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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

A. To generate new responses based on training data, without relying on external data sources.
B. To return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
C. To perform entity recognition and classify documents based on specific keywords, ignoring the overall document meaning.
D. To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.

Question 2

You are fine-tuning a pre-trained language model on a dataset of financial news articles to improve its ability to generate summaries of financial reports. After several epochs of training, you observe that the model performs well on the training data, achieving near-perfect accuracy. However, the model's performance on the validation set is much lower, indicating potential overfitting.
What is the most effective adjustment to reduce overfitting while continuing to fine-tune the model?

A. Increase the number of epochs
B. Apply dropout during training
C. Reduce the size of the training dataset
D. Increase the learning rate

Question 3

In what situation might greedy decoding fail to generate an optimal output, even though it consistently chooses the most probable token at each step?

A. Greedy decoding is highly effective when multiple equally probable tokens are available at each step
B. Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
C. Greedy decoding guarantees the highest overall probability for the output sequence
D. Greedy decoding works best when combined with temperature scaling to increase randomness

Question 4

In a RAG system, you need to select an appropriate retriever to fetch relevant documents from a large corpus before generating an answer. You are considering different types of retrievers, including embedding-based and keyword-based retrievers.
Which of the following describes a scenario where an embedding-based retriever using a vector database is the best choice?

A. When most of the queries consist of structured queries with precise Boolean operators and relational database-style searches
B. When retrieval must rely on semantic similarity between a query and documents, even if the exact words in the query don't appear in the document
C. When exact keyword matching is required, and synonyms or contextual understanding are irrelevant
D. When documents are labeled with metadata, and only metadata needs to be searched

Question 5

In the context of generative AI and large language models, text embeddings are a key component.
What is the primary purpose of text embeddings in a retrieval-augmented generation (RAG) system, and how are they used?

A. Text embeddings generate a one-to-one mapping of words, allowing for the exact reconstruction of the original input text.
B. Text embeddings are used to randomly assign values to words in the corpus, providing variability in how the model generates text.
C. Text embeddings are used to reduce the dimensionality of the input text for faster training of the generative model.
D. Text embeddings map words and phrases to high-dimensional vectors that capture their semantic meaning, allowing for efficient document retrieval based on contextual similarity.

Solutions: