C-AIG-2412 Dumps PDF 2025 Strategy Your Preparation Efficiently [Q39-Q57]

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C-AIG-2412 Dumps PDF 2025 Strategy Your Preparation Efficiently

Latest Verified & Correct SAP C-AIG-2412 Questions

NO.39 Why is generative Al gaining significant attention and investment in the current business landscape?
Note: There are 2 correct answers to this question.

It lowers barriers to adoption.

It can replicate complex technical skills without training or quality control.

It can run entire business operations without human intervention.

It only requires natural language skills to use.

NO.40 What is the goal of prompt engineering?

To replace human decision-making with automated processes

To craft inputs that guide Al systems in generating desired outputs

To optimize hardware performance for Al computations

To develop new neural network architectures for Al models

NO.41 What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

To simplify the process of training the embedding model

To enable the matching of different relevant passages to user queries

To improve the efficiency of encoding queries into vector representations

To reduce the storage space required for the vector database

NO.42 Which of the following executables in generative Al hub works with Anthropic models?

GCP Vertex Al

Azure OpenAl Service

SAP AI Core

AWS Bedrock

NO.43 Which of the following are features of the SAP AI Foundation? Note: There are 2 correct answers to this question.

Ready-to-use Al services

Al runtimes and lifecycle management

Open source Al model repository

Joule integration in SAP SuccessFactors

NO.44 How does SAP deal with vulnerability risks created by generative Al? Note: There are 2 correct answers to this question.

By implementing responsible Al use guidelines and strong product security standards.

By identifying human, technical, and exfiltration risks through an Al Security Taskforce.

By focusing on technological advancement only.

By relying on external vendors to manage security threats.

NO.45 What must be defined in an executable to train a machine learning model using SAP AI Core? Note:
There are 2 correct answers to this question.

Pipeline containers to be used

Infrastructure resources such as CPUs or GPUs

User scripts to manually execute pipeline steps

Deployment templates for SAP AI Launchpad

NO.46 Why is generative Al gaining significant attention and investment in the current business landscape? Note: There are 2 correct answers to this question.

It lowers barriers to adoption.

It can replicate complex technical skills without training or quality control.

It can run entire business operations without human intervention.

It only requires natural language skills to use.

NO.47 Which of the following are grounding principles included in SAP’s AI Ethics framework? Note: There are 3 correct answers to this question.

Transparency and explainability

Human agency and oversight

Avoid bias and discrimination

Maximize business profits

Store all user data for legal proceedings

NO.48 Why would a user include formatting instructions within a prompt?

To force the model to separate relevant and irrelevant output

To ensure the model’s response follows a desired structure or style

To increase the faithfulness of the output

To redirect the output to another software program

NO.49 Which of the following sequence of steps does SAP recommend you use to solve a business problem using generative Al hub?

Create a basic prompt in SAP AI Launchpad
* Evaluate various models for the problem using generative-ai-hub-sdk
* Scale the solution using generative-ai-hub-sdk
* Create a baseline evaluation method for the simple prompt
* Enhance the prompts.

Create a basic prompt in SAP AI Launchpad
* Enhance the prompts
* Create a baseline evaluation method for the simple prompt
* Evaluate various models for the problem using generative-ai-hub-sdk
* Scale the solution using generative-ai-hub-sdk

Create a basic prompt in SAP AI Launchpad
* Scale the solution using generative-ai-hub-sdk
* Create a baseline evaluation method for the simple prompt
* Enhance the prompts
* Evaluate various models for the problem using generative-ai-hub-sdk

NO.50 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> {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”])

Generate random examples for language model training

Evaluate the performance of a language model using few-shot learning

Train a language model from scratch

Preprocess a dataset for machine learning

NO.51 What are some benefits of using an SDK for evaluating prompts within the context of generative Al? Note: There are 3 correct answers to this question.

Maintaining data privacy by using data masking techniques

Creating custom evaluators that meet specific business needs

Automating prompt testing across various scenarios

Supporting low code evaluations using graphical user interface

Providing metrics to quantitatively assess response quality

NO.52 Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

The user’s questi on is used to search a knowledge base or a set of documents.

The embedding model stores the generated ans wers for future reference.

The retrieved content is combined with the LLM’s capabilities to generate a response.

The LLM directly ans wers the user’s question without accessing external information.

NO.53 What is a significant risk associated with using LLMs?

Complete elimination of human oversight in content creation

Inability to generate text in multiple languages

Potential biases in generated content

Unlimited processing power usage without cost control

NO.54 How can Joule improve workforce productivity?
Note: There are 2 correct answers to this question.

By maintaining strict adherence to data privacy regulations.

By resolving hardware malfunctions.

By offering generic task recommendations unrelated to specific roles.

By providing context-based role-specific task assistance.

NO.55 Where can you configure language models in generative Al hub?

The Configuration tab within ML Operations in SAP AI Launchpad

The Models tab in Prompt Editor

The Configuration tab of the SAP BTP cockpit

The Orchestration tab in SAP AI Launchpad

NO.56 What are some advantages of using agents in training models? Note: There are 2 correct answers to this question.

To guarantee accurate decision making in complex scenarios

To improve the quality of results

To streamline LLM workflows

To eliminate the need for human oversight

NO.57 Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

The user’s questi on is used to search a knowledge base or a set of documents.

The embedding model stores the generated ans wers for future reference.

The retrieved content is combined with the LLM’s capabilities to generate a response.

The LLM directly ans wers the user’s question without accessing external information.

SAP C-AIG-2412 Exam Syllabus Topics:

Topic	Details
Topic 1	Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.
Topic 2	SAP’s Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP’s Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.
Topic 3	SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP’s AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill that needs to be evaluated.
Topic 4	SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.