IBM C1000-185 : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In a scenario where a large language model (LLM) is integrated into a customer support application, the model is designed to retrieve relevant product information to answer complex user queries. The dataset consists of diverse product documents, including PDFs, user manuals, and website pages.
Which of the following best describes when to use a vector database as part of the Retrieval-Augmented Generation (RAG) approach?

A) When the data consists of diverse unstructured documents, and you need to retrieve semantically similar content using dense vector representations.
B) When there is a requirement to process large volumes of streaming data in real-time, and exact matching is the priority.
C) When there is a need to perform efficient keyword-based search on highly structured documents.
D) When the dataset consists mainly of structured tabular data and relational queries.

2. You are designing a Retrieval-Augmented Generation (RAG) system that will handle real-time queries from users, using a combination of a retriever and a transformer-based generator.
Which of the following implementation details is the most critical to ensure that the system delivers responses in a timely manner while maintaining accuracy?

A) The retriever and generator should operate independently of each other to avoid communication overhead.
B) The retriever should utilize an efficient vector search algorithm with approximate nearest neighbor (ANN) techniques to balance speed and accuracy.
C) The retriever should use exact match algorithms to minimize retrieval time and complexity.
D) The system should cache previous queries and responses to avoid invoking the retriever and generator multiple times.

3. You are using IBM Watsonx to control the randomness of a language model's output by adjusting the top-k parameter.
What happens when you reduce the top-k value from 50 to 5 during text generation?

A) Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.
B) The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.
C) Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.
D) Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.

4. Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

A) Apply prompt engineering techniques to avoid triggering known biases in the model.
B) Fine-tune the model using only the training data from trusted sources, without expanding the dataset.
C) Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.
D) Use adversarial training techniques to make the model more robust against bias and poisoning.

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

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

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