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SAP C_AIG_2412 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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.

Topic 4

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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.

A. Grant access rights to the SAP BTP cockpit.
B. Provide the storage secret to access the dataset.
C. Store the dataset in a hyperscaler object store.
D. Store the dataset in the SAP HANA Vector Engine.

Answer: B,C

NEW QUESTION # 32
You want to use the orchestration service through SAP's generative-Al-hub-sdk.
What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM
11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})

A. Create the Orchestration Configuration
B. Define the Template and Default Input Values
C. Define the LLM
D. Run the Orchestration Request

Answer: C

NEW QUESTION # 33
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.

A. They enable simultaneous orchestration of Kubernetes clusters.
B. They ensure workload separation for different tenants or departments.
C. They provide isolation for datasets and Al artifacts.
D. They enhance pipeline execution speeds through workload distribution.

Answer: B,C

Explanation:
Resource groups in SAP AI Core play a vital role in managing machine learning workloads by offering mechanisms for separation and isolation, which are essential for maintaining efficiency and security.
1. Ensuring Workload Separation for Different Tenants or Departments:
* Multitenancy Support:Resource groups enable the segregation of workloads among various tenants or departments within an organization, ensuring that each unit's processes are isolated and managed independently.
* Operational Efficiency:This separation prevents interference between workloads, allowing for tailored resource allocation and management strategies that meet the specific needs of each tenant or department.

NEW QUESTION # 34
What are some functionalities provided by SAP Al Core?
Note: There are 3 correct answers to this question.

A. Monitoring and retraining models in SAP Al Core
B. Integration of Al services with business applications using a standardized API
C. Management of SAP S/4HANA cloud infrastructure
D. Orchestration of Al workflows such as model training and inference
E. Continuous delivery and tenant isolation for scalability

Answer: B,D,E

NEW QUESTION # 35
What are some drivers for the rapid adoption of generative AI?
Note: There are 2 correct answers to this question.

A. Significant hardware cost savings
B. Availability of skilled developers
C. Wide availability
D. Ease of use

Answer: C,D

NEW QUESTION # 36
......

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