SAP Analytics Cloud Data Analyst (C_SAC) Practice Exam
Prepare for the SAP Certified Associate – Data Analyst – SAP Analytics Cloud exam across all five weighted areas — story design, planning, data modeling and integration, connections and data preparation, and performance and security — with objective-mapped questions, Learn-mode feedback, and a full timed Exam mode.
Start 24-hour free trial →Planning is a third of this exam — not a footnote
SAP merged its separate Analytics and Planning certifications into this single C_SAC code, and the blueprint shows it: Planning carries 21–30%, level with Story Design and Data Modeling. Versions, data actions, value driver trees, and predictive planning are core material, not an appendix. Study material that treats SAC as a visualization tool with a planning add-on is preparing you for a third of the paper.
Format note: industry reporting indicates SAP is transitioning this certification line toward an AI role-play scenario format on newer releases, while the current release remains multiple-choice. The underlying knowledge is the same either way, but confirm the format on SAP Learning before booking.
SAP Analytics Cloud Data Analyst exam at a glance
- Vendor
- SAP
- Exam code
- C_SAC_2501
- Certification
- SAP Certified Associate – Data Analyst – SAP Analytics Cloud
- Level
- Associate (data analyst)
- Blueprint
- Five weighted areas; Story Design, Planning, and Data modeling all in the 21–30% band
- Format
- 60 questions, multiple-choice and multiple-response; multi-answer items state how many answers are correct
- Duration
- 120 minutes (2 hours)
- Cut score
- 70%
- Language
- English
- Validity
- 12 months; each successful Stay Current assessment extends it by a further 12 months
- Lineage
- SAP consolidated its previously separate SAC Analytics and Planning certifications into this single code — which is why planning carries full weight
- Reference training
- SAP’s Designing Stories in SAP Analytics Cloud and enterprise planning learning journeys
Sources: SAP Learning — Exploring SAP Analytics Cloud · SAP Learning — Designing stories in SAP Analytics Cloud. SAP reserves the right to update exam content, items, and weighting at any time; verify current details before scheduling.
About the SAP Analytics Cloud Data Analyst (C_SAC) certification
The SAP Certified Associate – Data Analyst – SAP Analytics Cloud credential verifies the fundamental and core knowledge required for the SAP Analytics Cloud data analyst profile — building stories, modeling and integrating data, running planning processes, and managing connections. SAP positions the outcome as participating as a member of a project team.
The single most useful fact about this exam is its history. SAP merged the previously separate SAC Analytics and Planning certifications into one code, and the blueprint reflects that merge: Story Design, Planning, and Data modeling each carry 21–30%. If your mental model of SAC is “dashboards, plus some planning features,” you are under-weighting a third of the exam. Expect versions, data actions, value driver trees, and predictive planning at the same depth as charts and calculated measures. The other reliable trap is live versus import, which decides not just how data arrives but where security is defined and what planning you can do. For where this sits among data tracks, see the IT certifications guide in our Learning Hub.
SAP Analytics Cloud exam topic areas and weights
SAP publishes five weighted areas. Three sit in the same 21–30% band and together carry the bulk of the paper — treat the bands as an indication of emphasis rather than an exact split.
Building stories and dashboards: chart types and when each fits, tables, geo maps, filters and input controls, linked analysis, responsive pages, and the design choices that make an insight land rather than merely appear.
The area candidates underestimate: planning models and versions, public versus private versions, data actions for allocation, copy and calculation, value driver trees, collaborative input forms, and predictive planning.
Models with measures and dimensions, hierarchies, calculated and restricted measures, currency conversion, blending data from multiple sources, datasets versus models, and Smart Predict and smart discovery for automated insight.
Live versus import connections and what each implies; connecting to SAP sources such as Datasphere, S/4HANA and BW/4HANA and to non-SAP sources; scheduling dataset refreshes; and cleansing and preparing data before it reaches a story.
Optimizing story and model performance and diagnosing what is slow; roles, teams, and data access controls; and the live-connection rule that catches people out — on a live connection, data security is defined in the source system.
Topic areas and weight bands reflect SAP’s published outline for this exam version. SAP reserves the right to update exam content, items, and weighting at any time. Source: SAP Learning — Exploring SAP Analytics Cloud. Confirm the current outline before scheduling.
Who this exam is for
SAP aims this credential at people who build in SAC rather than administer it:
- Business intelligence and data analysts building stories and models for business audiences.
- Finance and FP&A practitioners running budgeting and forecasting in SAC planning models — a third of this exam is their day job.
- SAP analytics consultants delivering SAC on projects and connecting it to Datasphere, BW/4HANA, or S/4HANA.
- Data professionals from other BI platforms who need the SAC model and the live-versus-import distinction formalized.
Professionals holding this certification commonly work as SAP analytics consultants, business intelligence analysts, data analysts, and planning analysts. Adjacent credentials cover the layers beneath: SAP HANA data engineer for the modeling layer SAC often reads from, SAP BW/4HANA data engineer for the managed warehouse, and SAP Business Data Cloud for the unified data foundation. For the roles this certification supports, see the data career paths in our Career Hub.
What this SAP Analytics Cloud practice exam delivers
Learn mode
Get the correct answer, the explanation, and why each other choice is wrong — immediately after each question. Best for live-versus-import scenarios, where the consequences are easy to state and easy to get backwards.
Exam mode
60 questions, 120-minute timer — the real C_SAC format at a 70% cut score. Two minutes a question, so pace matters.
Planning at full weight
Practice covering versions, data actions, value driver trees, and predictive planning — the 21–30% area most study material treats as an afterthought.
Score by topic area
Results break down across all five published areas, so practice tells you exactly which to revisit before you book.
Sample SAP Analytics Cloud practice questions
Ten free questions across the five published topic areas, with full explanations and source links to SAP resources. The complete bank is available with the 24-hour trial.
A model uses a live connection. Where is data security defined?
- In the source system
- In an SAP Analytics Cloud data access control
- In the SAP Analytics Cloud model
- In the SAP Analytics Cloud role
Show answer & explanation
Correct: A — In the source system. On a live connection the data never leaves the source, so the source system’s own authorizations decide what the user can see — SAC passes the query through and honours what comes back.
Why not the others: data access controls (B) and model-level security (C) apply to imported models where the data actually sits in SAC; and an SAC role (D) governs what a user can do in the application, not which rows they see.
Source: SAP Learning — Exploring SAP Analytics Cloud → Further reading: PowerKram — SAP HANA data engineer →A dataset extracts data from an SAP BW system, and the BW data changes regularly. How do you keep the dataset current?
- Create a new dataset each time the source changes
- Refresh the story that uses the dataset
- Schedule the dataset to update on a regular basis
- Manually reimport the data whenever it changes
Show answer & explanation
Correct: C — Schedule the dataset to update on a regular basis. An imported dataset holds a copy, so it needs refreshing — and scheduling is the mechanism that keeps it current without anyone remembering to act.
Why not the others: a new dataset each time (A) discards everything built on the old one; refreshing the story (B) re-renders what is already imported rather than fetching new data; and manual reimport (D) works but is the chore scheduling exists to remove.
Source: SAP Learning — Exploring SAP Analytics Cloud →A team must decide between a live and an import connection to SAP Datasphere. What is the key difference?
- Live connections are read-only in every respect and cannot be used for reporting
- On a live connection the data stays in the source and is queried in real time; an import connection copies data into an SAC model where it can be transformed, scheduled, and planned on
- Import connections query the source in real time; live connections copy the data nightly
- There is no functional difference — the choice is purely a licensing matter
Show answer & explanation
Correct: B — Live queries the source in real time; import copies into an SAC model. That one distinction drives the consequences the exam keeps testing: where security lives, whether you can transform the data, whether refreshes need scheduling, and what planning is possible.
Why not the others: live being unusable for reporting (A) is false — real-time reporting is its purpose; swapping the definitions (C) inverts both; and calling it a licensing matter (D) ignores real functional differences.
Source: SAP — Datasphere → Further reading: PowerKram — SAP Business Data Cloud →A finance team must spread an annual budget across months, copy actuals into a forecast version, and recalculate driver-based figures — repeatably. What should they use?
- Export to a spreadsheet, do the work there, and import the result
- A story filter applied to the planning model
- Manual entry into the input form for each cell
- Data actions, which run multi-step planning operations such as allocation, copy, and calculation on the model
Show answer & explanation
Correct: D — Data actions. They package allocation, copy, and calculation steps into a repeatable operation that runs against the planning model — which is exactly what “repeatably” is asking for.
Why not the others: a spreadsheet round trip (A) leaves the plan outside the system that governs it; a story filter (B) changes what is displayed, not the data; and manual entry (C) does not scale and cannot be repeated reliably.
Source: SAP Learning — Planning with SAP Analytics Cloud → Further reading: PowerKram — SAP BW/4HANA →A planner wants to model a scenario without anyone else seeing the numbers until it is ready. Which version type fits?
- A private version, visible only to its owner until published
- A public version, since all planning versions are shared by design
- A separate planning model built for the scenario
- A story bookmark saved to My Files
Show answer & explanation
Correct: A — A private version. Private versions let a planner work through a scenario in isolation and publish when ready; that publish step is what makes the numbers public.
Why not the others: a public version (B) is visible to others immediately, which the requirement excludes; a separate model (C) is heavy machinery for a scenario and fragments the data; and a bookmark (D) saves a story state, not plan data.
Source: SAP Learning — Planning with SAP Analytics Cloud →An analyst wants selecting a region in one chart to filter every other chart on the page. What should they configure?
- A separate story page per region
- A calculated measure that hard-codes the region
- Linked analysis, so a selection in one widget filters the others
- An export to spreadsheet where the user can filter themselves
Show answer & explanation
Correct: C — Linked analysis. It connects widgets so a selection propagates, which is what turns a page of charts into an interactive story rather than a set of static pictures.
Why not the others: a page per region (A) multiplies maintenance and still is not interactive; a hard-coded measure (B) fixes the value rather than letting the user drive it; and exporting (D) hands the analysis to the user’s spreadsheet.
Source: SAP Learning — Designing stories in SAP Analytics Cloud →What must an SAP Analytics Cloud data model contain to be functional?
- Hierarchies and variables
- Dimensions and measures
- Calculated measures and restricted measures
- A live connection and a planning version
Show answer & explanation
Correct: B — Dimensions and measures. Dimensions supply the qualitative context — time, geography, product — and measures the numbers analysed against it; without both there is nothing to analyse.
Why not the others: hierarchies and variables (A) and calculated or restricted measures (C) are useful additions built on top of the basics; and a live connection with a planning version (D) describes one particular configuration rather than the minimum.
Source: SAP Learning — Exploring SAP Analytics Cloud → Further reading: PowerKram — SF Workforce Analytics →Why would an analyst use predictive analytics in SAP Analytics Cloud?
- To predict software release trends
- To increase advertising reach
- To forecast business outcomes from historical data
- To improve hardware durability
Show answer & explanation
Correct: C — To forecast business outcomes from historical data. Smart Predict and predictive planning exist to project what the business data implies about what comes next — the whole purpose of the capability.
Why not the others: software trends (A), advertising reach (B), and hardware durability (D) are not what a business analytics platform’s predictive features are aimed at, however plausible they sound in isolation.
Source: SAP — Cloud analytics →A story loads slowly. What should be analysed first?
- Browser cache settings
- User access levels
- Network latency between the office and the data centre
- Data model complexity
Show answer & explanation
Correct: D — Data model complexity. Most SAC performance problems originate in how much the model asks for and how it is built, so that is where diagnosis starts — before the client or the network.
Why not the others: browser cache (A) and network latency (C) are worth ruling out but rarely the cause of a consistently slow story; and user access levels (B) affect what a user sees, not how fast it renders.
Source: SAP Learning — Exploring SAP Analytics Cloud →A story sits in My Files and a colleague needs to review and comment on it. What should the analyst do?
- Share the story with the colleague, who can then open it and add comments
- Export the story to PDF and email it for comments by reply
- Ask the colleague to rebuild the story in their own My Files
- Take screenshots and paste them into a document for review
Show answer & explanation
Correct: A — Share the story. My Files is private, so sharing is what makes the story reachable — and commenting in place keeps the discussion attached to the data rather than scattered across email.
