C_AIG_2412
Free SAP Certified Associate - Generative AI Developer Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$2406 exam domainsLevel Intermediate2 timed practice tests in the course
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A developer is building a compliance-reporting chatbot on SAP Generative AI Hub that must return consistent, factual, low-variability answers across repeated runs for audit purposes. Which temperature setting should be configured for the model?
Temperature controls output randomness; low values (near 0) make token selection near-deterministic, which is required when consistent, factual answers are needed for compliance use cases.
A developer at an SAP customer needs to classify incoming customer support tickets into exactly one of five predefined categories (Billing, Technical, Shipping, Account, Other) using a prompt in the Generative AI Hub. Initial zero-shot prompting produces inconsistent category names (e.g., 'billing issue' instead of 'Billing'). Which technique BEST resolves this inconsistency?
Few-shot examples that demonstrate the exact input-to-label mapping anchor the model to the required output format and vocabulary, directly reducing label drift and inconsistency — a core prompt engineering technique tested for classification tasks.
A development team built a generative AI application using GPT-4 through the Generative AI Hub's Orchestration Service. Business now requires switching the underlying model to a different provider's LLM (e.g., Anthropic Claude) with minimal disruption and NO changes to the application's calling code. Which approach BEST meets this requirement?
The Orchestration Service exposes a harmonized API where the model is just a configurable parameter in the model configuration module. Changing the model name/version there swaps the underlying LLM without touching application code — this is the core value proposition of the Orchestration Service tested on the exam.
A development team built and validated a prompt template named 'customer-support-v1' in the Prompt Registry of their SAP AI Launchpad DEV subaccount. They now need the identical template available in the PROD subaccount's Orchestration Service without manually re-typing the template content. Which approach BEST achieves this?
Prompt Registry templates are managed as resources via REST APIs exposed by the Orchestration Service; promoting a template across environments is done programmatically (GET from source, PUT/POST to target), which fits naturally into a CI/CD pipeline and avoids drift or manual error.
A developer is building a Node.js backend on Cloud Foundry that must call the Orchestration Service to run a chat completion and allow the LLM to invoke a custom 'getWeather' tool when needed. Which approach, using the SAP Cloud SDK for AI (JavaScript), correctly implements this requirement?
The `@sap-ai-sdk/orchestration` package supports function/tool calling by supplying a `tools` array in the orchestration configuration passed to `chatCompletion()`; the response exposes tool call requests so the application can execute the function and send the result back as a follow-up message in the conversation.
A development team is building a RAG application using the SAP Generative AI Hub Orchestration Service. They need LLM responses to be grounded in internal SAP documents stored in a data repository before the model generates an answer. Which module must they add to the orchestration pipeline configuration to achieve this?
The Orchestration Service pipeline supports a dedicated grounding module whose type is set to the document grounding service; this module retrieves relevant document chunks from a configured data repository and injects them into the prompt before the LLM call.
A company wants its LLM-powered assistant to answer questions using internal product documentation stored in SAP BTP, without retraining the model and while minimizing hallucinated answers. Which approach should they implement?
The Orchestration Service provides document grounding, retrieving relevant content from a document repository and injecting it into the prompt context (RAG), reducing hallucinations without retraining the model.
A team is building a prompt that must return structured data for downstream parsing by an SAP application, requiring a strict JSON object with keys 'invoice_number', 'amount', and 'due_date'. Which prompt design approach BEST ensures reliable, parseable JSON output?
Explicitly defining the schema (keys, types) and instructing the model to output only JSON with no surrounding text is the standard technique for reliable structured output and minimizes parsing failures downstream.
Which SAP tool provides a graphical interface within SAP BTP for discovering available foundation models in the Generative AI Hub, testing prompts interactively, and comparing outputs across models before building an application?
AI Launchpad is the UI layer on SAP BTP for the Generative AI Hub, letting users browse model catalogs, test prompts, and compare model responses without writing code.
Which SAP Generative AI Hub component is specifically responsible for storing parameterized prompt templates so they can be referenced by name and version across multiple orchestration pipelines and applications?
The Prompt Registry is the dedicated component for creating, versioning, and managing reusable prompt templates that can be invoked by name/version from orchestration pipelines.
A Python application using the generative-ai-hub-sdk needs to stream tokens from a chat completion back to a web client as they are generated, rather than waiting for the full response. Which capability should the developer use?
generative-ai-hub-sdk (and the underlying OpenAI-compatible client it wraps) supports streaming responses by enabling a streaming flag on the chat completion call, which yields partial response chunks as a generator/iterator so they can be forwarded to the client incrementally.
Which SAP BTP service is used by the Generative AI Hub to persist and search vector embeddings for document grounding?
SAP HANA Cloud's native vector engine stores document embeddings and performs similarity search, serving as the vector store backing the document grounding capability in Generative AI Hub.
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How much does the C_AIG_2412 exam cost?
The exam fee is approximately $240 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 6 domains: Foundations of SAP Business AI and Large Language Models (High (>12%)), Prompt engineering fundamentals and techniques (High (>12%)), The Generative AI Hub and Orchestration Service (High (8-12%)), Prompt development, registry, and templates (Medium (8-12%)), Building and evaluating with the SAP Cloud SDK for AI (Medium (8-12%)), Grounding, RAG, and document grounding with vector search (High (>12%)). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.