant column in your query filter to trigger pruning.

Example Values:

• Target Column: AMOUNT_TYPE
• Constant for Source 1: 'Actual'
• Constant for Source 2: 'Plan'

Expected Result: When a reporting tool issues a query with a filter expression specifically requesting AMOUNT_TYPE = 'Actual', the SQL optimizer ignores the 'Plan' data source, saving processing time and resources.

Quick Mistakes:

• Forgetting to explicitly include the constant column in the WHERE clause of the calling query, meaning no pruning will occur and both sources will be processed.
• Confusing explicit constant pruning inside the calculation view with implicit pruning, which requires creating a separate Pruning Configuration Table.

Exam Insight:

• Tests knowledge of performance optimization techniques within calculation views.
• Essential for understanding how the SQL optimizer evaluates union nodes and how explicit tagging restricts data source access at runtime.

Step-by-Step Execution:

1. Open an SQL console connected to your target SAP HANA database.
2. Write the CREATE VIRTUAL TABLE statement.
3. Specify your target local schema and the desired virtual table name.
4. Append the AT keyword followed by the remote source, remote database, remote schema, and remote object names separated by dots.
5. Enclose identifiers in double quotes to preserve case sensitivity.
6. Execute the SQL statement to provision the catalog object.

Example Values:

• Remote Source: EXT_HR_SYS
• Remote Database:
• SQL Syntax: CREATE VIRTUAL TABLE "HR_SCHEMA"."VT_EMPLOYEES" AT "EXT_HR_SYS".""."PUBLIC"."EMPLOYEES";

Expected Result: A new virtual table is successfully created in the local schema. It stores only metadata, allowing local users to run SQL queries that fetch data dynamically from the remote system.

Quick Mistakes:

• Forgetting to enclose the value in double quotes when the remote database or remote schema is not applicable for the adapter type.
• Omitting the target schema name if the session user is not meant to be the default schema owner.
• Running the statement without having the CREATE VIRTUAL TABLE privilege on the specified remote source.

Exam Insight:

• Tests the strict SQL syntax required for creating virtual tables and understanding that the keyword must always be quoted when skipping hierarchical elements like the database or schema name.

Step-by-Step Execution:

1. Open the calculation view and navigate to the Parameters tab in the Semantics node.
2. Open the Input Parameter/Variables Mapping feature by launching the Manage Mapping dialog.
3. Select the mapping type Views for value help for variables/input parameters from the dropdown list.
4. Locate the variable used for the value help view on the left side and the variable defined in the current calculation view on the right side.
5. Drag a line between the left and right side to map them, or click auto-map if the names match exactly.

Example Values:

• Mapping Type: Views for value help for variables/input parameters
• Source Variable (Left): IP_PRODUCT_GROUP (from external Value Help view)
• Target Variable (Right): IP_PRODUCT_GROUP (in main Calculation View)

Expected Result: At runtime, when the user selects a Product Group, this value is passed through the mapping to the value help calculation view, filtering the subsequent list of Products to show only those belonging to the selected group.

Quick Mistakes:

• Selecting "Data Sources" as the mapping type instead of "Views for value help for variables/input parameters".
• Attempting to cross-map an input parameter to a variable (the system strictly requires mapping variables to variables and input parameters to input parameters).
• Leaving the value help pointing to the main calculation view, which forces the system to scan the entire dataset just to populate the prompt list.

Exam Insight:

• Tests the understanding of parameter mapping types and dynamic value help configuration.
• Essential for knowing how to build user-friendly, high-performing cascading prompts without competing with the main calculation view for data processing resources.

Step-by-Step Execution:

1. Navigate to the src folder of your SAP HANA Database Module in the Explorer view.
2. Create a new file and assign it the .hdbsynonym extension.
3. Open the file in the Synonym Editor or the text editor.
4. Enter the desired name for the synonym to be used inside your HDI container.
5. Specify the actual object name and the external schema name where the target object resides.
6. Deploy the synonym file to generate the runtime object.

Example Values:

• Name of Synonym: EXT_CUSTOMER_MASTER
• Object: CUSTOMERS
• Schema: ERP_SCHEMA

Expected Result: A runtime synonym is successfully deployed in the HDI container, allowing your calculation views to select data from the external schema table as if it were a local object.

Quick Mistakes:

• Attempting to use a synonym before deploying the corresponding .hdbgrants file, which provides the necessary privileges to the object owner and application user.
• Providing the wrong target object name or schema name, which causes deployment to fail when the HDI deployer attempts to resolve the reference.
• Accessing an external table in a calculation view using direct SQL schema references (e.g., SCHEMA.TABLE), which is strictly blocked in HDI.

Exam Insight:

• Tests the understanding of HDI container isolation and cross-schema data access.
• Essential for knowing the required architectural components (User-Provided Services, HDBGRANTS, and Synonyms) to build models over external data.

Step-by-Step Execution:

1. Add a Rank node to your calculation view and connect an aggregated sales data source to it.
2. Navigate to the Mapping tab, define the Sort Column, and set the sort direction to Descending.
3. In the Properties pane, set the Aggregation Function to Row and the Result Set Direction to Top.
4. Assign the Logical Partition column to reset the ranking counter per region.
5. Specify the Target Value to establish the cutoff for the rank result set.

Example Values:

• Aggregation Function: Row
• Logical Partition: REGION
• Sort Column: GROSS_AMOUNT (Descending)
• Target Value: 5

Expected Result: The calculation view outputs exactly 5 rows per region, representing the 5 products with the highest gross amount, effectively filtering out all lower-ranked products.

Quick Mistakes:

• Using raw, unaggregated transactional data as the source for the Rank node (the Rank node does not aggregate data itself; it only sorts and numbers existing rows).
• Forgetting to define a Logical Partition, which would result in the query returning the top 5 products globally instead of the top 5 per region.
• Selecting an incorrect Result Set Direction (e.g., choosing Bottom instead of Top, which would return the worst-selling products).

Exam Insight:

• Tests advanced data slicing and window function capabilities in graphical calculation views.
• It is essential to understand the difference between the Row, Rank, and Dense Rank aggregation functions, and how logical partitioning dictates the grouping for the rank calculation.