Why not the others: a PDF export (B) and screenshots (D) both detach the conversation from the live story; and rebuilding it (C) duplicates work to solve a permissions question.
Source: SAP Learning — Designing stories in SAP Analytics Cloud → Further reading: PowerKram — Data career paths →Keep going: Learning & Career resources
SAC sits at the consumption end of SAP’s data estate — and it opens onto analyst and planning roles. Two PowerKram hubs back this exam.
Deep dive: the merged blueprint, live vs import, and study path
Why planning is a third of the exam
SAP used to certify SAC analytics and SAC planning separately. Those were merged into this single C_SAC code, and the blueprint carries the consequence: Story Design, Planning, and Data modeling each sit at 21–30%. If you came to SAC as a dashboard tool, planning is the area that will decide your result. Know versions cold — public versus private, and what publishing does — along with data actions for allocation, copy and calculation, value driver trees, and predictive planning. It is not an add-on to the exam; it is a third of it. SAP Learning — Planning with SAP Analytics Cloud →
Live versus import decides more than you think
This is the distinction the exam returns to from several directions. On a live connection the data stays in the source and is queried in real time — nothing is copied, so the source system defines data security, and transformation options are limited. On an import connection the data is copied into an SAC model, where it can be transformed, scheduled to refresh, and used for full planning. One idea, four consequences: security location, transformation, refresh, and planning capability. Learn it once and a whole family of questions resolves. SAP — Datasphere →
The security question people get wrong
Ask most SAC users where data security is defined and they will say data access controls — and on an imported model they are right. On a live connection they are wrong: the data never leaves the source, so the source system’s authorizations govern what comes back. That single reversal is worth internalizing because it is precisely the kind of item the performance-and-security area uses to separate people who have configured SAC from people who have read about it. SAP Learning — Exploring SAP Analytics Cloud →
Stories reward design judgement, not chart trivia
At 21–30%, story design is not a matter of naming chart types. The questions are about making a story work: linked analysis so a selection propagates across widgets, input controls and filters that let a reader explore rather than request a new report, responsive layout for the executives who will open it on a phone, and sharing so the conversation happens on the story rather than in an email thread. Build one properly and this area stops being memorization. SAP Learning — Designing stories in SAP Analytics Cloud →
Study path, validity, and the format shift
60 questions in 120 minutes at a 70% cut score gives you two minutes a question and a demanding bar. Start with SAP’s Exploring SAP Analytics Cloud material, then work the two official reference journeys — designing stories, and enterprise planning — because those are what SAP points at. Two practical notes: the credential is valid for 12 months and each successful Stay Current assessment extends it another 12, so this is one you maintain; and industry reporting indicates SAP is moving this certification line toward an AI role-play scenario format on newer releases while the current one stays multiple-choice. The underlying knowledge does not change, but check the format before you book. SAP HANA data engineer on PowerKram →
Frequently asked questions
What are the exam topic areas and their weights?
Five areas: Story Design (21–30%), Planning (21–30%), Data modeling, analysis and integration (21–30%), Connections and data preparation (11–20%), and Performance, troubleshooting and security management (10% or less). The first three carry the bulk of the paper.
How many questions is the exam, and what is the cut score?
60 questions in 120 minutes with a 70% cut score, in English. The exam uses multiple-choice and multiple-response items, with SAP stating how many answers are correct on multi-answer questions. The certification is valid for 12 months and extends by a further 12 with each successful Stay Current assessment.
How much of the exam is planning?
21–30% — the same band as story design and data modeling. SAP merged its previously separate SAC Analytics and Planning certifications into this one code, so planning carries full weight. Expect versions, data actions, value driver trees, and predictive planning at real depth.
What is the difference between a live and an import connection?
On a live connection the data stays in the source system and is queried in real time, so nothing is copied into SAC and the source system defines data security. An import connection copies data into an SAC model, where it can be transformed, scheduled to refresh, and used for full planning. That distinction drives several exam questions.
Is the exam changing format?
Industry reporting indicates SAP is transitioning this certification line toward an AI role-play scenario format on newer releases, while the current release remains multiple-choice. The SAP Analytics Cloud knowledge tested is the same either way, so concept-based preparation serves both — but confirm the format on SAP’s certification page before booking.
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