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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are managing a generative AI model deployment in IBM Watsonx and need to implement prompt versioning to ensure traceability and reproducibility of model behavior over time.
Which of the following strategies best enables versioning of prompts during deployment?

A) Storing prompts in a flat file system and manually tracking versions.
B) Using a source control system (e.g., Git) to track prompt changes alongside model code.
C) Relying on model checkpointing to manage both model weights and prompts.
D) Disabling versioning for prompts since it is not required for generative models.

2. You are optimizing a large language model (LLM) for deployment on edge devices with limited computational resources.
To reduce the model size and improve efficiency without significantly compromising performance, which of the following quantization techniques is most appropriate for this scenario?

A) Post-training 16-bit floating point quantization
B) 32-bit floating point quantization with fine-tuning
C) Binary quantization (1-bit)
D) Post-training 8-bit integer quantization

3. Your team is tasked with enhancing a document search engine using IBM watsonx Discovery. The search engine will be used to help employees quickly find relevant internal documentation, such as policy guides, project specifications, and technical manuals. You have decided to use Retrieval-Augmented Generation (RAG) to enhance this search engine's performance. The current focus is on selecting the best retriever for the task and setting up a vector database to store document embeddings.
Which of the following retriever types is most suitable for this RAG-based search engine setup, considering the diverse and unstructured nature of the documents, and why?

A) Sparse retriever with BM25 ranking in combination with a traditional relational database.
B) Hybrid retriever that combines sparse retrieval methods with dense embeddings stored in a flat-file system.
C) Rule-based retriever using manually crafted rules to find documents based on predefined logic.
D) Sparse retriever with BM25 ranking in combination with a traditional relational database.

4. You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?

A) Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.
B) Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.
C) Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.
D) Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.

5. You are implementing techniques to ensure that an IBM Watsonx Generative AI model does not expose any personal or sensitive information (PII) in its outputs.
What is the most effective technique for excluding personal information during the inference stage of the generative AI process?

A) Train the model on synthetic data that does not include any personal information.
B) Implement real-time filtering of model outputs using regular expressions to detect and mask personal information.
C) Use a greedy decoding strategy to limit the creativity of the model and prevent unexpected outputs.
D) Use temperature tuning to control the diversity of outputs, reducing the likelihood of personal information being revealed.

Solutions: