IBM C1000-185 test insides dumps : 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. You are fine-tuning a generative model to generate text-based responses in a customer service chatbot. You want to ensure the responses are concise and relevant, without causing the model to produce overly long or irrelevant output.
Which of the following parameters and stopping criteria would be most effective for achieving this goal?

A) Set a low top-k value and implement a repetition penalty with a low maximum token limit.
B) Increase the temperature to 1.5 and set a high maximum token limit.
C) Use beam search decoding with a low beam width and a high repetition penalty.
D) Use greedy decoding with no repetition penalty and a high stopping probability threshold.

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

3. You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

A) Increase the model's temperature to allow for more diverse outputs.
B) Implement a post-processing step that filters for factual accuracy using a knowledge base.
C) Reduce the maximum token limit and set a higher repetition penalty.
D) Use beam search with a high beam width to generate the most likely sequence.

4. You are implementing a Retrieval-Augmented Generation (RAG) system using IBM Watsonx to improve your generative AI model. The system retrieves relevant information from a large corpus and augments it into the generative process.
In this context, what role do embeddings play in a RAG-based system?

A) Embeddings ensure that only syntactically correct documents are retrieved, without regard to semantic content.
B) Embeddings reduce the size of the generative model by compressing the parameters into a smaller representation.
C) Embeddings are used to retrieve relevant documents by calculating the semantic similarity between user queries and the stored documents.
D) Embeddings store the entire content of documents, which is then directly passed to the generative model.

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

Solutions: