SAP HANA Data Engineer (C_HAMOD) Practice Exam
Prepare for the SAP Certified Associate – Data Engineer – SAP HANA exam across all seven weighted areas — building calculation views, configuring modeling functions, optimizing performance, managing models, provisioning data, SQL and SQLScript, and securing models — with objective-mapped questions, Learn-mode feedback, and a full timed Exam mode.
Start 24-hour free trial →Calculation views are the exam — and security is a published area
Building calculation views carries 21–30%, the largest single area, and the questions go well past “what is a calculation view” into node types, join behaviour, hierarchies, and the semantics node. Study material that treats calculation views as one concept rather than a deep subject is under-preparing you for a third of the paper.
Two areas that summaries routinely skip are published and tested: Securing models (analytic privileges) and Managing and administering models. Meanwhile SQL and SQLScript carries 10% or less — less than most candidates expect. Confirm the current outline on the official certification page before scheduling.
SAP HANA Data Engineer exam at a glance
- Vendor
- SAP
- Exam code
- C_HAMOD_2404
- Certification
- SAP Certified Associate – Data Engineer – SAP HANA
- Level
- Associate (data engineer)
- Scope
- Data provisioning and data modelling skills in SAP HANA Cloud and SAP HANA
- Blueprint
- Seven weighted areas, led by Building calculation views at 21–30%
- Format
- 80 questions; multi-answer items state how many answers are correct; no true/false questions
- Duration
- 180 minutes (3 hours)
- Cut score
- 66% — roughly 53 of 80 correct
- Language
- English
- Booking
- Available as a cloud certification through the Certification Hub, which allows up to six exam attempts of your choice in one year
- Note
- Exams may contain unscored items being trialled for upcoming releases
Sources: SAP Learning — official certification page · SAP Learning — Developing data models with SAP HANA Cloud. SAP notes its topic list is a guide, not a guarantee, and reserves the right to update content, items, and weighting at any time.
About the SAP HANA Data Engineer (C_HAMOD) certification
The SAP Certified Associate – Data Engineer – SAP HANA credential verifies that you possess the data provisioning and data modelling skills required for the SAP HANA data engineer role, in SAP HANA Cloud and SAP HANA. It is built on the knowledge from SAP’s HANA data provisioning and data modeling learning journeys, and it is a modeling exam first and foremost.
Two things about the weighting are worth knowing before you plan. Building calculation views is 21–30% — the largest area by a clear margin — and it is tested in depth: node types, join semantics including which joins support a temporal condition, shared hierarchies, orphan-node handling, and what the semantics node exposes. And SQL and SQLScript carries 10% or less, which surprises candidates who assume a HANA developer exam is mostly code. Two areas that never appear in informal summaries — Securing models and Managing and administering models — are published areas worth up to 30% between them. For where this sits among data tracks, see the IT certifications guide in our Learning Hub.
SAP HANA Data Engineer exam topic areas and weights
SAP publishes seven weighted areas. Note that SAP states the topic list is a guide whose accuracy does not constitute a legitimate claim, and it reserves the right to update content, items, and weighting at any time — treat the bands as an indication of emphasis.
Detailed knowledge of building a data model using all types of calculation views and nodes: CUBE and DIMENSION views, star joins, join types and their semantics, union, aggregation, rank and projection nodes, and the semantics node that exposes the result.
Common modeling functions: filters and input parameters, variables, calculated and restricted columns, currency conversion, and hierarchies — including shared hierarchies and how orphan nodes are handled.
Best practices for building high-performance models and the tools to monitor them: pushing work down, avoiding unnecessary materialization, partitioning, and reading what the plan visualizer and explain plan actually tell you.
Setting up the modeling environment and using the tools that manage models: the development workspace, project and repository structure, deployment, and lifecycle management across environments. A published area that informal summaries omit.
Getting data in: Smart Data Integration for real-time and batch replication into HANA tables, Smart Data Access for virtual federated access to remote sources without copies, and choosing between them for a given requirement.
Applying SQL and SQLScript in HANA modeling: procedures and table functions, when SQL belongs inside a calculation view, and what scripted logic buys you over graphical modeling. Smaller than most candidates assume.
Preventing unauthorised access to data models: SELECT privileges versus analytic privileges, where each must be granted across a CUBE and its DIMENSION views, and row-level restriction by attribute value. A published area, and rarely revised.
Topic areas and weight bands reflect SAP’s published outline for this exam version. SAP notes the list’s accuracy does not constitute a legitimate claim and reserves the right to update content, items, and weighting at any time. Source: SAP Learning — official certification page. Confirm the current outline before scheduling.
Who this exam is for
SAP aims this credential at people who model data in HANA rather than administer the database:
- SAP HANA modelers and data engineers building calculation views for analytics and applications.
- BW and analytics practitioners working with HANA-native modeling alongside or beneath their warehouse.
- Developers on HANA-based applications who own the data layer their services read from.
- Data professionals moving to SAP from other platforms who need the HANA modeling vocabulary formalized.
Professionals holding this certification commonly work as SAP HANA data engineers, HANA modelers, and data warehouse developers. Adjacent credentials cover the neighbouring layers: SAP HANA database administration for operating the platform rather than modeling on it, SAP BW/4HANA data engineer for the managed warehouse above it, SAP Analytics Cloud for the consumption layer, and SAP CAP developer for applications built on HANA. For the roles this certification supports, see the data engineer career path.
What this HANA Data Engineer practice exam delivers
Learn mode
Get the correct answer, the explanation, and why each other choice is wrong — immediately after each question. Best for join semantics and analytic privileges, where the near-miss options are genuinely close.
Exam mode
80 questions, 180-minute timer — the real C_HAMOD format at a 66% cut score, which means about 53 correct out of 80.
Weighted to the real blueprint
Practice concentrated on calculation views at 21–30%, with genuine coverage of securing and administering models — not spread evenly or skewed toward SQLScript.
Score by topic area
Results break down across all seven published areas, so practice tells you exactly which to revisit before you book.
Sample HANA Data Engineer practice questions
Ten free questions across the seven published topic areas, with full explanations and source links to SAP resources. The complete bank is available with the 24-hour trial.
A data engineer builds an analytical model combining a fact table with several dimensions and exposing measures for reporting. Which calculation view type and node fit?
- A DIMENSION view with a projection node, since dimensions carry the attributes
- A CUBE view with a star join node connecting the fact entity to DIMENSION views
- An ABAP CDS view consumed by HANA
- A BW/4HANA aDSO exposed to HANA
Show answer & explanation
Correct: B — A CUBE view with a star join node. CUBE views expose measures for analytical consumption, and the star join node is the optimized pattern for connecting a central fact entity to its DIMENSION views.
Why not the others: a DIMENSION view (A) models the attributes a CUBE joins to rather than the analytical model itself; ABAP CDS (C) belongs to the ABAP application layer; and a BW aDSO (D) is a BW object, not HANA-native modeling.
Source: SAP Learning — Developing data models with SAP HANA Cloud → Further reading: PowerKram — SAP BW/4HANA →A user must query a CUBE calculation view but see only United States data, where the country attribute comes from a joined DIMENSION view. Which privileges are required?
- A SELECT privilege and an analytic privilege on the CUBE view only
- A SELECT privilege on the CUBE view, plus an analytic privilege restricting country on the DIMENSION view
- A SELECT privilege and an analytic privilege on the DIMENSION view only
- SELECT privileges and analytic privileges on both views
Show answer & explanation
Correct: B — SELECT on the CUBE, analytic privilege on the DIMENSION. The user queries the CUBE, so that is where SELECT is needed; the restriction applies where the attribute lives, which is the DIMENSION view supplying country.
Why not the others: privileges on the CUBE alone (A) cannot restrict on an attribute the DIMENSION owns; the DIMENSION alone (C) leaves no SELECT on the view actually queried; and both everywhere (D) grants more than required, which is the habit least-privilege exists to break.
Source: SAP Learning — C_HAMOD certification → Further reading: PowerKram — SAP HANA DBA →Which join type in a calculation view supports a temporal condition?
- Referential join
- Text join
- Inner join
- Left outer join
Show answer & explanation
Correct: C — Inner join. A temporal condition restricts the join to records valid at a point in time, and it is supported on the inner join — a detail that only comes from having built one.
Why not the others: a referential join (A) assumes referential integrity and may be pruned entirely; a text join (B) is for language-dependent descriptions; and a left outer join (D) preserves unmatched rows, which is at odds with a temporal restriction.
Source: SAP Learning — Developing data models with SAP HANA Cloud →A hierarchy contains nodes whose parent does not exist in the data. What options handle these orphan nodes? (SAP states two answers are correct; pick the pairing that is genuinely supported.)
- Delete the orphan nodes, and rebuild the hierarchy from the remaining data
- Convert the hierarchy to a flat list, and expose it as an attribute
- Ignore them silently, and let the reporting tool decide
- Generate additional root nodes, or assign them to a level below the root
Show answer & explanation
Correct: D — Generate additional root nodes, or assign them to a level below the root. Both keep the orphans in the hierarchy where a user can still find them, rather than dropping data because its parent is missing.
Why not the others: deleting orphans (A) discards real data; flattening the hierarchy (B) abandons the structure to avoid the problem; and leaving it to the reporting tool (C) makes the behaviour unpredictable per client.
Source: SAP Learning — Developing data models with SAP HANA Cloud →A team must query a remote Oracle database from HANA without persisting a copy of the data. Which capability applies?
- Smart Data Integration, replicating the tables into HANA on a schedule
- A flat-file export and import routine
- Smart Data Access, providing virtual federated access to the remote source
- Rebuilding the Oracle tables natively in HANA
Show answer & explanation
Correct: C — Smart Data Access. SDA exposes remote tables virtually so queries reach the source at runtime, which is exactly the “without persisting a copy” requirement.
Why not the others: SDI (A) is the replication answer and creates the copy the question excludes; flat-file export and import (B) is manual and stale on arrival; and rebuilding the tables (D) duplicates the data permanently.
Source: SAP — SAP HANA Cloud → Further reading: PowerKram — SAP Business Data Cloud →A source system’s data must land physically in HANA tables, kept current in near real time. Which capability applies?
- Smart Data Integration, providing real-time and batch replication into HANA tables
- Smart Data Access, federating the source at query time
- A nightly CSV import job
- Manual data entry by the operations team
Show answer & explanation
Correct: A — Smart Data Integration. SDI is the replication capability, moving data physically into HANA tables in real time or in batches — the counterpart to SDA’s virtual access.
Why not the others: SDA (B) never persists the data, which the requirement demands; a nightly CSV job (C) fails the near-real-time condition; and manual entry (D) is not a provisioning mechanism.
Source: SAP — SAP HANA Cloud →Why would a modeler introduce SQL into a calculation view?
- To implement custom logic that graphical modeling cannot express
- To fine-tune performance, since SQL always outperforms graphical nodes
- To enable write capabilities in the view
- Because graphical modeling is deprecated
Show answer & explanation
Correct: A — To implement custom logic. SQL earns its place where the requirement outruns what the graphical nodes express; that is the reason, and the exam is precise about it.
Why not the others: assuming SQL always performs better (B) is false, since graphical models are optimized by the engine and hand-written SQL can block that; write capability (C) is not what a calculation view provides; and graphical modeling (D) is very much not deprecated — it is the largest area on the exam.
Source: SAP Learning — Developing data models with SAP HANA Cloud → Further reading: PowerKram — SAP CAP developer →A calculation view over a billion-row fact table performs poorly. What is the soundest first approach?
- Convert the underlying tables from column store to row store
- Remove indexes so the engine has fewer structures to maintain
- Materialize the whole model into a persisted table each night
- Examine the execution plan to see where work happens, then push filters and aggregation down and consider partitioning
Show answer & explanation
Correct: D — Read the plan, then push work down and consider partitioning. SAP’s stated skill is following best practices and using the provided tools to monitor performance — measure first, then reduce the rows moving up the model.
Why not the others: row store (A) is the wrong engine for analytical scans; removing indexes (B) removes the structures that help; and nightly materialization (C) trades freshness for speed before you have even diagnosed the problem.
Source: SAP Learning — Developing data models with SAP HANA Cloud →Why would a modeler mark a column as hidden in the semantics node of a calculation view?
- To ensure the column is not exposed to the reporting tool while remaining available inside the model
- To delete the column from the underlying table
- To improve query performance by excluding the column from the column store
- To prevent other modelers from seeing the column in the editor
Show answer & explanation
Correct: A — Keep it out of the reporting tool while the model still uses it. Hiding is about the consumption surface: a column can drive a calculation or serve as a label without cluttering what a report author sees.
Why not the others: deleting from the table (B) confuses a view setting with a DDL change; a performance gain (C) misreads what hiding does; and hiding it from other modelers (D) mistakes a semantics setting for access control — that is what privileges are for.
Source: SAP Learning — Developing data models with SAP HANA Cloud →A modeling team must move a validated data model from development into test and then production, repeatably. What does SAP expect a data engineer to know here?
- Exporting the view definition and pasting it into each environment by hand
- Rebuilding the model in each environment from the design document
- Setting up the modeling environment and using the provided tools to manage and deploy models across environments
- Nothing — promotion between environments is purely a Basis responsibility
Show answer & explanation
Correct: C — Set up the modeling environment and use the provided tools to manage and deploy models. That is SAP’s stated skill for this area, and it is why managing and administering models is a published topic rather than an afterthought.
Why not the others: hand-pasting definitions (A) and rebuilding per environment (B) are exactly what managed deployment removes; and disowning it as a Basis job (D) contradicts a published area of the data engineer’s own exam.
Source: SAP Learning — C_HAMOD certification → Further reading: PowerKram — Data engineer career path →Keep going: Learning & Career resources
HANA modeling sits beneath the analytics and applications that read from it — and it opens onto data engineering and architecture roles. Two PowerKram hubs back this exam.
Deep dive: calculation views, the areas people skip, and study path
Calculation views are a third of the exam
At 21–30%, building calculation views is the largest area and the one that rewards depth rather than recognition. SAP’s stated skill is detailed knowledge of building a model using all types of calculation views and nodes — so know CUBE versus DIMENSION and when each is right, the star join node, and the behaviour of join types: which is pruned when unused, which handles language-dependent text, and which supports a temporal condition. Question pools bear this out, opening on shared hierarchies, the semantics node, and join details. If your preparation covers “calculation views exist and combine data,” you have covered the topic’s title. SAP Learning — Developing data models with SAP HANA Cloud →
Securing models is published, small, and reliably tested
At 10% or less it is easy to skip, and it is the area candidates most often meet cold. The distinction that matters: a SELECT privilege lets a user query a view, while an analytic privilege restricts which rows they see by attribute value. The subtle part is where each belongs. When a user queries a CUBE whose country attribute comes from a joined DIMENSION view, SELECT goes on the CUBE they query and the analytic privilege goes on the DIMENSION that owns the attribute — not both on both. Learn that pattern once and the security questions become straightforward. SAP’s official certification page →
SDI and SDA: the distinction the exam keeps asking
Provisioning is 11–20% and it turns on one clean distinction. Smart Data Integration replicates data physically into HANA tables, in real time or batch — you get a copy you control. Smart Data Access federates virtually — the data stays in the remote source and queries reach it at runtime. Scenario questions hand you a constraint (“without persisting a copy”, “must land in HANA tables”) and expect you to pick. Read the constraint before the options and the answer is usually decided already. SAP — SAP HANA Cloud →
Less SQLScript than you expect
Working with SQL and SQLScript is 10% or less, which regularly surprises developers who assume a HANA engineering exam is mostly code. The reason is that this is a modeling credential: graphical models are the primary artifact and the engine optimizes them, so SQL belongs where graphical modeling genuinely cannot express the logic. That is also the exam’s answer to why you would add SQL to a view — custom logic, not a blanket performance win. Budget your study accordingly rather than by comfort. SAP’s official certification page →
Realistic study path and booking
80 questions in 180 minutes at a 66% cut score means about 53 correct — over two minutes a question, comfortable for recall and tight for a model you have never built. Work SAP’s HANA Cloud data modeling course, which maps closely onto the blueprint, then use objective-mapped practice to find whether your gap is calculation-view depth or one of the small published areas. One practical note: this is available as a cloud certification through the Certification Hub, which gives you up to six exam attempts of your choice in one year — useful if you would rather sit it and learn the shape than wait until you feel certain. SAP HANA DBA on PowerKram →
Frequently asked questions
What are the exam topic areas and their weights?
Seven areas: Building calculation views (21–30%), Configuring modeling functions (11–20%), Optimizing the performance of models (11–20%), Managing and administering models (11–20%), Provisioning Data to SAP HANA (11–20%), Working with SQL and SQLScript in models (up to 10%), and Securing models (up to 10%).
How many questions is the exam, and what is the cut score?
80 questions in 180 minutes with a 66% cut score, in English — roughly 53 correct out of 80. Multi-answer questions state how many answers are correct. Note that SAP exams may contain unscored items being trialled for upcoming releases.
Is this exam mostly SQLScript?
No — that is the most common misconception. Working with SQL and SQLScript carries 10% or less, while building calculation views carries 21–30%. This is a modeling credential: graphical calculation views are the primary artifact, and SQL belongs where graphical modeling cannot express the logic.
What is the difference between SDI and SDA?
Smart Data Integration replicates data physically into SAP HANA tables, in real time or in batches. Smart Data Access provides virtual, federated access to remote sources without persisting a copy — queries reach the source at runtime. Scenario questions usually state a constraint that decides which one applies.
Does the exam cover SAP HANA Cloud or on-premise SAP HANA?
Both. SAP describes the certification as verifying data provisioning and data modelling skills in SAP HANA Cloud and SAP HANA for the data engineer role, based on the related learning journeys. Preparing only for one deployment leaves gaps in the other.
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