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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?

A) Retraining the model based on emerging trends in data
B) Initial testing on synthetic datasets and model validation
C) Iterative prompt tuning based on A/B test results and feedback loops
D) Deployment to production with regular monitoring and logging

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

3. Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?

A) Post-training dynamic quantization
B) Quantization-aware training (QAT)
C) Post-training static quantization
D) Hybrid quantization

4. You are building a RAG system in IBM watsonx for a legal document retrieval platform. The platform deals with highly structured legal texts and unstructured client queries. Your task is to ensure that both structured documents and unstructured queries can be efficiently retrieved using vector embeddings generated by the system.
Which of the following actions will help you optimize the generation of vector embeddings to support accurate retrieval in the RAG system, considering the diverse data types? (Select two)

A) Generate embeddings for legal documents at the sentence level rather than the document level, as this increases precision in retrieval.
B) Fine-tune the embedding model on legal text data to ensure embeddings capture the specific structure and semantics of the documents.
C) Ensure that embeddings for legal documents are based solely on the document metadata, as it contains the most important information.
D) Use a separate embedding model for legal documents and client queries to account for differences in data structure.
E) Rely on pre-trained general-purpose embeddings without any domain-specific adaptation, as they provide broad coverage across many industries.

5. In the IBM watsonx Prompt Lab, you are tasked with improving a customer-facing chat model by editing and refining prompts.
Which of the following is an effective prompt editing option to fine-tune a chat-based generative AI model?

A) Modifying the stop sequence to ensure the model responds with shorter answers for concise customer interactions.
B) Decreasing the number of tokens allowed in the output to encourage more creative responses.
C) Increasing the temperature parameter to make the model more deterministic and consistent in its responses.
D) Altering the tone of the prompt to make it more aligned with the brand's voice and customer expectations.

Solutions: