SAP Generative AI Developer Practice Exam (C_AIG)
Prepare for the SAP Certified Associate — SAP Generative AI Developer exam (C_AIG) with scenario questions across the full syllabus: SAP AI Core, the generative AI hub, large language models, and SAP Business AI. Each explanation links to the exact SAP documentation page, with full timed simulation in Exam mode.
Start 24-hour free trial →C_AIG exam at a glance
- Vendor
- SAP
- Exam code
- C_AIG (current versions C_AIG_2412, C_AIG_2511)
- Certification
- SAP Certified Associate — SAP Generative AI Developer
- Level
- Associate (developer)
- Questions
- 60
- Duration
- 120 minutes
- Passing score
- 65%
- Language
- English
- Cost
- Region-dependent; single or multi-attempt subscription via SAP Certification Hub — confirm on SAP’s site
- Validity
- 1 year (SAP AI certifications update frequently)
Sources: SAP Learning — official SAP Generative AI Developer certification. SAP versions this exam by release (for example C_AIG_2412, C_AIG_2511) and may change topics, items, and weighting at any time; confirm the current version, format, and pricing with SAP before scheduling.
About the C_AIG Generative AI Developer certification
The SAP Certified Associate — SAP Generative AI Developer (exam C_AIG) validates that you can build and extend SAP Business Technology Platform (BTP) applications with generative AI. It confirms foundational, developer-oriented skills: working with SAP AI Core and SAP AI Launchpad, using SAP’s generative AI hub to access and orchestrate large language models, and grounding AI in enterprise data. It is a technical, hands-on credential — not a business-positioning exam.
The exam centers on SAP’s AI stack: SAP AI Core for deploying and managing AI models and workflows, SAP AI Launchpad as the operations cockpit, and the generative AI hub for model access, prompt tooling, orchestration, and grounding (including retrieval-augmented generation over vectorized enterprise data). It also expects a working understanding of large language model behavior — capabilities, limitations, evaluation, and responsible use — and where generative AI fits within SAP Business AI, including Joule.
PowerKram’s C_AIG practice questions mirror that developer focus and link every explanation to the exact SAP documentation page it derives from, so a wrong answer becomes a specific page to read. For deeper context on the concepts this exam tests, see the AI & machine learning guide.
C_AIG exam domains and weights
SAP publishes weight ranges for this exam. The generative AI hub is the single heaviest area. Ranges are SAP’s own and overlap by design, so they do not sum to exactly 100 — plan study time roughly in proportion to them, and confirm the current split on SAP’s exam page (SAP can update topics and weighting at any time).
Model access and the model catalog, prompt tooling and orchestration, grounding and retrieval-augmented generation over enterprise data, and connecting LLM-powered scenarios to SAP applications.
LLM capabilities and limitations, prompt design and optimization, embeddings and vector-based knowledge handling, model selection, evaluation, and responsible-use considerations in enterprise settings.
Core services for deploying, running, and managing AI models and workflows; executables and configurations; resource plans and GPU allocation; lifecycle operations and integration with enterprise applications.
SAP’s Business AI portfolio and value context — how generative AI fits SAP solutions, Joule and embedded AI, and why solution choices must support real business use cases.
Source: SAP — SAP Generative AI Developer certification (C_AIG). Weight ranges are SAP’s published bands and overlap by design; SAP reserves the right to update topics, items, and weighting.
Who the C_AIG exam is for
This is a technical, developer-focused associate credential, well suited to people building AI into SAP solutions:
- SAP BTP developers extending applications with generative AI, LLMs, and the generative AI hub.
- AI and machine-learning engineers working in the SAP ecosystem with AI Core and AI Launchpad.
- Solution and integration consultants embedding generative AI into SAP S/4HANA and BTP scenarios.
- Developers pivoting toward AI who want to validate applied generative-AI skills in an SAP context.
Common companions are the SAP Business AI exam, the BTP platform exams like SAP BTP Architect, and developer credentials such as SAP CAP Developer. For the roles this credential supports, with salary ranges and progression, see the AI engineer career path in our Career Hub.
What this C_AIG practice exam delivers
Learn mode
Get the correct answer, the reasoning, and why each other option is wrong — immediately after each question. Ideal for the “which service or technique fits” decisions the exam leans on.
Exam mode
A timed run that mirrors the 60-question, 120-minute C_AIG format, so you build the pacing and scenario reasoning the real exam demands.
Source-linked explanations
Every answer links to the exact SAP documentation page — so you learn AI Core, the generative AI hub, and grounding from SAP’s own Help Portal, not just a memorized letter.
Score by domain
Results break down by the four exam domains — generative AI hub, LLMs, AI Core, and Business AI — so practice tells you exactly what to drill.
Sample C_AIG practice questions
Ten free scenario questions across the C_AIG domains, each with a full explanation and a source link to the SAP documentation it derives from. The complete bank is available with the 24-hour trial.
A developer needs to deploy and manage the lifecycle of machine learning models within an SAP environment, integrated with enterprise applications.
Which SAP service provides the runtime for training, deploying, and serving AI models?
- SAP Analytics Cloud
- SAP AI Core
- SAP Datasphere
- SAP Build Apps
Show answer & explanation
Correct: B — SAP AI Core. SAP AI Core is the runtime that handles training, deployment, and serving of AI models and workflows on SAP BTP, with lifecycle management and integration to enterprise applications.
Why not the others: Analytics Cloud (A) is BI and planning; Datasphere (C) is a data-management/warehousing layer; Build Apps (D) is low-code app building — none is the AI model runtime.
Source: SAP Help — SAP AI Core → Further reading: PowerKram — AI & machine learning →A team wants a single place to access multiple large language models, manage prompts, and orchestrate generative AI scenarios inside SAP.
Which capability provides unified access to LLMs with prompt and orchestration tooling?
- The generative AI hub in SAP AI Core
- SAP HANA Cloud vector engine alone
- SAP Fiori launchpad
- SAP Cloud Transport Management
Show answer & explanation
Correct: A — the generative AI hub. The generative AI hub in SAP AI Core gives unified access to a catalog of LLMs plus prompt tooling, orchestration, and grounding, so teams build generative scenarios from one place.
Why not the others: the HANA vector engine (B) stores embeddings but isn’t the model-access hub; the Fiori launchpad (C) is a UI shell; Transport Management (D) moves configuration between systems.
Source: SAP Help — Generative AI hub in SAP AI Core → Further reading: PowerKram — SAP Business AI exam →A developer wants an LLM to answer questions using the company’s own current product documentation, without retraining the model.
Which technique should the developer use?
- Fine-tune the base model on the entire company intranet
- Increase the model’s temperature parameter
- Retrieval-augmented generation (RAG): retrieve relevant documents and supply them as grounding context in the prompt
- Reduce the maximum token limit to force shorter answers
Show answer & explanation
Correct: C — RAG. Retrieval-augmented generation grounds an LLM in current, source-of-truth content by retrieving relevant documents (often via vector search) and passing them as context, improving accuracy without retraining.
Why not the others: fine-tuning (A) is costly and stale between runs; temperature (B) affects randomness, not knowledge; a lower token limit (D) just shortens output.
Source: SAP Help — Grounding in the generative AI hub → Further reading: PowerKram — AI & machine learning →A developer must convert product descriptions into numeric representations so a system can find semantically similar items.
What are these numeric representations called?
- Tokens
- Embeddings (vector representations)
- Hyperparameters
- Checkpoints
Show answer & explanation
Correct: B — embeddings. Embeddings are dense numeric vectors that capture semantic meaning, so similar text sits close together in vector space — the basis for semantic search and RAG retrieval.
Why not the others: tokens (A) are the units text is split into before processing; hyperparameters (C) configure training; checkpoints (D) are saved model states.
Source: SAP Help — Embeddings in the generative AI hub →A developer needs to run a training job that requires GPU acceleration for a deep learning model in SAP AI Core.
How is the compute (including GPU) for a workload specified?
- By selecting a resource plan on the executable/configuration
- By editing the model weights directly
- By increasing the LLM temperature
- GPU workloads are not supported in SAP AI Core
Show answer & explanation
Correct: A — a resource plan. In SAP AI Core, resource plans define the compute allocated to a workload, including CPU/GPU tiers, and are referenced from the executable and configuration that run the job.
Why not the others: editing weights (B) isn’t how compute is requested; temperature (C) is unrelated to compute; and SAP AI Core does support GPU resource plans (D).
Source: SAP Help — SAP AI Core resource plans →An operations team wants a visual cockpit to monitor deployments, manage configurations, and observe AI scenarios without writing API calls for every action.
Which SAP tool provides this operations interface for SAP AI Core?
- SAP AI Launchpad
- SAP GUI
- SAP Web IDE
- SAP Solution Manager
Show answer & explanation
Correct: A — SAP AI Launchpad. SAP AI Launchpad is the central, multi-tenant operations cockpit for managing AI scenarios, deployments, configurations, and connections across SAP AI Core and other AI runtimes.
Why not the others: SAP GUI (B) is the classic ERP client; Web IDE (C) is a legacy development environment; Solution Manager (D) is for application lifecycle management, not AI operations.
Source: SAP Help — SAP AI Launchpad →A developer wants to steer an LLM’s output format and behavior for a customer-summary task without changing the model itself.
Which approach best fits?
- Retrain the model on customer data for each request
- Prompt engineering: use clear instructions, examples, and constraints in the prompt (and prompt templates in the hub)
- Delete the model and select a smaller one at random
- Disable grounding to speed up responses
Show answer & explanation
Correct: B — prompt engineering. Clear instructions, few-shot examples, and constraints (managed via prompt templates in the generative AI hub) steer output format and behavior without touching the underlying model.
Why not the others: per-request retraining (A) is impractical; random model swaps (C) don’t address formatting; disabling grounding (D) reduces accuracy rather than shaping output.
Source: SAP Help — Prompt tooling in the generative AI hub → Further reading: PowerKram — SAP CAP Developer exam →A company wants an AI copilot embedded across SAP applications that gives role-specific, context-aware assistance to employees.
Which SAP offering is this?
- SAP Joule
- SAP Signavio
- SAP Concur
- SAP Fieldglass
Show answer & explanation
Correct: A — SAP Joule. Joule is SAP’s generative-AI copilot embedded across SAP applications, providing context-aware, role-specific assistance grounded in business data and processes.
Why not the others: Signavio (B) is process management; Concur (C) is travel and expense; Fieldglass (D) is external workforce management — none is the embedded AI copilot.
Source: SAP Help — SAP Joule →A generated customer summary occasionally states facts that are plausible but not present in the source data.
What is this failure mode, and what is the primary mitigation?
- Overfitting; mitigate by adding more layers to the model
- Hallucination; mitigate by grounding the model in trusted source data (for example, RAG) and adding human review
- Latency; mitigate by increasing GPU count
- Tokenization error; mitigate by disabling embeddings
Show answer & explanation
Correct: B — hallucination. LLMs can produce fluent but unsupported statements. Grounding the model in trusted enterprise data (RAG) and adding human review are the primary enterprise mitigations, a core responsible-AI concern.
Why not the others: overfitting (A) is a training-data issue in classical ML; latency (C) is a performance concern; tokenization errors (D) are unrelated to fabricated facts.
Source: SAP Help — Responsible use in the generative AI hub → Further reading: PowerKram — AI & machine learning →What best characterizes SAP’s distinctive approach to enterprise AI?
A stakeholder asks how SAP’s AI differs from a general-purpose AI platform.
- SAP focuses AI solely on customer-support chatbots
- SAP offers AI only as a future roadmap item with nothing available today
- SAP deeply integrates AI with business processes and data across its application suite, grounded in enterprise context
- SAP uses AI mainly for marketing analytics
Show answer & explanation
Correct: C. SAP’s differentiator is embedding AI directly into business processes and data across its suite — relevant, reliable, and responsible AI grounded in enterprise context rather than a standalone, general-purpose tool.
Why not the others: SAP AI spans far more than support chatbots (A) or marketing (D), and its Business AI capabilities are available today, not future-only (B).
Source: SAP Learning — SAP Generative AI Developer →Keep going: Learning & Career resources
Generative AI on SAP BTP is one of the fastest-growing developer specialties, and this credential is a recognized proof point. Two PowerKram hubs back this exam.
Deep dive: exam facts, the SAP AI stack, and a study plan
Exam facts and versioning
C_AIG is a 60-question, 120-minute associate exam with a 65% passing score, in English, registered through the SAP Certification Hub and valid for one year. SAP versions these exams by release — recent forms include C_AIG_2412 and C_AIG_2511 — and updates topics and weighting over time, so always confirm the active version and outline on SAP’s certification page. SAP AI certifications move quickly; plan to recertify as the platform evolves. Read the IT certifications guide →
The SAP AI stack at a glance
Four pieces anchor the exam. SAP AI Core is the runtime that trains, deploys, and serves models and workflows, with resource plans for compute. SAP AI Launchpad is the operations cockpit for managing scenarios and deployments. The generative AI hub (inside SAP AI Core) provides unified LLM access, prompt tooling, orchestration, and grounding — the single heaviest exam area. And SAP Business AI, including Joule, is the portfolio and value context that explains where generative AI fits SAP solutions. Know what each does and how they connect. See the SAP Business AI exam →
LLM concepts the exam assumes
Expect applied questions on prompt engineering (instructions, examples, constraints, templates), embeddings and vector search, retrieval-augmented generation for grounding, model selection and evaluation, and responsible use — especially mitigating hallucinations with grounding and human review. These aren’t trivia; scenarios ask you to pick the technique that fits a real requirement. Read the AI & machine learning guide →
Realistic study plan
Follow the official SAP Learning journey for the SAP Generative AI Developer certification and get hands-on: provision SAP AI Core, use SAP AI Launchpad, and build a small grounded scenario in the generative AI hub (prompt template plus RAG). Read the SAP Help Portal pages for AI Core, AI Launchpad, the generative AI hub, and Joule — the same pages linked in the explanations above — then run timed practice until you consistently clear 65%. AI engineer career paths →
Frequently asked questions about the C_AIG exam
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