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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q179-Q184):

NEW QUESTION # 179
You are designing a prompt for a generative AI model in a customer support chatbot. The goal is to help users troubleshoot issues with a software installation.
Which of the following prompts would be the most effective for guiding the AI to deliver clear, actionable troubleshooting steps?

A. "Guide the user through troubleshooting software installation issues, starting from common problems like insufficient disk space and incompatible OS."
B. "Tell the user what software installation is and how it works."
C. "Please list all the possible errors that occur during software installation."
D. "Can you tell the user about software installation?"

Answer: A

NEW QUESTION # 180
A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?

A. Deploying rule-based post-processing filters to validate the output
B. Switching to greedy decoding for more deterministic responses
C. Increasing the model's response diversity by adjusting top-p sampling
D. Using human-in-the-loop (HITL) methods for real-time validation

Answer: A

NEW QUESTION # 181
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 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.
B. 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.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 182
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 compresses the documents and stores them in a traditional SQL database to improve retrieval speed.
B. LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.
C. LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.
D. LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.

Answer: C

NEW QUESTION # 183
In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A. When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
B. When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
C. When the model needs to generate a strictly factual output with minimal deviation from the prompt.
D. When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.

Answer: D

NEW QUESTION # 184
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