IBM Certified watsonx Generative AI Engineer - Associate C1000-185

시험준비시간 최소화

IT업계 엘리트한 강사들이 퍼펙트한 C1000-185시험응시 IBM watsonx Generative AI Engineer - Associate덤프문제집을 제작하여 디테일한 C1000-185문제와 답으로 여러분이 아주 간단히 IBM watsonx Generative AI Engineer - Associate시험응시를 패스할 수 있도록 최선을 다하고 있습니다.덤프구매후 2,3일 정도만 공부하시면 바로 시험보셔도 되기에 가장 짧은 시간을 투자하여 시험에서 패스할수 있습니다.

C1000-185시험은 최근 제일 인기있는 인증시험입니다. IT업계에 종사하시는 분들은 자격증취득으로 자신의 가치를 업그레이드할수 있습니다. IBM watsonx Generative AI Engineer - Associate 시험은 유용한 IT자격증을 취득할수 있는 시험중의 한과목입니다. IBM IBM Certified watsonx Generative AI Engineer - Associate 최신버전 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다. C1000-185덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다. C1000-185시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를 공부하고 시험패스하세요.

최신 업데이트버전 C1000-185덤프

저희는 2,3일에 한번씩 C1000-185덤프자료가 업데이트 가능한지 체크하고 있습니다. IBM watsonx Generative AI Engineer - Associate덤프가 업데이트된다면 업데이트된 버전을 고객님 구매시 사용한 메일주소로 발송해드립니다. C1000-185 덤프 업데이트서비스는 구매일로부터 1년내에 유효함으로 1년이 지나면 C1000-185덤프 업데이트서비스가 자동으로 종료됩니다. IBM watsonx Generative AI Engineer - Associate덤프 무료 업데이트 서비스를 제공해드림으로 고객님께서 구매하신 C1000-185덤프 유효기간을 최대한 연장해드립니다.

퍼펙트한 서비스 제공

승진이나 연봉인상을 꿈꾸면 승진과 연봉인상을 시켜주는 회사에 능력을 과시해야 합니다. IT인증시험은 국제적으로 승인해주는 자격증을 취득하는 시험입니다. 시험을 패스하여 자격증을 취득하면 회사에서 꽃길만 걷게 될것입니다. C1000-185인증시험덤프 덤프구매전 한국어 온라인 상담서비스부터 구매후 덤프 무료 업데이트버전 제공, C1000-185인증시험 덤프 불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. IBM watsonx Generative AI Engineer - Associate인증시험 덤프는 인기덤프인데 지금까지 C1000-185덤프를 구매한후 불합격으로 인해 환불신청하신 분은 아직 한분도 없었습니다.

최신 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 무료샘플문제:

1. You are selecting a model to fine-tune using Tuning Studio for a financial application that requires high accuracy and domain-specific language understanding.
Which type of model should you select to maximize fine-tuning efficiency and performance?

A) A small pre-trained model specifically designed for open-domain tasks.
B) A model pre-trained on financial and business data but with limited language capabilities.
C) A large pre-trained language model that has been fine-tuned on generic business communication.
D) A pre-trained model that has been optimized for creative text generation.

2. You are implementing a Retrieval-Augmented Generation (RAG) system using IBM Watsonx to improve your generative AI model. The system retrieves relevant information from a large corpus and augments it into the generative process.
In this context, what role do embeddings play in a RAG-based system?

A) Embeddings are used to retrieve relevant documents by calculating the semantic similarity between user queries and the stored documents.
B) Embeddings ensure that only syntactically correct documents are retrieved, without regard to semantic content.
C) Embeddings store the entire content of documents, which is then directly passed to the generative model.
D) Embeddings reduce the size of the generative model by compressing the parameters into a smaller representation.

3. You are deploying a new version of a generative AI model in IBM Watsonx, and you want to maintain the integrity of prompt versioning throughout the deployment lifecycle.
Which of the following methods is the most effective for ensuring that the correct prompt version is used with the corresponding model version in production?

A) Hard-code the prompt within the model deployment script to ensure that the correct prompt is always used
B) Use semantic versioning for both the model and the associated prompts, and track them independently in separate systems.
C) Use the latest available prompt version for every deployment, without specifying an exact version
D) Implement model registry tags that associate a specific prompt version with each model version during deployment.

4. You are designing a Retrieval-Augmented Generation (RAG) model within IBM watsonx to assist in generating responses for a customer service chatbot. The model needs to leverage a knowledge base (KB) of articles to enhance the accuracy of responses.
Which of the following correctly describes how the RAG model can be implemented to achieve this goal?

A) The RAG model retrieves relevant knowledge from the knowledge base using a search or retrieval mechanism before generating a response, which is then influenced by this retrieved information.
B) The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.
C) The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.
D) The RAG model first generates an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.

5. You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.
Which of the following represent significant risks that should be mitigated before full deployment? (Select two)

A) The model occasionally generates offensive or inappropriate content when responding to user queries.
B) The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.
C) The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.
D) The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.
E) The model is trained on open-source financial data, which results in slower response times during inference.

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