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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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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 implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.
What is the most important function of LlamaIndex in managing document retrieval?

A) LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.
B) LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.
C) LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.
D) LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.

2. You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

A) Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
B) Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
C) Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
D) Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.
E) Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.

3. In IBM Watsonx's Prompt Lab, example input prompts can be used to improve the effectiveness of generated responses.
Which of the following best describes a key benefit of utilizing example input prompts in Prompt Lab?

A) Example input prompts automatically adjust the model's training dataset for more accurate predictions.
B) Example input prompts help generate responses that are more aligned with the specific context or style intended by the user.
C) Example input prompts guarantee consistency across all outputs, regardless of variability in user-provided data.
D) Example input prompts allow the model to learn new concepts and update its knowledge base dynamically.

4. You are fine-tuning a general-purpose language model on a medical dataset to generate summaries of patient consultations. After fine-tuning, you notice that the model sometimes generates hallucinations-statements that are factually incorrect or irrelevant to the specific domain. You suspect that the fine-tuning process did not sufficiently align the model with the medical domain.
Which of the following is the most effective technique to reduce hallucinations during fine-tuning?

A) Increase the model's batch size during training
B) Add more general-purpose data to the fine-tuning dataset
C) Increase the number of layers in the model
D) Use domain-specific tokenization during fine-tuning

5. You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?

A) Set the stop sequence to a punctuation mark like ";"
B) Use multiple stop sequences, including a period ."
C) Increase the token limit to avoid premature cut-off
D) Use a more distinct and unlikely stop sequence, such as "<END>"

質問と回答：