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1. What is one primary advantage of using Prompt Lab in IBM Watsonx when evaluating prompt variations for a generative AI model?

A) Prompt Lab automatically selects the best performing prompt based on predefined metrics without any user input.
B) Prompt Lab automatically generates prompts based on the dataset, requiring minimal manual input from developers.
C) Prompt Lab enables side-by-side comparisons of multiple prompt variations, allowing developers to evaluate different formulations in a systematic manner.
D) Prompt Lab allows users to lock the output for each prompt, ensuring deterministic results across different runs.

2. You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

A) Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
B) Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.
C) Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
D) Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.

3. You are testing a new version of a prompt template designed to improve the accuracy of responses from a generative model deployed on IBM Watsonx. After deploying the new prompt version, you need to ensure that it performs better or at least as well as the previous version.
Which of the following approaches provides the most reliable method for testing the performance of the new prompt template version?

A) Use a random subset of production data and test both versions in a local environment, as local tests always replicate the conditions of production.
B) Run a series of A/B tests comparing the new prompt template to the old one, using a set of predetermined metrics, such as response accuracy and completion time.
C) Test the new prompt in production without monitoring and observe user feedback to gauge performance.
D) Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.

4. You are part of a team building an AI-powered assistant that helps software developers by answering technical queries. To handle the vast amount of technical documentation efficiently, the team has chosen to implement the Retrieval-Augmented Generation (RAG) pattern. LangChain is used to construct the retrieval-generation pipeline, and SingleStore is employed to store the document embeddings. You need to ensure that the integration of LangChain with SingleStore allows for real-time retrieval of relevant technical documentation.
Which of the following configurations should be used to ensure an optimal RAG implementation with LangChain and SingleStore?

A) Use LangChain to retrieve pre-generated embeddings from SingleStore, and then fine-tune the generative model based on the retrieved embeddings to improve response accuracy.
B) Use LangChain to retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
C) Use LangChain to embed documents into a vector space and store these embeddings in SingleStore. During inference, embed the query in the same vector space and retrieve documents using vector similarity search.
D) Use LangChain to embed both queries and documents into the same vector space and store these embeddings in SingleStore. During inference, retrieve documents using SQL queries based on keyword similarity.

5. You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?

A) Increase the temperature parameter to introduce more diversity and creativity into the model's output.
B) Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
C) Set the minimum token length high to ensure the model has enough time to fully develop its response.
D) Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.

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