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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with securing an endpoint for a generative AI model that interacts with external applications.
Which of the following practices is MOST effective in ensuring both security and stability of the model's endpoint in a production environment?

A) Allow all external applications unrestricted access to the endpoint to facilitate wider model usage.
B) Limit the number of API calls per minute, restrict prompt lengths, and apply input validation to prevent malicious queries.
C) Implement strict IP whitelisting and authentication, but allow unfiltered prompts for flexibility.
D) Focus exclusively on endpoint authentication without enforcing any rate limits to prevent user frustration.

2. When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?

A) Decreasing the value of the temperature parameter to 0.2
B) Increasing the temperature parameter to 1.5
C) Using Top-k Sampling with a k value of 100
D) Reducing the beam size in beam search from 5 to 1

3. You are building a Retrieval-Augmented Generation (RAG) system where documents are converted into embeddings. You decide to use a transformer-based model to convert your text into embeddings. The embeddings will later be used in a vector search engine. After generating the embeddings, you observe that similar documents are not being clustered closely in the vector space, leading to poor retrieval.
What could be a likely reason for this behavior, and how can you address it?

A) The model has not been fine-tuned for generating document embeddings, leading to inaccurate representations.
B) The vector search algorithm is using Euclidean distance, which is inappropriate for high-dimensional embedding spaces.
C) The embedding vectors are being generated using sentence-level embeddings instead of word-level embeddings.
D) The model's output is not normalized, which can cause issues during the vector search process.

4. You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A) Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
B) Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
C) Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
D) Ensure that the synthetic data covers edge cases as well as common industry scenarios.
E) Generate only a small amount of synthetic data to minimize computational costs and training time.

5. You are designing a prompt to converse with a model for multilingual translation.
How would you frame the prompt to translate an English business email to Japanese, ensuring that the translated email is formal and appropriate for a business setting in Japan?

A) "Translate this business email into Japanese, making sure the tone is formal and culturally appropriate for a business context."
B) "Translate this business email into Japanese using informal language."
C) "Translate the following business email into Japanese, focusing on word-for-word accuracy."
D) "Translate this business email into Japanese but do not consider the tone or formalities."

Solutions: