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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are implementing a RAG system that uses vector embeddings to retrieve relevant information from a large corpus of documents. To store and efficiently query the vector embeddings, you need to choose a suitable vector database.
Which of the following is a key capability that a vector database must support to ensure efficient retrieval in this context?

A) The ability to store and query data only in JSON format
B) Automatic generation of vector embeddings from raw text data
C) The ability to store sparse vectors instead of dense vectors
D) Built-in support for Approximate Nearest Neighbor (ANN) search algorithms

2. Which prompt engineering strategy is most effective for reducing the risk of generating biased content in a generative AI model?

A) Using a larger number of examples in the prompt to steer the model towards safer and more neutral outputs.
B) Adjusting the model's temperature to a lower value to generate more deterministic and consistent outputs.
C) Including specific disclaimers or guidelines in the prompt instructing the model to avoid biased language.
D) Designing prompts that explicitly ask for balanced perspectives, such as "Provide both pros and cons of this issue."

3. Which of the following is the most effective approach when planning for data elements to optimize application usage in IBM watsonx generative AI models?

A) Aggregate similar data types to minimize the need for feature selection during model optimization.
B) Ensure that all features are included in the model to capture as much data context as possible, regardless of their relevance.
C) Use feature selection techniques to reduce dimensionality, enhancing model efficiency without sacrificing performance.
D) Select only high-dimensional features to increase the complexity of the model and boost its predictive power.

4. You are using IBM Watsonx to generate answers for a customer service chatbot. However, the responses generated sometimes include fabricated details that are inaccurate (hallucinations).
Which of the following strategies will best mitigate the risk of hallucination when designing your prompt?

A) Ask open-ended questions without providing reference information: "What are the latest features of our product?"
B) Supply explicit product details and constraints in the prompt: "List the latest features of the XYZ smartphone released in 2024, including its AI-powered camera and battery-saving mode."
C) Rely on the AI model to fact-check and correct hallucinations in its responses automatically: "What can you tell me about our product?"
D) Use subjective language in your prompts: ":Can you describe why our product is the best on the market?"

5. You are fine-tuning a generative AI model in IBM Watsonx and need to define appropriate stopping criteria to ensure the generated text is relevant and coherent.
Which of the following would be an example of a valid stopping criterion for a text generation task?

A) The model will stop generating text once the average probability of each word in the output falls below a 50% threshold.
B) The model will stop generating text once a predefined number of characters is reached, ensuring that the output does not exceed a set length.
C) The model will stop generating text once it detects a repetitive sequence of words or phrases, automatically cutting off redundancy.
D) The model will stop generating text when it encounters a special end-of-sequence token or punctuation such as a period or question mark.

Solutions: