SAP C-AIG-2412 PDF DUMPS - STUDY WHENEVER YOU WANT

SAP C-AIG-2412 PDF Dumps - Study Whenever You Want

SAP C-AIG-2412 PDF Dumps - Study Whenever You Want

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The third format of 2Pass4sure product is the desktop SAP C-AIG-2412 practice exam software. You can access the SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) practice exam after installing this software on your Windows computer or laptop. Specifications we have discussed in the paragraph of the web-based version are available in desktop C-AIG-2412 Practice Exam software.

The SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) practice test software also keeps a record of attempts, keeping users informed about their progress and allowing them to improve themselves. This feature makes it easy for C-AIG-2412 desktop-based practice exam software users to focus on their mistakes and overcome them before the original attempt. Overall, the Windows-based SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) practice test software has a user-friendly interface that facilitates candidates to prepare for the SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) exam without facing technical issues.

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q47-Q52):

NEW QUESTION # 47
What is a Large Language Model (LLM)?

  • A. A database system optimized for storing large volumes of textual data.
  • B. A gradient boosted decision tree algorithm for predicting text.
  • C. A rule-based expert system to analyze and generate grammatically correct sentences.
  • D. An Al model that specializes in processing, understanding, and generating human language.

Answer: D

Explanation:
A Large Language Model (LLM) is an advanced AI model designed to handle various natural language processing tasks.
1. Definition and Purpose:
* Processing:LLMs analyze human language to understand syntax, semantics, and context.
* Understanding:They interpret the meaning behind text, enabling comprehension of nuanced language elements.
* Generating:LLMs can produce coherent and contextually appropriate text, facilitating tasks like content creation and translation.
2. Characteristics of LLMs:
* Scale:These models are trained on vast datasets, encompassing billions of words, which enhances their language capabilities.
* Architecture:LLMs typically utilize complex neural network architectures, such as transformers, to manage and process language data effectively.
3. Applications:
* Content Generation:Creating articles, summaries, and reports.
* Language Translation:Converting text from one language to another with high accuracy.
* Conversational Agents:Powering chatbots and virtual assistants to interact with users naturally.


NEW QUESTION # 48
Which of the following executables in generative Al hub works with Anthropic models?

  • A. GCP Vertex Al
  • B. SAP AI Core
  • C. AWS Bedrock
  • D. Azure OpenAl Service

Answer: C


NEW QUESTION # 49
What are some components of the training pipeline in SAP AI Core? Note: There are 2 correct answers to this question.

  • A. Automated deployment to Kubernetes clusters
  • B. Input datasets stored in a hyperscaler object store
  • C. The SAP HANA database for model storage
  • D. Executables that define the training process

Answer: B,D

Explanation:
The training pipeline in SAP AI Core comprises several key components that facilitate the development and deployment of machine learning models.
1. Input Datasets Stored in a Hyperscaler Object Store:
* Data Storage:Input datasets are often stored in hyperscaler object stores, which provide scalable and secure storage solutions. These datasets serve as the foundational data for training machine learning models.
* Integration:SAP AI Core integrates with various hyperscaler object stores, allowing seamless access to training data during the model development process.


NEW QUESTION # 50
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.

  • A. Configure the training pipeline using templates.
  • B. Register the input dataset in SAP AI Core.
  • C. Perform manual data integration with SAP HANA.
  • D. Configure the model deployment in SAP Al Launchpad.
  • E. Define the required infrastructure resources for training.

Answer: A,B,E

Explanation:
Before initiating the training of a machine learning model in SAP AI Core, SAP recommends the following steps:
* Configure the training pipeline using templates:Utilize predefined templates to set up the training pipeline, ensuring consistency and efficiency in the training process.
* Define the required infrastructure resources for training:Specify the computational resources, such as CPUs or GPUs, necessary for the training job to ensure optimal performance.
* Register the input dataset in SAP AI Core:Ensure that the dataset intended for training is properly registered within SAP AI Core, facilitating seamless access during the training process.
These preparatory steps are crucial for the successful training of machine learning models within the SAP AI Core environment.


NEW QUESTION # 51
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note:
There are 2 correct answers to this question.

  • A. Relevance
  • B. Speed
  • C. Carbon footprint
  • D. Faithfulness

Answer: A,D

Explanation:
Evaluating the effectiveness of a Retrieval-Augmented Generation (RAG) system involves assessing specific metrics that determine the quality and reliability of the generated content.
1. Faithfulness:
* Definition:Faithfulness measures the degree to which the generated output accurately reflects the information retrieved from source documents without introducing unsupported content.
* Importance:High faithfulness ensures that the system's responses are trustworthy and based on factual data, which is crucial for applications requiring precise information dissemination.
2. Relevance:
* Definition:Relevance assesses how pertinent the generated content is to the user's query or the task at hand.
* Importance:Ensuring relevance guarantees that the system provides information that directly addresses user needs, enhancing user satisfaction and system utility.
3. Application in RAG Systems:
* Performance Evaluation:By measuring faithfulness and relevance, developers can fine-tune RAG systems to produce outputs that are both accurate and pertinent, thereby improving overall system performance.
* User Trust:Maintaining high levels of these metrics fosters user trust, as the system consistently delivers reliable and contextually appropriate information.


NEW QUESTION # 52
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