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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q229-Q234):

NEW QUESTION # 229
You are implementing a RAG system that uses vector embeddings to retrieve relevant information from a large corpus of documents. To store and efficiently query the vector embeddings, you need to choose a suitable vector database.
Which of the following is a key capability that a vector database must support to ensure efficient retrieval in this context?

A. The ability to store sparse vectors instead of dense vectors
B. Built-in support for Approximate Nearest Neighbor (ANN) search algorithms
C. The ability to store and query data only in JSON format
D. Automatic generation of vector embeddings from raw text data

Answer: B

NEW QUESTION # 230
When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?

A. Soft prompts produce more deterministic outputs because they are optimized for a specific task through manual input adjustment.
B. Soft prompts are preferable in scenarios where the task requires rigid structure and adherence to specific predefined guidelines.
C. Soft prompts allow for more human oversight by embedding specific instructions into the model's generation process.
D. Soft prompts offer higher flexibility by dynamically adjusting the model's behavior based on pre-learned embeddings rather than relying on static instructions.

Answer: D

NEW QUESTION # 231
You are tasked with deploying a versioned prompt for a customer-facing generative AI application. The prompts are iteratively improved based on feedback, and you need to ensure that each version of the prompt is tracked and accessible for rollback in case a newer version produces worse results.
Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?

A. Leverage a cloud-based deployment pipeline with integrated versioning that automates prompt rollback and audit tracking.
B. Store each version of the prompt as a separate file in a local folder and manually deploy the desired version when needed.
C. Deploy prompts directly to production without versioning and manually track changes in a spreadsheet.
D. Use a version control system like Git to track prompt changes and synchronize the latest version with the deployment environment.

Answer: D

NEW QUESTION # 232
You are working with a foundation model pre-trained on a large general-purpose dataset, and you plan to deploy it for a specialized task in healthcare-related text generation. However, before tuning the model, you want to assess whether tuning is necessary for your use case.
Which of the following is the best indicator that it is time to tune the foundation model for your task?

A. The model performs well on general datasets but fails to capture specific domain-related terminology and context.
B. The model's inference time is longer than expected, and you need to reduce latency for real-time applications.
C. You are noticing that the model occasionally makes grammar mistakes in the generated text.
D. The model's accuracy is already above 90%, but you want to achieve 95% accuracy for your task.

Answer: A

NEW QUESTION # 233
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. 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.
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. 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.
D. 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.

Answer: B

NEW QUESTION # 234
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