FREE PDF QUIZ SAP - C_AIG_2412 - SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER UPDATED EXAM TOPICS

Free PDF Quiz SAP - C_AIG_2412 - SAP Certified Associate - SAP Generative AI Developer Updated Exam Topics

Free PDF Quiz SAP - C_AIG_2412 - SAP Certified Associate - SAP Generative AI Developer Updated Exam Topics

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SAP C_AIG_2412 Exam Syllabus Topics:

TopicDetails
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 (Q59-Q64):

NEW QUESTION # 59
What are some components of the training pipeline in SAP AI Core?
Note: There are 2 correct answers to this question.

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

Answer: A,B


NEW QUESTION # 60
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"])

  • A. Train a language model from scratch
  • B. Evaluate the performance of a language model using few-shot learning
  • C. Preprocess a dataset for machine learning
  • D. Generate random examples for language model training

Answer: B


NEW QUESTION # 61
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 enhance pipeline execution speeds through workload distribution.
  • B. They enable simultaneous orchestration of Kubernetes clusters.
  • C. They ensure workload separation for different tenants or departments.
  • D. They provide isolation for datasets and Al artifacts.

Answer: C,D


NEW QUESTION # 62
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

  • A. To improve the efficiency of encoding queries into vector representations
  • B. To reduce the storage space required for the vector database
  • C. To enable the matching of different relevant passages to user queries
  • D. To simplify the process of training the embedding model

Answer: C

Explanation:
In Retrieval-Augmented Generation (RAG) systems, splitting documents into smaller overlapping chunks is a crucial preprocessing step that enhances the system's ability to match relevant passages to user queries.
1. Purpose of Splitting Documents into Smaller Overlapping Chunks:
* Improved Retrieval Accuracy:Dividing documents into smaller, manageable segments allows the system to retrieve the most relevant chunks in response to a user query, thereby improving the precision of the information provided.
* Context Preservation:Overlapping chunks ensure that contextual information is maintained across segments, which is essential for understanding the meaning and relevance of each chunk in relation to the query.
2. Benefits of This Approach:
* Enhanced Matching:By having multiple overlapping chunks, the system increases the likelihood that at least one chunk will closely match the user's query, leading to more accurate and relevant responses.
* Efficient Processing:Smaller chunks are easier to process and analyze, enabling the system to handle large documents more effectively and respond to queries promptly.


NEW QUESTION # 63
Where can you configure language models in generative Al hub?

  • A. The Models tab in Prompt Editor
  • B. The Configuration tab of the SAP BTP cockpit
  • C. The Orchestration tab in SAP AI Launchpad
  • D. The Configuration tab within ML Operations in SAP AI Launchpad

Answer: D


NEW QUESTION # 64
......

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