SAP C-AIG-2412 New Exam Bootcamp, C-AIG-2412 Exam Cost
SAP C-AIG-2412 New Exam Bootcamp, C-AIG-2412 Exam Cost
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Tags: C-AIG-2412 New Exam Bootcamp, C-AIG-2412 Exam Cost, Upgrade C-AIG-2412 Dumps, C-AIG-2412 Valid Exam Camp Pdf, C-AIG-2412 Exam Material
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>> SAP C-AIG-2412 New Exam Bootcamp <<
100% Pass Quiz C-AIG-2412 - Updated SAP Certified Associate - SAP Generative AI Developer New Exam Bootcamp
One of the best ways to prepare for the SAP C-AIG-2412 exam is to study the SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) exam questions. Familiarizing yourself with the C-AIG-2412 certification using practice test on real-world data sets can help you build your confidence and prepare you for the exam. Additionally, taking C-AIG-2412 Exam Questions and quizzes can help you identify areas where you need to improve and gauge your understanding of the material.
SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q51-Q56):
NEW QUESTION # 51
What are some components of the training pipeline in SAP AI Core? Note: There are 2 correct answers to this question.
- A. The SAP HANA database for model storage
- B. Input datasets stored in a hyperscaler object store
- C. Executables that define the training process
- D. Automated deployment to Kubernetes clusters
Answer: B,C
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 # 52
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.
- A. Proprietary LLMs exclusively
- B. Integration of foundation models into applications
- C. Code generation to extend SAP BTP applications
- D. Tools for prompt engineering and experimentation
Answer: C,D
NEW QUESTION # 53
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.
- A. To follow a specific schema - human input, Al processing, and output for human consumption.
- B. To interpret human instructions and control software systems always producing output for human consumption.
- C. To produce outputs based on software input.
- D. To interpret human instructions and control software systems without necessarily producing output for human consumption.
Answer: B,D
Explanation:
* C. To interpret human instructions and control software systems without necessarily producing output for human consumption.This is a key area where generative AI is breaking new ground. Think of it as AI acting as a "middleman" between you and software. Here are some examples:
* Automating complex tasks:You could tell the AI to "optimize this database for performance" or
"find and fix security vulnerabilities in this code." The AI would then interact with the software systems to carry out these instructions, without needing to show you every step or result.
* Controlling robots or IoT devices:Imagine instructing an AI to "adjust the lighting in the meeting room" or "have the robot retrieve the package from the warehouse." The AI translates your instructions into actions for those systems.
* Managing cloud resources:AI could dynamically allocate cloud resources based on your needs, scaling them up or down without your direct intervention.
* D. To interpret human instructions and control software systems always producing output for human consumption.This is more in line with traditional chatbot interactions, but with a broader scope. It's about AI generating outputs that are directly useful or informative for humans. Examples include:
* Creating realistic images or videos:Based on your description, the AI could generate a photorealistic image of a new product design or a short video clip for a marketing campaign.
* Writing different kinds of creative text formats:AI can generate stories, poems,articles, summaries, and even code, all tailored to your specifications.
* Providing personalized recommendations:AI can analyze your preferences and provide recommendations for products, services, or information.
Why the other options are incorrect:
* A. To produce outputs based on software input.This is a general capability of AI, not something specific to generative AI or beyond chatbots. Many AI systems analyze software input (like sensor data or log files) to produce outputs.
* B. To follow a specific schema - human input, AI processing, and output for human consumption.
This describes the basic interaction pattern of many AI systems, including chatbots. It's not something that specifically differentiates generative AI or goes beyond typical chatbot applications.
NEW QUESTION # 54
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note: There are 2 correct answers to this question.
- A. Faithfulness
- B. Carbon footprint
- C. Relevance
- D. Speed
Answer: A,C
NEW QUESTION # 55
Which of the following is a benefit of using Retrieval Augmented Generation?
- A. It reduces the computational resources required for language modeling.
- B. It allows LLMs to access and utilize information beyond their initial training data.
- C. It eliminates the need for fine-tuning LLMs for specific tasks.
- D. It enables LLMs to learn new languages without additional training.
Answer: B
NEW QUESTION # 56
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