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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with building a question-answering system using IBM WatsonX's LLM integrated with LangChain. The system needs to retrieve relevant documents from a corpus of research papers and generate accurate, contextually aware responses.
Which of the following steps is most critical when implementing a RAG pattern in this environment?

A) Using WatsonX LLM to pre-process the research papers before storing them in LangChain's memory.
B) Fine-tuning WatsonX's LLM on the research papers before enabling retrieval through LangChain.
C) Configuring LangChain to use WatsonX's LLM for exact keyword matching during document retrieval.
D) Setting up the vector database to store embeddings of the research papers for semantic search.

2. You are tasked with designing a Retrieval-Augmented Generation (RAG) system using embeddings to improve the response quality of a generative AI model.
In this context, what are embeddings used for, and how do they contribute to enhancing the generative AI's performance?

A) Embeddings compress the input data to reduce computational load, improving the efficiency of the retrieval and generation process.
B) Embeddings transform the input data into high-dimensional vectors, capturing semantic similarities between the input query and potential retrieval candidates to provide contextually relevant information for the generative model.
C) Embeddings serve as a form of knowledge storage within the generative model, allowing it to answer questions without retrieving external information.
D) Embeddings provide a summary of the input data, which the model then uses to generate its final output without retrieving external content.

3. You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?

A) Use Data Refinery's "Data Quality" feature to generate a profiling report and assess the scope of the issues.
B) Delete all rows containing null values in the 'ticket_description' field.
C) Proceed with training the model without any changes, as missing data will not impact NLP models significantly.
D) Replace all null values with zeros, as this will standardize the dataset.

4. You are tasked with preparing a dataset for training a machine learning model using IBM Watsonx. The dataset contains over 1 million rows, and you notice a significant imbalance in the distribution of class labels.
To optimize model performance and minimize bias, what would be the best next step in addressing this imbalance?

A) Remove the majority class instances to balance the dataset.
B) Increase the learning rate of the model to improve its ability to learn from the imbalanced data.
C) Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic data for the minority class.
D) Randomly shuffle the data to improve the model's exposure to different instances during training.

5. You are tasked with fine-tuning a large language model (LLM) to perform sentiment analysis on product reviews. The dataset contains customer reviews, but some reviews are very short, and others contain irrelevant data like product specifications or spam. You want to prepare the dataset for fine-tuning by ensuring the data is clean, relevant, and representative of the task at hand.
Which of the following steps is most critical to ensure the dataset is suitable for fine-tuning?

A) Increase the learning rate to adjust for inconsistencies in the dataset
B) Filter the dataset to remove irrelevant or outlier reviews
C) Increase the number of epochs to handle the noisy data
D) Randomly remove 10% of the dataset to reduce noise

Solutions: