Free PDF C1000-185 - Useful Valid Dumps IBM watsonx Generative AI Engineer - Associate Free
Free PDF C1000-185 - Useful Valid Dumps IBM watsonx Generative AI Engineer - Associate Free
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q65-Q70):
NEW QUESTION # 65
You are implementing a Retrieval-Augmented Generation (RAG) system to enhance a large language model's (LLM) ability to answer questions based on an external document store.
What role do embeddings play in the RAG architecture, and how can you optimize them for more relevant document retrieval? (Select two)
- A. Increasing the dimensionality of embeddings always improves retrieval accuracy by providing a more detailed representation of the documents.
- B. Embeddings are vector representations of text, used to measure the similarity between queries and documents.
- C. Fine-tuning the embedding model with task-specific data can lead to more accurate retrievals.
- D. Embeddings are only used during the generation phase of RAG to enhance language model outputs.
- E. The cosine similarity metric is typically less effective than Euclidean distance for comparing embeddings in RAG systems.
Answer: B,C
NEW QUESTION # 66
In the context of prompt engineering for IBM Watsonx Generative AI, which of the following is the most accurate description of a prompt variable?
- A. A prompt variable is a placeholder within a prompt template that can be replaced with specific input values during execution.
- B. A prompt variable is a function that allows real-time feedback from the model to modify the prompt after generation.
- C. A prompt variable is a fixed string that the AI uses to refine its generative process for more context-aware responses.
- D. A prompt variable is a predefined input that alters the architecture of the AI model during runtime.
Answer: A
NEW QUESTION # 67
In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?
- A. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
- B. Embeddings are used to directly generate the textual responses in the output.
- C. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.
- D. Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.
Answer: C
NEW QUESTION # 68
You are tasked with deploying a suite of AI assets, including a pretrained generative language model and a set of custom-trained models. Your company operates in an environment where scalability and adaptability are key, as customer needs vary significantly across regions and sectors. You need to ensure that the deployment of these AI assets can meet varying demand, maintain performance, and allow for customization based on specific client needs.
Which deployment strategy best balances scalability, performance, and adaptability for this suite of AI assets?
- A. Deploy all AI assets as a single monolithic model on a multi-GPU setup, allowing scalability across multiple customers without the need for further customization.
- B. Deploy the AI assets on edge devices near the customer, reducing latency and ensuring consistent performance without relying on cloud infrastructure.
- C. Containerize each AI asset separately, deploy them as microservices on a cloud platform, and use load balancing to manage varying demand across regions.
- D. Fine-tune the pretrained model for each client individually, deploy as separate models for each use case, and store them locally for each region.
Answer: C
NEW QUESTION # 69
When designing a generative AI system to minimize the risk of producing hate speech or abusive content, which of the following strategies in prompt engineering is the most effective?
- A. Implement real-time content moderation at the output level rather than relying on input prompts alone.
- B. Use offensive words in prompts to test the model's robustness in handling them.
- C. Ensure that the model is trained on a larger dataset, even if the dataset contains diverse viewpoints, including some controversial opinions.
- D. Use strict prompt filtering to block any input that contains offensive words.
Answer: A
NEW QUESTION # 70
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