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IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

2. When deploying AI assets in a deployment space, what is the most critical benefit of using deployment spaces in a large-scale enterprise environment?

A) Isolated environments to manage and monitor multiple model versions
B) Better data labeling quality through automated labeling tools
C) Faster training times due to streamlined compute resources
D) Improved model accuracy through hyperparameter tuning

3. A financial institution is deploying a generative AI model to generate loan approval recommendations based on applicant profiles, including factors like income, credit score, and employment history. The organization is concerned about ensuring that the model does not introduce bias in its recommendations, particularly related to gender and race. You have been asked to design a process to evaluate the model's inferences during deployment and mitigate any potential bias.
Which method would be most effective for evaluating the model's inferences for bias in this deployment scenario?

A) Manually review all loan decisions generated by the model for signs of bias before releasing them to customers.
B) Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods.
C) Implement a fairness audit, where a sample of the model's inferences is checked for disparate impact across protected groups such as gender and race.
D) Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race.

4. You are enhancing an existing relational database system to support vector-based similarity search, integrating RAG into your infrastructure.
Which of the following technologies or approaches represents a valid method for extending a traditional SQL database to handle vector embeddings and similarity searches?

A) Using a full-text indexing engine, such as Elasticsearch, to store the vector embeddings
B) Using graph database extensions to enable vector embeddings within a SQL database
C) Normalizing vector embeddings into relational tables with foreign key constraints
D) Adding a plugin that provides support for k-nearest neighbors (k-NN) search

5. While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.
Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?

A) Breaking complex prompts into simpler, sequential prompts.
B) Including multiple tasks in a single prompt to maximize efficiency.
C) Switching from a narrative-style prompt to a bulleted list format.
D) Using stop tokens early in the prompt to minimize generation length.

Solutions: