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C_AIG_2412 Practice Exam Questions and Answers

SAP Certified Associate - SAP Generative AI Developer

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Question # 1

Which statement best describes the Chain-of-Thought (COT) prompting technique?

Options:

A.  

Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain.

B.  

Writing a series of connected prompts creating a chain of related information.

C.  

Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.

D.  

Connecting related concepts by having the LLM generate chains of ideas.

Discussion 0
Question # 2

Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Question # 3

Which of the following is a benefit of using Retrieval Augmented Generation?

Options:

A.  

It allows LLMs to access and utilize information beyond their initial training data.

B.  

It enables LLMs to learn new languages without additional training.

C.  

It eliminates the need for fine-tuning LLMs for specific tasks.

D.  

It reduces the computational resources required for language modeling.

Discussion 0
Question # 4

What capabilities does the Exploration and Development feature of the generative Al hub provide? Note: There are 2 correct answers to this question.

Options:

A.  

Al playground and chat

B.  

Automatic model selection

C.  

Develop and debug ABAP code

D.  

Prompt editor and management

Discussion 0
Question # 5

You want to extract useful information from customer emails to augment existing applications in your company.

How can you use generative-ai-hub-sdk in this context?

Options:

A.  

Generate a new SAP application based on the mail data.

B.  

Generate JSON strings based on extracted information.

C.  

Generate random email content and send them to customers.

D.  

Train custom models based on the mail data.

Discussion 0
Question # 6

What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.

Options:

A.  

To produce outputs based on software input.

B.  

To follow a specific schema - human input, Al processing, and output for human consumption.

C.  

To interpret human instructions and control software systems without necessarily producing output for human consumption.

D.  

To interpret human instructions and control software systems always producing output for human consumption.

Discussion 0
Question # 7

What is Machine Learning (ML)?

Options:

A.  

A subset of Al that focuses on enabling computer systems to learn and improve from experience or data.

B.  

A statistical method for data processing that does not involve any Al techniques.

C.  

A form of Al that only focuses on creating new content, including text, images, sound, and videos.

D.  

A technology that equips machines with human-like capabilities such as problem-solving, visual perception, and decision-making.

Discussion 0
Question # 8

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_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"])

Options:

A.  

Generate random examples for language model training

B.  

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

C.  

Train a language model from scratch

D.  

Preprocess a dataset for machine learning

Discussion 0
Question # 9

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

Options:

A.  

Input datasets stored in a hyperscaler object store

B.  

Executables that define the training process

C.  

The SAP HANA database for model storage

D.  

Automated deployment to Kubernetes clusters

Discussion 0
Question # 10

Which of the following is a principle of effective prompt engineering?

Options:

A.  

Use precise language and providing detailed context in prompts.

B.  

Combine multiple complex tasks into a single prompt.

C.  

Keep prompts as short as possible to avoid confusion.

D.  

Write vague and open-ended instructions to encourage creativity.

Discussion 0
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