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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with integrating IBM watsonx with a third-party customer relationship management (CRM) system to enhance customer interactions through conversational AI.
Which of the following approaches best ensures real-time responses while minimizing the latency caused by data exchange between systems?

A) Use IBM watsonx API for batch processing of customer requests, triggering the CRM system via a scheduled job.
B) Set up periodic data synchronization between the CRM system and IBM watsonx using FTP file transfers.
C) Utilize watsonx's real-time streaming API with pre-configured webhooks in the CRM system for instant data retrieval and response.
D) Employ an asynchronous message queue between IBM watsonx and the CRM system to handle customer requests.

2. When preparing a dataset for fine-tuning a large language model for a named entity recognition (NER) task, which of the following preprocessing steps is most critical for ensuring accurate entity classification?

A) Randomly shuffle the dataset before training to increase diversity
B) Ensure proper tokenization of the dataset according to the model's vocabulary
C) Remove rare entities to improve model performance on common entities
D) Use sentence segmentation to isolate each named entity in its own sentence

3. 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) Binary quantization (1-bit)
C) Post-training 8-bit integer quantization
D) 32-bit floating point quantization with fine-tuning

4. You are working with a healthcare provider to deploy a generative AI model that assists in diagnosing patient conditions based on medical history and symptoms. The healthcare provider is concerned about ethical use, bias, and compliance with health data regulations such as HIPAA.
Which governance approach is the most critical to ensure both compliance and ethical AI deployment?

A) Apply a federated learning approach to train the model without directly accessing the healthcare provider's patient data.
B) Use a cloud-based infrastructure with HIPAA certification to deploy the model, ensuring compliance with healthcare regulations.
C) Ensure data encryption during model inference to protect patient information from unauthorized access.
D) Implement a bias detection framework to identify and mitigate any biases present in the model's outputs.

5. You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

A) Use beam search with a high beam width to generate the most likely sequence.
B) Increase the model's temperature to allow for more diverse outputs.
C) Reduce the maximum token limit and set a higher repetition penalty.
D) Implement a post-processing step that filters for factual accuracy using a knowledge base.

Solutions: