Download SAP Certified Associate - SAP Generative AI Developer.C_AIG_2412.Pass4Success.2026-01-28.19q.vcex

Vendor: SAP
Exam Code: C_AIG_2412
Exam Name: SAP Certified Associate - SAP Generative AI Developer
Date: Jan 28, 2026
File Size: 14 KB
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Demo Questions

Question 1
What are some examples of generative Al technologies? Note: There are 2 correct answers to this question.
  1. Al models that generate new content based on training data
  2. Rule-based algorithms
  3. Robotic process automation
  4. Foundation models
Correct answer: A, D
Question 2
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.
  1. To produce outputs based on software input.
  2. To follow a specific schema - human input, Al processing, and output for human consumption.
  3. To interpret human instructions and control software systems without necessarily producing output for human consumption.
  4. To interpret human instructions and control software systems always producing output for human consumption.
Correct answer: C, D
Question 3
What capabilities does the Exploration and Development feature of the generative Al hub provide? Note: There are 2 correct answers to this question.
  1. Al playground and chat
  2. Automatic model selection
  3. Develop and debug ABAP code
  4. Prompt editor and management
Correct answer: A, D
Question 4
What does SAP recommend you do before you start training a machine learning model in SAP AI Core? Note: There are 3 correct answers to this question.
  1. Configure the training pipeline using templates.
  2. Define the required infrastructure resources for training.
  3. Perform manual data integration with SAP HANA.
  4. Configure the model deployment in SAP Al Launchpad.
  5. Register the input dataset in SAP AI Core.
Correct answer: A, B, E
Question 5
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.
  1. Proprietary LLMs exclusively
  2. Code generation to extend SAP BTP applications
  3. Tools for prompt engineering and experimentation
  4. Integration of foundation models into applications
Correct answer: B, C
Question 6
Which of the following is a benefit of using Retrieval Augmented Generation?
  1. It allows LLMs to access and utilize information beyond their initial training data.
  2. It enables LLMs to learn new languages without additional training.
  3. It eliminates the need for fine-tuning LLMs for specific tasks.
  4. It reduces the computational resources required for language modeling.
Correct answer: A
Question 7
What are the benefits of SAP's generative Al hub? Note: There are 2 correct answers to this question.
  1. Accelerate Al development with flexible access to a broad range of models
  2. Provide libraries for no-code development
  3. Build custom Al solutions and extend SAP applications
  4. Send your data to various LLM providers for training feedback
Correct answer: A, C
Question 8
What is the primary function of the generative Al hub in SAP's Al Foundation?
  1. To serve as an abstraction layer to access a range of foundation Al models
  2. To provide ready-to-use Al services for document processing
  3. To store embeddings of unstructured data for semantic data retrieval
  4. To manage the Al lifecycle efforts end-to-end
Correct answer: A
Question 9
How can few-shot learning enhance LLM performance?
  1. By enhancing the model's computational efficiency
  2. By providing a large training set to improve generalization
  3. By reducing overfitting through regularization techniques
  4. By offering input-output pairs that exemplify the desired behavior
Correct answer: D
Question 10
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.
  1. It operates independently of SAP's partners and ecosystem.
  2. It ensures relevant, reliable, and responsible business Al.
  3. It only supports traditional machine learning models.
  4. It provides instant access to a wide range of large language models (LLMs).
Correct answer: B, D
Question 11
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
- "urgency" as one of {{?urgency}}
- "sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}}
Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t
import random random.seed(42) k = 3
examples random. sample (dev_set, k) example_template = """ {example_input} examples
'\n---\n'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[
f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])
  1. Generate random examples for language model training
  2. Evaluate the performance of a language model using few-shot learning
  3. Train a language model from scratch
  4. Preprocess a dataset for machine learning
Correct answer: B
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