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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q196-Q201):
NEW QUESTION # 196
You are working on a Retrieval-Augmented Generation (RAG) system using IBM watsonx. The system needs to retrieve relevant documents based on a user's query and generate a response using a language model. To optimize retrieval, you are tasked with generating vector embeddings for documents and queries using a pre-trained model. Your goal is to ensure that the embeddings are semantically meaningful to improve the retrieval accuracy.
Which of the following steps should be taken to ensure the vector embeddings are correctly generated and effective for document retrieval in a RAG system? (Select two)
- A. Generate embeddings for documents only and skip embeddings for user queries, relying on traditional keyword-based retrieval for queries.
- B. Normalize the vector embeddings after generation to ensure they are comparable during retrieval.
- C. Use a generative language model to generate embeddings without any fine-tuning, as it captures all the necessary context.
- D. Use a pre-trained model designed specifically for embedding generation rather than general-purpose language models.
- E. Manually adjust the embedding vectors to emphasize certain keywords that are more important for retrieval.
Answer: B,D
NEW QUESTION # 197
You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?
- A. Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.
- B. Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.
- C. Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
- D. Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
Answer: B
NEW QUESTION # 198
When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?
- A. Reducing the beam size in beam search from 5 to 1
- B. Decreasing the value of the temperature parameter to 0.2
- C. Using Top-k Sampling with a k value of 100
- D. Increasing the temperature parameter to 1.5
Answer: B
NEW QUESTION # 199
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 no stop sequence and maximum tokens set to 200.
- B. Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.
- C. Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".
- D. Greedy decoding with temperature set to 2.0 and no stop sequence.
Answer: B
NEW QUESTION # 200
You are working with IBM Watsonx and have developed a custom machine learning model using Watson Studio. Now, you need to deploy the model so that it can be accessed via an application.
Which step is NOT required when deploying the custom model for application access?
- A. Defining a scoring endpoint for the model in Watson Machine Learning
- B. Verifying model accuracy before deployment.
- C. Assigning appropriate IAM roles for users who need access to the model
- D. Registering the model in Watson Machine Learning (WML)
Answer: B
NEW QUESTION # 201
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