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IBM Certified watsonx Generative AI Engineer - Associateの基礎準備資料問題集には、IBM Certified watsonx Generative AI Engineer - Associate C1000-185試験を受けるために必要なすべての材料が含まれています。詳細は、正確で論理的なものを作成するために業界の経験を常に使用しているIBM Certified watsonx Generative AI Engineer - Associate によって研究と構成されています。

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JPNTestは、1週間で完璧に認定試験を準備することができる、忙しい受験者に最適な問題集を提供しております。 C1000-185の問題集は、IBMの専門家チームがベンダーの推奨する授業要綱を深く分析して作成されました。弊社のC1000-185学習材料を一回のみ使用するだけで、IBM認証試験に合格することができます。

C1000-185はIBMの重要な認証であり、あなたの専門スキルを試す認定でもあります。受験者は、試験を通じて自分の能力を証明したいと考えています。 JPNTest IBM watsonx Generative AI Engineer - Associate は、IBM Certified watsonx Generative AI Engineer - Associateの380の問題と回答を収集して作成しました。IBM watsonx Generative AI Engineer - Associateの知識ポイントをカバーし、候補者の能力を強化するように設計されています。 JPNTest C1000-185受験問題集を使用すると、IBM watsonx Generative AI Engineer - Associateに簡単に合格し、IBM認定を取得して、IBMとしてのキャリアをさらに歩むことができます。

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IBM watsonx Generative AI Engineer - Associate 認定 C1000-185 試験問題:

1. You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai's large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents.
What is the best approach to achieve this integration?

A) Use watsonx.ai's LLM to both retrieve and summarize the documents, bypassing Watson Discovery.
B) Use Watson Discovery for summarizing documents and watsonx.ai's LLM for only retrieving relevant content from the documents.
C) Use watsonx.ai's LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.
D) Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai's LLM for summarization through API calls.

2. You are tasked with designing a prompt for an IBM Watsonx model that will automate customer support responses for a company that sells technical products. The use case requires the model to respond accurately to specific customer inquiries about product troubleshooting.
What is the most effective prompt to use for this scenario?

A) "Help a customer resolve an issue with our product."
B) "Based on the following error description, provide a step-by-step solution: 'The device won't power on even after charging for 3 hours.' Be specific and concise in your response."
C) "Write a generic response to help customers with any issue they may have."
D) "Write a creative explanation of how to fix our product when it fails to function properly."

3. A financial institution is using a generative AI model to create reports based on transaction data. During deployment, the institution notices that the model sometimes fabricates trends or patterns that do not exist in the underlying data. This is an example of a hallucination.
Which of the following techniques would best minimize this risk during inference?

A) Disable the model's autoregressive capability to prevent it from generating future predictions.
B) Use a retrieval-augmented generation (RAG) model that incorporates external financial data into the generation process.
C) Increase the top-p value to ensure more tokens are considered during generation.
D) Reduce the model size to decrease its capacity to hallucinate complex patterns.

4. You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?

A) Setting the maximum tokens to 50 and minimum tokens to 150
B) Setting the minimum tokens to 150 and maximum tokens to 50
C) Setting the minimum tokens to 50 and maximum tokens to 150
D) Setting no minimum token value but configuring the maximum tokens to 150

5. When working with IBM Watsonx Generative AI models, it's important to configure proper stopping criteria to control when the model should terminate the text generation process. You are developing a chatbot where responses should stay within a manageable length without losing coherence.
Which configuration best represents an effective stopping criterion to ensure coherent responses without abrupt truncation?

A) Greedy decoding with temperature set to 2.0 and no stop sequence.
B) Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".
C) Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.
D) Greedy decoding with no stop sequence and maximum tokens set to 200.

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