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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are building a question-answering system using a Retrieval-Augmented Generation (RAG) architecture. You are deciding whether to incorporate a vector database into the system to handle the document embeddings.
Under which of the following circumstances is the use of a vector database most appropriate?

A) When real-time similarity search over high-dimensional embeddings is needed for large-scale unstructured text data
B) When the text corpus consists entirely of predefined categories that can be handled by simple keyword matching algorithms
C) When the corpus consists mainly of short, structured text like JSON records and traditional SQL indexing will suffice
D) When the data consists primarily of binary files such as images and videos, and full-text search is required

2. A healthcare organization is deploying a generative AI model to assist doctors in generating patient reports based on medical notes and test results. The organization has strict compliance requirements regarding patient data privacy (e.g., HIPAA in the U.S.) and needs to ensure that the model outputs are governed under their AI policies. You are tasked with integrating AI governance policies into the deployment to meet both ethical and legal standards.
Which AI governance measure is most important to implement during the deployment phase of this AI solution, given the healthcare organization's need for compliance with patient privacy regulations?

A) Deploying an explainability module to allow doctors to see the reasons behind each AI-generated suggestion, ensuring ethical use of the model.
B) Implementing a system to track and log all model-generated inferences, flagging any that suggest potential bias or non-compliance with privacy regulations.
C) Requiring doctors to manually review every AI-generated report before it is finalized to meet compliance with medical standards.
D) Ensuring that the model's training data includes only anonymized patient records to avoid potential data breaches.

3. You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?

A) Use a prompt that explicitly asks for neutrality across demographic groups.
B) Lower the temperature to reduce randomness in the model's response.
C) Switch to using deterministic (greedy) decoding to ensure more consistent outputs
D) Incorporate additional training data from underrepresented demographic groups.

4. You are working as a generative AI engineer and have developed a custom large language model (LLM) optimized for a specific use case. You are tasked with deploying this model on the IBM Watsonx platform.
Which of the following steps is most essential to ensure the successful deployment of your custom model, given that the model uses a third-party transformer architecture?

A) Modify the model to use IBM's proprietary transformer architecture, as third-party architectures are not supported by Watsonx.
B) Containerize the model using Docker or an equivalent containerization tool, ensuring that all required dependencies, such as transformers, tokenizers, and necessary packages, are included.
C) Set up auto-scaling in the IBM Watsonx environment to handle large numbers of simultaneous model inference requests.
D) Ensure that the model's training data is in a proprietary IBM format, as only Watsonx-specific formats are supported for custom model deployments.

5. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
B) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
C) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
D) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.
E) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.

Solutions: