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What does the acronym LLM stand for in the context of the SAP Generative AI Developer certification?

Linked Language Module

Large Language Model

Logic Learning Machine

Layered Linguistic Mapping

to track

2026 Statistics

Key Facts: SAP Generative AI Developer Exam

Exam Questions

180-minute time limit

67%

Passing Score

SAP certification standard

$245

Exam Fee

Per attempt (Pearson VUE)

31-40%

Generative AI Hub Weight

Largest exam domain

C_AIG_2412

Credential ID

SAP Certification Hub

Remote

Testing Available

Pearson VUE OnVUE

The C_AIG_2412 exam has 80 multiple-choice questions, a 180-minute time limit, and a 67% passing score. It covers four domains: LLMs (21-30%), SAP AI Core (21-30%), SAP Business AI (11-20%), and the SAP Generative AI Hub (31-40%). The exam fee is $245 and the credential is delivered through Pearson VUE or the SAP Certification Hub subscription.

Sample SAP Generative AI Developer Practice Questions

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

1What does the acronym LLM stand for in the context of the SAP Generative AI Developer certification?

A.Linked Language Module

B.Large Language Model

C.Logic Learning Machine

D.Layered Linguistic Mapping

Explanation: LLM stands for Large Language Model. LLMs are deep learning neural networks (typically transformer-based) trained on massive text corpora to understand and generate human-like text. SAP's Generative AI Hub provides access to multiple LLMs (GPT-4, Claude, Gemini, Llama, Mistral, Granite) through a harmonized API.

2Which underlying neural network architecture powers most modern Large Language Models accessible through the SAP Generative AI Hub?

A.Convolutional Neural Network (CNN)

B.Recurrent Neural Network (RNN)

C.Transformer architecture with self-attention

D.Support Vector Machine (SVM)

Explanation: Modern LLMs (GPT-4, Claude, Gemini, Llama, Mistral) are built on the Transformer architecture introduced in the 2017 paper 'Attention Is All You Need'. The self-attention mechanism allows the model to weigh the relevance of different tokens in the input sequence in parallel, which is what enables LLMs to capture long-range dependencies efficiently.

3In LLM terminology, what is a 'token'?

A.A security credential issued by SAP BTP

B.A subword unit (or character/word fragment) that the model processes; LLM cost and context length are measured in tokens

C.A row in the SAP HANA Cloud Vector Engine

D.An OAuth scope used by SAP AI Core

Explanation: A token is the basic unit of text an LLM processes. Tokenizers split text into subword pieces (e.g., 'unbelievable' → 'un', 'believ', 'able'). For English, ~1 token ≈ 4 characters or ~0.75 words. Both pricing and context-window limits in the Generative AI Hub are expressed in tokens, so token efficiency directly affects cost.

4Which prompt engineering technique provides NO examples and asks the LLM to perform a task purely from instruction?

A.Few-shot prompting

B.Zero-shot prompting

C.Chain-of-thought prompting

D.Retrieval-augmented prompting

Explanation: Zero-shot prompting gives the model only an instruction with no demonstrations. The model relies entirely on its pre-trained knowledge to respond. It works well for general tasks but accuracy drops for domain-specific or formatted-output tasks where examples (few-shot) help guide the model.

5Which prompt engineering technique includes a small number of input/output examples directly in the prompt to guide the model's response format?

A.Zero-shot prompting

B.Few-shot prompting

C.Self-consistency prompting

D.ReAct prompting

Explanation: Few-shot prompting provides 2-5 example pairs ('shots') in the prompt so the model can pattern-match the format and style. It is especially useful for classification tasks, structured output (JSON, CSV), or when the task is unusual and the model has not seen it during training.

6What is the primary purpose of chain-of-thought (CoT) prompting?

A.To compress prompts by removing examples

B.To encourage the model to expose its intermediate reasoning steps before producing a final answer, improving accuracy on multi-step problems

C.To limit the model's output length to one sentence

D.To bypass content filters

Explanation: Chain-of-thought prompting (e.g., adding 'Let's think step by step') asks the model to write out its reasoning before the answer. This significantly improves performance on arithmetic, logic, and multi-step reasoning tasks. CoT works best on capable models (GPT-4-class) and is widely used in Generative AI Hub orchestration flows.

7Which LLM parameter primarily controls the randomness/creativity of generated output?

A.max_tokens

B.temperature

C.stop_sequences

D.frequency_penalty

Explanation: Temperature scales the probability distribution of next-token selection. Low temperature (0.0-0.3) gives deterministic, focused output ideal for factual tasks. High temperature (0.8-1.5) increases diversity and creativity. The Generative AI Hub harmonized API exposes temperature consistently across providers.

8What does the 'context window' of an LLM refer to?

A.The UI panel in SAP AI Launchpad that shows model output

B.The maximum number of tokens (input + output) the model can process in a single request

C.The geographic region where the model is deployed

D.The number of concurrent users the deployment supports

Explanation: The context window is the maximum total tokens the model can attend to in one call, including the system prompt, user message, prior conversation, retrieved context, and the generated response. Exceeding it causes truncation or an error. Modern LLMs in the Generative AI Hub range from ~8K to over 1M tokens.

9What is an 'embedding' in the context of generative AI?

A.A binary file stored in SAP AI Core object storage

B.A high-dimensional numerical vector that represents the semantic meaning of text, enabling similarity search

C.An iframe used to embed Joule into SAP Fiori

D.A pre-built prompt template

Explanation: An embedding is a dense vector (e.g., 1,536 dimensions for text-embedding-ada-002) that captures the semantic meaning of input text. Semantically similar texts produce vectors close together (high cosine similarity). Embeddings are the foundation of RAG, similarity search, and clustering, and are stored in the SAP HANA Cloud Vector Engine.

10Which similarity metric is most commonly used to compare two embedding vectors for semantic similarity?

A.Manhattan distance

B.Cosine similarity

C.Hamming distance

D.Jaccard index

Explanation: Cosine similarity measures the cosine of the angle between two vectors and ranges from -1 (opposite) to 1 (identical direction). It is robust to vector magnitude differences, making it the standard for embedding comparison. The SAP HANA Cloud Vector Engine supports COSINE_SIMILARITY natively in SQL.

About the SAP Generative AI Developer Exam

The SAP Generative AI Developer certification (C_AIG_2412) validates skills in building LLM-powered applications with SAP AI Core, SAP Business AI, and the SAP Generative AI Hub including orchestration, grounding, and prompt engineering.

Questions

80 scored questions

Time Limit

180 minutes

Passing Score

67%

Exam Fee

$245 (SAP (Pearson VUE / SAP Certification Hub))

SAP Generative AI Developer Exam Content Outline

21-30%

Large Language Models (LLMs)

Transformers, tokens, embeddings, prompt engineering (zero-shot, few-shot, chain-of-thought), RAG vs fine-tuning, function calling, hallucinations, evaluation

21-30%

SAP AI Core

Resource groups, secrets, configurations, executions, deployments, Argo Workflows, AI Launchpad, OAuth/XSUAA, templates, batch processing

11-20%

SAP Business AI

Joule copilot, Joule Studio skills, AI Foundation, responsible AI principles, CAP integration, Integration Suite, AI ethics and explainability

31-40%

SAP Generative AI Hub

Harmonized API across LLMs, orchestration service, prompt templates, content filtering, data masking, grounding, document grounding, HANA Cloud Vector Engine, generative-ai-hub-sdk

How to Pass the SAP Generative AI Developer Exam

What You Need to Know

Passing score: 67%
Exam length: 80 questions
Time limit: 180 minutes
Exam fee: $245

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 Generative AI Developer Study Tips from Top Performers

1Master the Generative AI Hub orchestration pipeline order: templating → input filter / data masking → grounding → LLM → output filter (this is heavily tested)

2Get hands-on with SAP AI Launchpad and the Generative AI Hub Playground — try chat completions, prompt templates, and grounding in your trial BTP account

3Memorize AI Core's core objects: scenarios, configurations, executions, deployments, artifacts, secrets, resource groups — and which ones are scoped per resource group

4Practice writing HANA Cloud SQL with REAL_VECTOR and COSINE_SIMILARITY() — the exam tests practical RAG retrieval queries

5Know when to use RAG vs prompt engineering vs fine-tuning vs prompt-tuning — frequent fact updates favor RAG; consistent house style favors fine-tuning

6Understand prompt-injection defenses (delimiters, system priority, input/output filters) — security and responsible AI are recurring exam themes

Frequently Asked Questions

What is the SAP Generative AI Developer certification (C_AIG_2412)?

C_AIG_2412 is SAP's associate-level certification for developers building LLM-powered applications on SAP BTP. It validates knowledge of LLM fundamentals, SAP AI Core (the AI runtime), SAP Business AI (Joule and the wider AI strategy), and the SAP Generative AI Hub (the harmonized LLM platform with orchestration, grounding, and guardrails).

What topics does the SAP C_AIG_2412 exam cover?

The exam covers four domains: Large Language Models (21-30%), SAP AI Core (21-30%), SAP Business AI (11-20%), and SAP Generative AI Hub (31-40%). Generative AI Hub is the heaviest-weighted domain, covering orchestration, prompt templates, content filtering, data masking, and grounding with the SAP HANA Cloud Vector Engine.

How many questions are on the C_AIG_2412 exam and how long is it?

The exam contains approximately 80 multiple-choice/multi-response questions and you have 180 minutes (3 hours) to complete it. The passing score is around 67%. It is delivered through Pearson VUE or SAP Certification Hub with both online-proctored and test-center options.

How much does the SAP Generative AI Developer exam cost?

The exam fee is approximately $245 USD per attempt when booked individually. SAP also offers the SAP Certification Hub subscription (~$575/year, includes 6 attempts across the catalog), which is the most cost-effective option if you plan to take multiple SAP certifications in a year.

What prerequisites should I have before taking C_AIG_2412?

There are no formal prerequisites. SAP recommends hands-on familiarity with SAP BTP, basic Python or Node.js, REST APIs, and exposure to Cloud Foundry. Knowledge of generative AI concepts (transformers, embeddings, RAG), LLM prompt engineering, and SAP HANA Cloud helps significantly.

Which SAP learning journey should I follow to prepare?

SAP publishes the free 'SAP Generative AI Developer' learning journey on learning.sap.com, which covers the four exam domains in depth. Pair it with hands-on tutorials in SAP AI Launchpad, the Generative AI Hub Playground, and the generative-ai-hub-sdk Python package. Practice questions like the ones on OpenExamPrep help you measure readiness.

Is it 'SAP Generative AI Developer' or 'Specialist'?

The official credential name is 'SAP Certified Associate - SAP Generative AI Developer'. Some third-party listings still refer to a 'Specialist' label, but SAP's certification page and credential ID C_AIG_2412 use 'Developer'.