IBM C1000-185 Exam Questions : 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. After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

A) Small difference between training and validation loss
B) Low validation loss
C) Large difference between training and validation accuracy
D) Low training loss
E) High training accuracy

2. A company is using a Retrieval-Augmented Generation (RAG) system to enhance its question-answering model by incorporating relevant documents from a knowledge base. They need to generate vector embeddings for these documents and the queries using a pretrained transformer-based model.
What is the most crucial aspect of generating vector embeddings for effective retrieval in this scenario?

A) Use a different transformer model for document embeddings and query embeddings to ensure diversity in representations.
B) Generate vector embeddings with a static embedding model that does not account for fine-tuning on the specific dataset.
C) Ensure that both documents and queries are encoded using the same embedding model.

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

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

5. You are designing a prompt template for generating personalized marketing emails using IBM Watsonx. The emails need to be engaging, personalized based on customer data, and must include a clear call to action.
Which of the following is the best structure for a prompt template that can be reused to generate such emails?

A) "Write an email promoting the following product, ensuring to highlight customer-specific preferences and recommend personalized products or offers. Include a clear call to action."
B) "Generate a formal email to promote the latest offers, focusing on the technical details of the products and avoiding any emotional appeal."
C) "Generate a generic marketing email promoting our products. Make it professional but avoid using customer-specific details."
D) "Create a marketing email that lists the features of our latest products. No need to include any personalized information."

Solutions: