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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents.
Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?

A) Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.
B) Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
C) Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
D) Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.

2. You are tasked with developing a system that uses a vector database to store embeddings generated from a large corpus of documents. The system should be able to perform fast and efficient nearest neighbor search while balancing accuracy and speed. Given the large volume of data and the need for scalability, you are considering different indexing strategies offered by vector databases.
Which of the following indexing techniques is the most appropriate for balancing search accuracy and speed in high-dimensional vector space, and why?

A) Rely on full-text indexing of documents and avoid vector search altogether.
B) Linear search over the entire vector dataset.
C) Exact nearest neighbor (ENN) search using KD-trees.
D) Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.

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

4. IBM Watsonx's Prompt Lab offers various options to refine prompts for generating more effective AI outputs.
Which of the following is an accurate description of an editing option available in Prompt Lab?

A) Users can use Prompt Lab to train the AI model on new datasets and retrain it based on prompt performance.
B) Users can apply real-time machine learning to modify the underlying model parameters within Prompt Lab.
C) Prompt Lab allows users to experiment with prompt structures, such as adjusting token limits or adding contextual instructions, to improve responses.
D) Users can disable the model's access to certain pre-trained knowledge domains within Prompt Lab to focus its output on specific areas.

5. After completing a prompt-tuning experiment, you notice that the model's accuracy in generating relevant responses is high, but the fluency and grammatical correctness of the outputs seem to be suboptimal.
What statistical metric would most directly indicate this issue, and what action should you take to improve the output?

A) Perplexity score; apply additional language model fine-tuning on grammatical correctness.
B) F1 score; increase the training dataset size to improve overall accuracy.
C) ROUGE score; adjust the token generation limit to ensure longer outputs.
D) BLEU score; improve prompt engineering to ensure that the model focuses on fluency.

Solutions: