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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are fine-tuning a machine learning model using IBM Watsonx with a dataset that includes sensitive information. You decide to enable differential privacy while generating synthetic data to ensure the privacy of individual records.
What key feature of differential privacy ensures that the synthetic data does not leak private information from the original dataset?

A) Masking sensitive data fields before creating the synthetic data, ensuring no private information is directly used.
B) Using clustering techniques to group similar data points, preventing individual-level data from being exposed.
C) Adding controlled noise to the data, ensuring that no individual's data point is easily distinguishable from aggregate data.
D) Limiting the number of data points generated to avoid overfitting the synthetic data.

2. When creating a prompt-tuned model, which of the following factors is most critical to ensure that the model generates accurate and contextually relevant responses?

A) Using a diverse set of unrelated tasks in the tuning dataset to increase model generalization.
B) Ensuring that the prompt length does not exceed the model's input token limit.
C) Matching the training data prompts as closely as possible to real-world deployment scenarios.
D) Employing low learning rates to prevent the model from adapting too quickly to new prompts.

3. In a Retrieval-Augmented Generation (RAG) system, the retriever plays a crucial role in retrieving relevant documents or passages from a knowledge base.
What is the primary function of a retriever in this architecture?

A) The retriever finds and retrieves documents from a knowledge base that are semantically similar to the input query, often using vector embeddings to compare similarity.
B) The retriever modifies the transformer-based model's weights in real-time to improve the relevance of future retrievals.
C) The retriever performs tokenization and splitting of large documents into smaller chunks, making them easier to index.
D) The retriever generates the final response based on the retrieved documents and the user's query.

4. You are training a generative AI model using IBM's Tuning Studio and want to optimize its performance. You aim to avoid both overfitting and underfitting by carefully selecting the appropriate number of epochs.
Which of the following strategies would best help you set the optimal number of epochs during the tuning process?

A) Set the number of epochs equal to the size of the dataset
B) Set a high number of epochs and use early stopping to determine the optimal point
C) Use a single epoch to avoid overfitting
D) Gradually increase the number of epochs until the loss on the training set reaches zero

5. When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?

A) Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases
B) Incorporating fairness constraints during the model's training phase
C) Using temperature control during generation to manage diversity in responses
D) Training the model on a diverse and representative dataset

Solutions: