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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?

A) Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
B) Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
C) Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.
D) Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.

2. In a project where watsonx.ai is deployed as the core generative model for text generation, you need to augment its capabilities by integrating it with IBM Watson Discovery to access a large corpus of documents for fact-checking and data retrieval.
What is the best way to integrate these two services to ensure that generated responses are both creative and factual?

A) Use watsonx.ai to generate all responses, and periodically train the model using data indexed by Watson Discovery.
B) Deploy a custom API layer that combines watsonx.ai with Watson Assistant, letting the latter manage queries and retrieve documents from Watson Discovery on demand.
C) Train watsonx.ai to automatically search Watson Discovery for factual information during the text generation process, eliminating the need for an external integration layer.
D) Have Watson Discovery preprocess user inputs to identify relevant documents, and then feed the retrieved information into watsonx.ai as a context for text generation.

3. 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) Disabling versioning for prompts since it is not required for generative models.
C) Using a source control system (e.g., Git) to track prompt changes alongside model code.
D) Relying on model checkpointing to manage both model weights and prompts.

4. When conducting prompt engineering to reduce model risks related to hate speech and abusive content, which of the following strategies is least likely to be effective?

A) Modifying the prompt to include explicit instructions for civil language
B) Creating a reward model during Reinforcement Learning to penalize hate speech outputs
C) Increasing the temperature during text generation to encourage more creative outputs
D) Applying ethical guidelines during the fine-tuning of the model to prioritize inclusive language

5. You are tasked with building a Retrieval-Augmented Generation (RAG) system for answering legal questions. The legal documents vary significantly in complexity and structure.
How would you optimize embeddings in this domain to ensure the system retrieves the most relevant documents? (Select two)

A) Train a domain-specific embedding model using legal documents to better capture the nuances of legal terminology.
B) Apply dimensionality reduction techniques like PCA to compress embeddings and improve retrieval speed.
C) Rely solely on word-level embeddings to capture the meaning of legal phrases and concepts.
D) Use an unsupervised learning approach to generate embeddings, as labeled data is not necessary for improving retrieval performance.
E) Integrate additional metadata (e.g., document date, author) into the embedding representation to improve retrieval.

Solutions: