100+ Free SAP HANA Data Engineer Practice Questions

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Question 1

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What is the primary advantage of SAP HANA's columnar storage over row-based storage for analytical workloads?

Columnar storage improves transactional insert performance

Columnar storage enables faster aggregation because only relevant columns are read from memory

Columnar storage eliminates the need for indexing entirely

Columnar storage uses less disk space for all data types

to track

2026 Statistics

Key Facts: SAP HANA Data Engineer Exam

65%

Passing Score

SAP

Questions

SAP

180 min

Exam Duration

SAP

$562

Exam Fee

SAP

60-100 hrs

Study Time

Recommended

The C_HAMOD exam has 80 questions in 180 minutes, requiring 65% to pass. It covers SAP HANA modeling (Calculation Views, Attribute Views, Analytic Views), SQLScript, data provisioning (SDA/SDI), performance optimization, authorization, input parameters, hierarchies, table functions, and AFL.

Sample SAP HANA Data Engineer Practice Questions

Try these sample questions to test your SAP HANA Data Engineer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1What is the primary advantage of SAP HANA's columnar storage over row-based storage for analytical workloads?

A.Columnar storage improves transactional insert performance

B.Columnar storage enables faster aggregation because only relevant columns are read from memory

C.Columnar storage eliminates the need for indexing entirely

D.Columnar storage uses less disk space for all data types

Explanation: Columnar storage excels at analytical queries because it reads only the columns needed for a query, rather than entire rows. This dramatically reduces I/O for aggregation, filtering, and projection operations on large datasets. Row-based storage is still preferred for OLTP workloads with frequent single-row inserts and updates.

2Which SAP HANA view type is the most versatile and recommended by SAP for new modeling projects?

A.Procedure View

B.Calculation View

C.Attribute View

D.Analytic View

Explanation: Calculation Views are the most versatile view type in SAP HANA and are recommended by SAP for all new modeling projects. They can replicate the functionality of Attribute Views and Analytic Views while also supporting complex logic like unions, rank nodes, and scripted calculations. SAP has deprecated Attribute Views and Analytic Views in newer HANA versions.

3In a graphical Calculation View, what does the Aggregation node do?

A.It filters rows based on a WHERE condition

B.It creates a union of multiple data sources

C.It groups data by specified dimensions and applies aggregate functions to measures

D.It joins two data sources on a common key

Explanation: The Aggregation node in a graphical Calculation View groups data by the selected dimension columns and applies aggregate functions (SUM, COUNT, MIN, MAX, AVG) to measure columns. It is automatically added as the top node in Calculation Views of type 'Cube' to ensure proper aggregation behavior when the view is consumed.

4What is the difference between a Calculation View of type 'Cube' and type 'Dimension'?

A.Cube views contain measures and dimensions with an Aggregation node; Dimension views contain only attributes without measures

B.Cube views are used for real-time data; Dimension views are used for historical data

C.Cube views can only contain measures; Dimension views can only contain attributes

D.There is no functional difference; the types are interchangeable

Explanation: A Calculation View of type 'Cube' contains both measures (numeric values for aggregation) and dimensions (attributes for grouping), and automatically includes an Aggregation node at the top. A 'Dimension' type contains only attribute columns without measures and has a Projection node at the top instead. Dimension views are typically used as master data sources or joined into Cube views via star joins.

5Which join type in a Calculation View returns only matching rows from both data sources?

A.Right Outer Join

B.Inner Join

C.Full Outer Join

D.Left Outer Join

Explanation: An Inner Join returns only the rows where the join condition is satisfied in both the left and right data sources. Left Outer Join returns all rows from the left source plus matching rows from the right. Right Outer Join returns all rows from the right source plus matching rows from the left. Full Outer Join returns all rows from both sources.

6What is the purpose of a Star Join in a Calculation View?

A.To join a central fact data source with multiple Dimension-type Calculation Views

B.To perform a cross join between all available tables

C.To create a recursive hierarchy from a parent-child table

D.To join two fact tables together on a shared key

Explanation: A Star Join in SAP HANA connects a central fact data source (containing measures) with multiple Dimension-type Calculation Views (containing master data attributes). This follows the star schema modeling pattern where dimension tables surround a central fact table. Star Joins are configured in Cube-type Calculation Views and enable efficient analytical queries.

7In SAP HANA, what is the purpose of an Input Parameter in a Calculation View?

A.To specify the join condition between two data sources

B.To allow users to pass values at runtime that can be used in filters, calculated columns, or expressions

C.To define the default aggregation type for measures

D.To configure the data provisioning schedule

Explanation: Input Parameters allow consumers of a Calculation View to pass values at query runtime. These values can be used in filter expressions, calculated column formulas, or as parameters in SQLScript procedures. They enable dynamic behavior such as currency conversion, date filtering, or parameterized calculations without creating separate views for each scenario.

8What is the difference between an Input Parameter and a Variable in a Calculation View?

A.Input Parameters can be used in expressions and calculations; Variables are used to filter attribute values and are mapped to specific columns

B.There is no difference; they are synonyms in SAP HANA modeling

C.Input Parameters are used for measures; Variables are used for dimensions

D.Variables support multiple values; Input Parameters support only single values

Explanation: Input Parameters are general-purpose parameters used in formulas, expressions, and SQLScript logic within the view. Variables are specifically mapped to a column and used to filter attribute values, supporting features like value help (dropdown lists) for the end user. Variables can support single values, multiple values, and ranges, making them ideal for dimension filtering.

9Which hierarchy type in SAP HANA uses explicit levels such as Country > Region > City?

A.Parent-Child Hierarchy

B.Temporal Hierarchy

C.Level Hierarchy

D.Network Hierarchy

Explanation: A Level Hierarchy defines a fixed number of explicit levels with a predetermined depth, such as Country > Region > City. Each level corresponds to a specific attribute column. In contrast, a Parent-Child Hierarchy uses a self-referencing relationship (parent column and child column in the same table) and can have variable depth, making it suitable for organizational structures.

10What is a Rank node used for in a graphical Calculation View?

A.To rank hierarchy levels from highest to lowest

B.To assign priority to join conditions in multi-join scenarios

C.To sort the output alphabetically by a dimension column

D.To return the top-N or bottom-N records based on a specified measure and partition

Explanation: The Rank node in a graphical Calculation View returns the top-N or bottom-N records based on a sort column (typically a measure) within optional partitions. For example, it can find the top 10 customers by revenue in each region. The Rank node supports ordering direction (ascending/descending) and threshold values to limit the result set.

About the SAP HANA Data Engineer Exam

The SAP Certified Associate — SAP HANA Data Engineering (C_HAMOD) exam validates your skills in SAP HANA data modeling, SQLScript development, data provisioning with Smart Data Access and Smart Data Integration, performance optimization, and security concepts. It covers Attribute Views, Analytic Views, Calculation Views, table functions, input parameters, hierarchies, and the Application Function Library (AFL).

Questions

80 scored questions

Time Limit

180 minutes

Passing Score

65%

Exam Fee

$562 (SAP (Pearson VUE proctored))

SAP HANA Data Engineer Exam Content Outline

25%

SAP HANA Modeling

Attribute Views, Analytic Views, Calculation Views (graphical and scripted), joins, unions, projections, and aggregation nodes

20%

SQLScript Development

Stored procedures, user-defined functions, table functions, imperative and declarative logic, cursors, and exception handling

20%

Data Provisioning

Smart Data Access (SDA), Smart Data Integration (SDI), real-time replication, remote sources, virtual tables, and data provisioning agents

15%

Performance Optimization

Execution plans, partitioning, column vs row store, query optimization, memory management, and indexing strategies

10%

Authorization & Security

Analytic privileges, package privileges, schema-level security, SQL privileges, and role-based access in modeling

10%

Advanced Features

Input parameters, variables, hierarchies (level and parent-child), AFL (PAL, BFL), and data tiering (warm/cold storage)

How to Pass the SAP HANA Data Engineer Exam

What You Need to Know

Passing score: 65%
Exam length: 80 questions
Time limit: 180 minutes
Exam fee: $562

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

SAP HANA Data Engineer Study Tips from Top Performers

1Master Calculation Views: understand graphical vs scripted views, join types, union nodes, aggregation, and rank nodes

2Practice writing SQLScript stored procedures and table functions with both imperative and declarative logic

3Know Smart Data Integration (SDI) architecture: DP Agent, adapters, flowgraphs, and real-time replication

4Understand performance optimization: execution plans, column store, partitioning, and memory consumption analysis

5Study analytic privileges for row-level security and how input parameters/variables filter data at runtime

Frequently Asked Questions

What is the SAP HANA Data Engineering (C_HAMOD) exam pass rate?

SAP does not publicly disclose pass rates. The exam has 80 questions in 180 minutes, requiring 65% to pass. Questions emphasize practical SAP HANA modeling and data provisioning skills.

What SAP HANA topics should I focus on most?

Key areas: Calculation Views (graphical and scripted), SQLScript procedures and table functions, Smart Data Integration for data provisioning, and performance optimization techniques like partitioning and execution plan analysis.

Do I need hands-on SAP HANA experience for C_HAMOD?

Yes, hands-on experience is strongly recommended. The exam tests practical skills in creating HANA models, writing SQLScript, configuring data provisioning, and optimizing query performance in SAP HANA Studio or Business Application Studio.

How long should I study for the C_HAMOD exam?

Most candidates study 6-10 weeks, investing 60-100 hours. Use SAP Learning Hub courses (HA300, HA400) and hands-on practice with SAP HANA Cloud trial as primary study materials.