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IBM watsonx Generative AI Engineer - Associate - C1000-185 real prep

C1000-185
  • Exam Code: C1000-185
  • Exam Name: IBM watsonx Generative AI Engineer - Associate
  • Updated: Oct 18, 2025
  • Q & A: 380 Questions and Answers
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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are optimizing the generative AI model in IBM watsonx to balance creativity and coherence. You want to use a decoding method that dynamically adjusts the token probability threshold based on cumulative probabilities, thus ensuring the model generates coherent outputs while still allowing for some creativity.
Which parameter should you adjust, and what is the optimal setting?

A) Set temperature to 1.5 and top-p to 0.9
B) Set temperature to 1 and top-p to 0.2
C) Set top-p to 0.9 and temperature to 0.7
D) Set temperature to 0 and top-p to 0.5


2. While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?

A) Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.
B) Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.
C) Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.
D) Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.


3. While preparing a fine-tuning project using IBM watsonx, you want to generate synthetic data via the User Interface (UI) to supplement your existing dataset.
Which of the following describes an option supported by IBM watsonx for synthetic data generation through the UI?

A) Uploading real-time streaming data for immediate synthetic generation
B) Directly modifying the architecture of the model to generate task-specific synthetic data
C) Manually coding data augmentation scripts within the UI
D) Using pre-built templates to generate domain-specific synthetic data


4. 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) Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.
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) Use a random subset of production data and test both versions in a local environment, as local tests always replicate the conditions of production.
D) Test the new prompt in production without monitoring and observe user feedback to gauge performance.


5. You are implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.
What is the most important function of LlamaIndex in managing document retrieval?

A) LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.
B) LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.
C) LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.
D) LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: B
Question # 5
Answer: C

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