C_AIG_2412 Practice Exams and Training Solutions for Certifications [Q30-Q44] : Testking Free Dumps : http://blog.testkingfree.com

Q30. What can be done once the training of a machine learning model has been completed in SAP AI Core? Note: There are 2 correct answers to this question.

The model can be deployed in SAP HANA.

The model’s accuracy can be optimized directly in SAP HANA.

The model can be deployed for inferencing.

The model can be registered in the hyperscaler object store.

Q31. What are some components of the training pipeline in SAP AI Core?
Note: There are 2 correct answers to this question.

Input datasets stored in a hyperscaler object store

Executables that define the training process

The SAP HANA database for model storage

Automated deployment to Kubernetes clusters

Q32. What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question.

Direct deployment of Al models to SAP HANA.

Integration with non-SAP platforms like Azure and AWS.

Centralized Al lifecycle management for all Al scenarios.

Simplified model retraining and performance improvement.

Q33. How do resource groups in SAP AI Core improve the management of machine learning workloads? Note: There are 2 correct answers to this question.

They ensure workload separation for different tenants or departments.

They enhance pipeline execution speeds through workload distribution.

They enable simultaneous orchestration of Kubernetes clusters.

They provide isolation for datasets and Al artifacts.

Q34. Which technique is used to supply domain-specific knowledge to an LLM?

Domain-adaptation training

Prompt template expansion

Retrieval-Augmented Generation

Fine-tuning the model on general data

Q35. You want to download a json output for a prompt and the response.
Which of the following interfaces can you use in SAP’s generative Al hub in SAP AI Launchpad?

Chat

Prompt management

Administration

Prompt Editor

Q36. How can Joule improve workforce productivity?
Note: There are 2 correct answers to this question.

By maintaining strict adherence to data privacy regulations.

By resolving hardware malfunctions.

By offering generic task recommendations unrelated to specific roles.

By providing context-based role-specific task assistance.

Q37. What are some use cases for fine-tuning of a model? Note: There are 2 correct answers to this question.

To introduce new knowledge to a model in a resource-efficient way

To quickly create iterations on a new use case

To sanitize model outputs

To customize outputs for specific types of inputs

Q38. How can few-shot learning enhance LLM performance?

By enhancing the model’s computational efficiency

By providing a large training set to improve generalization

By reducing overfitting through regularization techniques

By offering input-output pairs that exemplify the desired behavior

Q39. 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”])

Generate random examples for language model training

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

Train a language model from scratch

Preprocess a dataset for machine learning

Q40. Which statement best describes the Chain-of-Thought (COT) prompting technique?

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

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

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

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

Q41. Match the components of a Retrieval Augmented Generation architecture to the diagram.

Q42. What are some examples of generative Al technologies?
Note: There are 2 correct answers to this question.

Al models that generate new content based on training data

Rule-based algorithms

Robotic process automation

Foundation models

Q43. What does the Prompt Management feature of the SAP AI launchpad allow users to do?

Create and edit prompts

Provide personalized user interactions

Interact with models through a conversational interface

Access and manage saved prompts and their versions

Q44. What are some drivers for the rapid adoption of generative AI? Note: There are 2 correct answers to this question.

Availability of skilled developers

Significant hardware cost savings

Wide availability

Ease of use

C_AIG_2412 Practice Exams and Training Solutions for Certifications [Q30-Q44]

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