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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テスト問題集を初めて使用したときにIBM Certified watsonx Generative AI Engineer - Associate C1000-185試験（IBM watsonx Generative AI Engineer - Associate）に合格されなかった場合は、購入料金を全額ご返金いたします。

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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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JPNTestのIBM Certified watsonx Generative AI Engineer - Associate C1000-185模擬試験問題集は、認定された対象分野の専門家と公開された作成者のみを使用して、最高の技術精度標準に沿って作成されています。

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

1. You are tasked with deploying a custom Watsonx Generative AI model for a client who requires low-latency responses and scalability to handle unpredictable traffic.
Which deployment architecture would best meet the client's requirements?

A) Deploying the model in a local on-premise data center
B) Using a serverless architecture with auto-scaling
C) Using a containerized architecture without orchestration
D) Deploying the model on a single dedicated server

2. In the context of IBM Watsonx Generative AI models, hallucinations refer to outputs where the model generates text that is factually incorrect or not grounded in the provided input or training data. Understanding the underlying causes of hallucinations is critical for maintaining the reliability of the model.
Which of the following best describes a primary cause of hallucinations in generative models?

A) The model's over-reliance on token repetition to form coherent sentences.
B) The model's training on incomplete or unstructured datasets leading to incorrect generalizations.
C) The model's use of a greedy decoding strategy without beam search.
D) The model's incapacity to follow the temperature parameter settings.

3. You are tasked with fine-tuning a pre-trained large language model (LLM) using synthetic data generated through the IBM watsonx user interface.
Which of the following steps should you follow to ensure the model is fine-tuned correctly and the synthetic data is used effectively?

A) Select the pre-trained model, generate synthetic data, and fine-tune the model using default parameters without further customization.
B) Use synthetic data as a replacement for real-world data without cross-validation or any quality control measures.
C) Directly upload synthetic data without inspecting or validating it and initiate the fine-tuning process.
D) Select the pre-trained model, generate synthetic data, inspect the generated data for quality, and fine-tune the model by adjusting hyperparameters and training settings.

4. You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?

A) Set the minimum token length high to ensure the model has enough time to fully develop its response.
B) Increase the temperature parameter to introduce more diversity and creativity into the model's output.
C) Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
D) Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.

5. You are tasked with improving the performance of a Retrieval-Augmented Generation (RAG) system in IBM watsonx. Part of this improvement involves selecting the right embedding model for document retrieval.
Which of the following is the best description of the differences between various embedding models, and how would you choose the most suitable model for your task?

A) Word2Vec, GloVe, and BERT are all embedding models, but BERT embeddings capture richer context by considering the entire sentence rather than just the local context, making it more effective for generating semantically relevant embeddings.
B) BERT embeddings are context-independent, which makes them less useful for a RAG system than Word2Vec or GloVe, which focus on learning semantic relationships between words.
C) Word2Vec embeddings capture only the syntactic relationships between words, while BERT embeddings focus on both syntax and semantic context, making BERT more suitable for complex retrieval tasks in a RAG system.
D) TF-IDF is an advanced embedding model that captures both the frequency and semantic meaning of words, making it more effective than deep learning-based models like BERT for retrieval in RAG systems.

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