Task Type: Executable Task Task Title: Compare and Select AI Models via Chart Mode Try Yourself: Identify the model with the largest context window and lowest output token cost using the Generative AI Hub Chart Mode. Business Scenario: You need an LLM to summarize lengthy internal company reports. You must find a model that handles large documents efficiently while minimizing token costs to ensure your solution is budget-friendly. System Interaction Path: Cloud application UI -> SAP AI Launchpad -> Generative AI Hub -> Model Library -> Chart Mode.

Step-by-Step Execution: Open the SAP AI Launchpad and expand the Generative AI Hub menu on the left,. Select Model Library to view all available foundational models. Click the Chart mode button at the top of the model list. Select the drop-down for X-Axis and choose your desired parameter. Select the drop-down for Y-Axis and choose your desired parameter. Hover over the data points on the scatter plot and click View details on a selected model to review its Model Card. Example Values: X-Axis: Context Window Y-Axis: Output Token Cost Expected Result: A visual scatter plot comparing models is generated, allowing you to clearly identify and select the optimum model based on cost and context limits. Quick Mistakes: Using the Leaderboard mode instead of Chart mode, missing the visual dual-axis comparison. Selecting "Input Token Cost" instead of "Output Token Cost" on the Y-Axis, leading to inaccurate budget assumptions for generated summaries. Exam Insight: This tests your knowledge of model evaluation tools within the Generative AI Hub. The certification exam frequently tests your ability to optimize LLM applications for scalability, cost, and specific business problem parameters.

Task Type: Executable Task Task Title: Build and Version a Dynamic Prompt Try Yourself: Create a prompt template with variables in the Prompt Editor, test it with a selected model, and save it as a new version. Business Scenario: Your marketing team needs to generate personalized marketing emails for different customer segments. You must create a reusable master prompt where the customer name and product interest can be injected dynamically at runtime to ensure high-quality, targeted emails. System Interaction Path: Cloud application UI -> SAP AI Launchpad -> Generative AI Hub -> Prompt Editor.

Step-by-Step Execution: Navigate to the Prompt Editor via the Generative AI Hub menu on the left side of the SAP AI Launchpad. In the Message Blocks section, enter your prompt instructions and define variables by surrounding them with two curly brackets and preceding the name with a question mark (e.g., {{?customer_name}}). Open the Model Configuration section to select your desired LLM from the drop-down menu and adjust inference parameters like Max Tokens and Temperature. Navigate to the Variable Definitions section and enter test values in the Current Value or Default Value fields to validate your template logic. Click Run to generate a test response, then enter a Name and Version at the top of the screen and click Save. Example Values: Prompt Text: Write a promotional email for {{?customer_name}} interested in {{?product_category}}. Model: GPT-5-Nano Expected Result: The Prompt Editor successfully generates a personalized response during the test run, and the prompt is saved as a specific version that can later be accessed and reused via Prompt Management. Quick Mistakes: Forgetting the question mark in the variable syntax (e.g., using {{customer_name}} instead of {{?customer_name}}), preventing the system from recognizing the placeholder. Failing to set a Default or Current Value for defined variables before clicking Run, which can cause the model to use an empty string or result in an inference error. Exam Insight: This tests your knowledge of prompt templating and lifecycle management. The certification exam frequently evaluates how to parameterize prompts so they can be seamlessly populated with runtime data via the SAP Cloud SDK or system workflows.

Task Type: Executable Task Task Title: Configure LLM Parameters in Chat Interface Try Yourself: Adjust the max tokens and temperature in the Chat interface to prototype concise model responses for sales queries. Business Scenario: You are prototyping an AI assistant to answer customer product questions. You need to test a specific foundation model and restrict the response length to keep the answers concise and highly deterministic before building the full application. System Interaction Path: Cloud application UI -> SAP AI Launchpad -> Generative AI Hub -> Chat -> Configure chat settings.

Step-by-Step Execution: Navigate to the Chat menu on the left side of the Generative AI Hub. Click the Configure chat settings gear icon at the top of the chat window. In the Model Settings tab, click the select option icon to open the Model Library and choose your desired model. Adjust the Max Tokens slider to restrict the response length and adjust the Temperature slider to control the creativity of the output. Click Save, then enter a test prompt in the message input field and send it. Example Values: Model: GPT-5-Nano Max Tokens: 150 Temperature: 0.1 Prompt: "What are the key features of S/4HANA Cloud?". Expected Result: The chat session updates to use the selected model, generating a restricted-length, factual response based on the newly applied inference parameters. Quick Mistakes: Forgetting to click Save in the configuration menu before sending the prompt. Setting Max Tokens too low, causing the LLM response to abruptly cut off mid-sentence. Exam Insight: This tests your ability to use the Generative AI Hub for rapid prototyping and human-to-AI interaction without writing code. The certification exam emphasizes knowing which tool to use for quick iterative testing and parameter tuning.

Task Type: Executable Task Task Title: Create and Save a Prompt Template Try Yourself: Create a version-controlled prompt template defining System and User roles for structured JSON extraction. Business Scenario: The Facility Solutions Company needs to standardize how Large Language Models (LLMs) analyze customer emails. You must create a reusable prompt template in the Prompt Registry to ensure consistent, machine-readable JSON outputs across multiple applications. System Interaction Path: SAP AI Launchpad -> Generative AI Hub -> Prompt Editor

Step-by-Step Execution: Expand Generative AI Hub and select Prompt Editor in the left pane. Enter the overarching rules and constraints into the System role under the Message Blocks section. Select Add role to introduce the user role. Enter the dynamic query instructions and XML-like tags into the User role under the Message Blocks section. Click the Save Template button to open the dialog box. Select the appropriate Scenario Name from the dropdown. Enter a Template Name ensuring there are no whitespaces or special characters. Enter a Version following the major.minor format. Click the Save button to finalize. Example Values: Scenario Name: orchestration Template Name: message-analyzer-A101 Version: 1.0.0 Expected Result: The prompt template is saved to the Prompt Registry and becomes available in Prompt Management for retrieval, A/B testing, or integration into orchestration workflows. Quick Mistakes: Including whitespaces or special characters in the Template Name. Failing to use the required major.minor format (e.g., 1.0.0) for the Version number. Saving dynamic user queries in the System role instead of separating concerns properly. Exam Insight: Tests your ability to utilize the Prompt Registry to centralize, standardize, and version-control prompts. Understanding how to separate instructions (System role) from dynamic data (User role) is a core requirement for building scalable enterprise AI solutions.

Task Type: Executable Task Task Title: Configure the Document Grounding Module via Orchestration Service Try Yourself: Prepare a vector knowledge base and implement the document grounding module using the Generative AI Hub Orchestration Service. Business Scenario: The customer support team requires an intelligent AI assistant to fetch and list specific customer-reported maintenance issues stored across SharePoint folders. System Interaction Path: SAP Cloud SDK for AI (Python) - generative SAP AI Launchpad / Orchestration API

Step-by-Step Execution: Install the SAP Cloud SDK for AI (Python) - generative via command line. Set the credentials for the SDK in your environment. Perform the initial one-time administrative step by creating a resource group and a generic secret for Microsoft SharePoint grounding. Run the Pipeline API to read unstructured documents from the repository, chunk the data, generate embeddings, and store them in the vector database. Obtain the assigned repository ID for the vectorized data. In the Orchestration configuration, add a grounding request by specifying the retrieved repository IDs and defining data filters. Run the orchestration service with a configured message template passing the user query. Example Values: Selected Source / Repository ID: pipeline-2fc168ab-b951-4470-ab48-a8... Max Chunks: 10 User Query Template Value: "List customer-reported issues" Expected Result: The orchestration result returns a message content choice that accurately lists customer issues (e.g., "HVAC System Noise", "Plumbing Leak") grounded entirely in the context retrieved from the repository data. Quick Mistakes: Skipping the prerequisite: step of creating a resource group and generic secret before executing the Pipeline API. Failing to pass the exact repository ID to the grounding module configuration. Exam Insight: Concept Tested: Applying the RAG (Retrieval Augmented Generation) approach through the Generative AI Hub Orchestration Service. Why it matters for exam: Hands-on knowledge of how the Pipeline API and Vector API prepare data, and how the Orchestration Service ties the knowledge base to the LLM, is critical for implementation-focused certification questions.

Free SAP Generative AI Developer Exam Questions 2026

Free C_AIG_2604 Exam Questions (2026): SAP Generative AI Developer System Tasks

By TheExamQuestions Team 12 min read

The SAP Certified - SAP Generative AI Developer (C_AIG_2604) certification is evolving by evaluating one’s ability through real-world scenarios and system-based tasks beyond traditional theory and MCQs. These Free C_AIG_2604 Exam Questions are designed by emphasizing on building practical implementation and problem-solving skills across the real-world environment & SAP ecosystem.

C_AIG Free questions.webp

What Does the C_AIG_2604 Certification Cover?

The C_AIG_2604 exam certification tests you based on your understanding, development capabilities, and implementation best practices across several domains, which are as follows:

Your strategy on developing and implementing Generative AI solutions and tests your working with LLMs, RAG, and AI-powered workflows.
How do you solve business and technical challenges by applying prompt engineering techniques?
Your understanding on responsible AI principles, security considerations, and governance practices and the way you apply real-world problem-solving approaches .

What You Will Get in Free C_AIG_2604 Practice Questions?

Free C_AIG_2604 questions involving system-based and exam-style scenario tasks.
Listed “Try Yourself” questions to test your understanding across Real SAP Generative AI Developer implementation scenarios.
Detailed answer explanations(Step-by-step)
Practical exercises containing questions aligned with the latest certification objectives and system-task requirements covering Generative AI concepts, prompt engineering,and AI-powered solution development domains.

Why Do Free C_AIG Questions Matter?

The listed free C_AIG_2604 exam questions match the latest SAP exam patterns and are designed as per current exam objective.
It helps in understanding real business use cases by improving your ability to analyze, execute, and solve problems across AI based domains.
It covers “applied concepts” to ease your understanding of its practical implementation in real scenarios rather than memorizing answers.

If you’re worrying about the full course preparation, then you should not! We have curated all rounder C_AIG_2604 System Questions for your ease, everything at one place.

Before diving Into the Full Course Materials, warm up your basics with the free C_AIG System Exam Tasks!

Practice Questions

Test your knowledge with these free sample questions

Task 1

Task Type: Executable Task

Task Title: Compare and Select an LLM via Model Library

Try Yourself: Use the Model Library leaderboard to evaluate foundation models by benchmark scores and test a chosen model directly in the chat interface.

Business Scenario: Your enterprise needs a cost-effective but highly capable model for an upcoming project. You must benchmark available Large Language Models (LLMs) based on their ChatBot Arena scores before deploying one for a categorization task.

System Interaction Path: SAP AI Launchpad -> Generative AI Hub -> Model Library

Answer:

Step-by-Step Execution:

Select Model Library in the left navigation pane.
Click the Leaderboard mode tab at the top of the screen to view benchmarking data.
Locate the ChatBotArena Arena Score column header.
Click the column header and select Sort Descending to view the highest-rated models.
Click the Catalog mode tab to return to the standard model view.
Search for your selected model (e.g., GPT-4.1 nano) and click on its model card to view details.
Click the Use in Chat button in the top right corner to open the testing interface.

Example Values:

Mode: Leaderboard
Sort Column: ChatBotArena Arena Score
Search/Model Selection: GPT-4.1 nano

Expected Result: The Model Library is successfully utilized to compare models based on objective benchmarks, and the chosen model is immediately opened in the Launchpad chat interface for manual prompt testing.

Quick Mistakes:

Attempting to sort by benchmark metrics while remaining in the "Catalog" mode instead of switching to "Leaderboard" mode.
Assuming all models listed in the global library are automatically available to your specific tenant (availability depends on your SAP AI Core entitlements and deployment configuration).
Selecting "Use in Prompt Editor" when the goal was to simulate a conversational back-and-forth test in the Chat UI.

Exam Insight: Tests your knowledge of the Model Library's functionalities within SAP AI Launchpad. Understanding how to toggle between Catalog and Leaderboard modes to make data-driven model selections based on metrics (like cost and quality) is a key competency for SAP AI architects.

Task 2

Task Type: Executable Task

Task Title: Manage and Retrieve Prompt Versions

Try Yourself: Access a saved prompt in Prompt Management, review its version history, and open it for editing.

Business Scenario: Another development team has already created and validated a highly effective prompt for extracting key data from vendor invoices. To save time and ensure consistency, you need to locate this existing prompt, review its history, and reuse it for your new automation project.

System Interaction Path: Cloud application UI -> SAP AI Launchpad -> Generative AI Hub -> Prompt Management -> Prompt Details.

Task 3

Task Type: Executable Task

Task Title: Ingest Direct Data Chunks via Vector API

Try Yourself: Bypass automated document processing and directly push pre-processed document chunks into the vector database using the Vector API.

Business Scenario: Your data science team has already cleaned, formatted, and chunked a set of highly sensitive financial policies using a custom proprietary algorithm. Instead of relying on automated SAP pipelines to read and chunk raw files, you need to manually ingest these specific data chunks directly into the vector knowledge base to maintain strict control over the embedding process.

System Interaction Path:

Python Environment / SAP Cloud SDK for AI (generative)
SAP Generative AI Hub -> Vector API

Answer:

Step-by-Step Execution:

Install the SAP Cloud SDK for AI (Python) - generative in your environment and set the required SDK credentials.
Perform the mandatory one-time administrative step by creating a Resource Group for Grounding.
Initialize the Vector API client within your Python environment.
Execute an API call to create a new vector collection specifically for these financial policies.
Create documents by directly passing your custom chunks of data into the Vector API.
Execute the final API call to store the data in the vector database and retrieve the newly assigned repository ID.

Example Values:

Data Chunk: "Financial Policy Clause 4.2: Expense Reporting..."
Assigned Repository ID: vector-repo-8a9b-4470-cd12-f192

Expected Result: The custom-chunked data is successfully vectorized and stored in the SAP HANA Vector database. The system returns a unique repository ID, making the custom data immediately available for the Orchestration module.

Quick Mistakes:

Attempting to upload raw, unstructured documents (like full PDF files) via the Vector API. The Vector API is exclusively for pre-chunked data; raw files require the Pipeline API.
Failing to create a vector collection before attempting to insert the document chunks.

Exam Insight:

What concept is tested: The architectural difference in data ingestion methods for Document Grounding.
Why it is important in certification exam: SAP exams heavily test knowing when to use specific ingestion paths. You must be able to distinguish between using the Pipeline API (for automated reading, chunking, and embedding from external repositories like SharePoint) versus the Vector API (for directly providing manual chunks of data).

Task 4

Task Type: Executable Task

Task Title: Enforce Model Restrictions in Orchestration Configuration

Try Yourself: Create an orchestration configuration that restricts Generative AI Hub usage to a specific, pre-approved list of Large Language Models (LLMs).

Business Scenario: To control enterprise costs and enforce strict compliance policies, your company dictates that only the gpt-4o-mini model is approved for the automated customer service workflow. You must configure the environment so that no other models can be accessed through this deployment.

System Interaction Path: SAP AI Launchpad -> ML Operations -> Configurations

Task 5

Task Type: Executable Task

Task Title: Provision SAP AI Core and Generate Service Keys

Try Yourself: Execute SAP BTP Boosters to provision the SAP AI Core environment and generate the credentials required for SDK access.

Business Scenario: A new development sub-account has been created for your AI engineering team. Before the team can build generative AI applications or programmatically connect via the Python SDK, you must provision the SAP AI Core environment and extract the required authentication credentials.

System Interaction Path: SAP BTP Cockpit -> Boosters -> Instances and Subscriptions