C1000-185 : IBM watsonx Generative AI Engineer - Associate

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ここでは、Xhs1991 C1000-185試験資料は、あなたのIBM C1000-185証明試験を通過することおよびIBM認定証明書を得ることを手助けします。我々の試験資材は、技術的な正確さで最も高い標準に書かれます。そして、C1000-185の試験質問と回答は、経験豊かな専門家によって編集されて、ヒット率の99.9%を持ちます。もしあなたが、IBM C1000-185試験の準備をするのに良いアイデアを全然持っていないならば、Xhs1991はあなたの最もよい選択です。

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

1. You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality.
Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)

A) Use beam search with a beam width of 1 to minimize computation time.
B) Apply top-k sampling with k = 50 to ensure diverse and creative outputs.
C) Implement early stopping criteria based on token repetition to avoid lengthy generation.
D) Increase the model's learning rate to accelerate content generation.
E) Reduce the batch size to decrease training time and speed up generation.

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

3. A machine learning engineer is optimizing a generative AI model for creative writing. They are debating the use of soft prompts over hard prompts.
What is the primary advantage of using soft prompts in this context, despite their complexity?

A) Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.
B) Soft prompts provide more direct control over the model's behavior by offering explicit, human-readable instructions.
C) Soft prompts allow for more nuanced control of the model's behavior through learned embeddings, which can adapt to a variety of tasks without requiring explicit human intervention.
D) Soft prompts enforce stricter generation outputs due to the deterministic nature of the learned embeddings, leading to higher consistency in results.

4. You are tasked with developing a customer support system for an e-commerce platform using the Retrieval-Augmented Generation (RAG) pattern. The system needs to retrieve relevant information from a large database of product specifications, user manuals, and FAQs. You decide to use LangChain for constructing the pipeline and SingleStore as the backend for storing and querying the document embeddings. The objective is to efficiently retrieve semantically similar documents and use them as input for a generative model that crafts human-like responses.
Which of the following steps best describes the correct implementation of the RAG pattern using LangChain and SingleStore for this customer support system?

A) Store the pre-trained generative model's parameters in SingleStore and use LangChain to retrieve embeddings from the database for training the model.
B) Use LangChain to create an LLM chain that integrates with SingleStore for embedding retrieval, where the retrieved documents are used as context for generating responses.
C) Use LangChain to generate embeddings and directly store the generated responses in SingleStore without any retrieval mechanism.
D) Use LangChain to fine-tune the generative model, then store the embeddings in SingleStore, and use SQL queries to retrieve documents based on exact keyword matches.

5. IBM Watsonx's Prompt Lab offers various options to refine prompts for generating more effective AI outputs.
Which of the following is an accurate description of an editing option available in Prompt Lab?

A) Users can apply real-time machine learning to modify the underlying model parameters within Prompt Lab.
B) Prompt Lab allows users to experiment with prompt structures, such as adjusting token limits or adding contextual instructions, to improve responses.
C) Users can use Prompt Lab to train the AI model on new datasets and retrain it based on prompt performance.
D) Users can disable the model's access to certain pre-trained knowledge domains within Prompt Lab to focus its output on specific areas.

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