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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A. Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
B. Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
C. Ensure that the synthetic data covers edge cases as well as common industry scenarios.
D. Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
E. Generate only a small amount of synthetic data to minimize computational costs and training time.

Question 2

You are building a Retrieval-Augmented Generation (RAG) system where documents are converted into embeddings. You decide to use a transformer-based model to convert your text into embeddings. The embeddings will later be used in a vector search engine. After generating the embeddings, you observe that similar documents are not being clustered closely in the vector space, leading to poor retrieval.
What could be a likely reason for this behavior, and how can you address it?

A. The model has not been fine-tuned for generating document embeddings, leading to inaccurate representations.
B. The model's output is not normalized, which can cause issues during the vector search process.
C. The vector search algorithm is using Euclidean distance, which is inappropriate for high-dimensional embedding spaces.
D. The embedding vectors are being generated using sentence-level embeddings instead of word-level embeddings.

Question 3

Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?

A. Quantization-aware training (QAT)
B. Post-training static quantization
C. Post-training dynamic quantization
D. Hybrid quantization

Question 4

You are tasked with integrating a generative AI model on watsonx.ai into a custom business workflow. The workflow requires complex prompt chains and interaction with external APIs.
Which of the following best describes how you should approach the integration using watsonx.ai and LangChain?

A. Implement LangChain to handle complex multi-step workflows, using watsonx.ai's APIs to generate responses at specific stages in the chain.
B. Directly integrate the external APIs with watsonx.ai without any intermediate framework, since LangChain would add unnecessary overhead.
C. Write custom scripts to manage all prompt sequences manually, leveraging watsonx.ai's SDK to call the generative AI model at every step.
D. Use only watsonx.ai's built-in APIs and SDKs for integration, as LangChain is not required for chaining multiple prompts.

Question 5

You are tasked with optimizing a generative AI model in IBM watsonx.ai for an NLP-based application.
During the planning stage, which of the following data elements is most important to ensure the model generalizes well to real-world application usage?

A. Pre-processed data with stop words removed to reduce noise
B. High-dimensional feature vectors from a small, well-curated dataset
C. A large, diverse dataset with both structured and unstructured data
D. Only the structured data relevant to specific business rules

Solutions: