IBM watsonx Generative AI Engineer - Associate (C1000-185) Free Practice Test

Question 1

You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A. Manually categorize each interaction and organize them into a taxonomy tree structure.

B. Tokenize the dataset before curating it and mapping it to the taxonomy tree.

C. Convert all text to lowercase to ensure uniformity in the dataset.

D. Perform a spelling correction on the entire dataset to remove any language inconsistencies.

Correct Answer: A

Question 2

A client is deploying a watsonx Generative AI solution to analyze customer feedback in real time. They require a cost-effective solution that can handle occasional traffic spikes but do not expect constant heavy loads.
What would be the most appropriate approach to minimize costs while ensuring adequate performance during traffic spikes?

A. Deploy the model using a serverless architecture with auto-scaling to handle sudden spikes in traffic.

B. Deploy a cloud-based instance with manual scaling to adjust resources when traffic spikes occur.

C. Deploy a single powerful GPU instance, relying on its ability to handle both regular and peak traffic without scaling.

D. Use a fixed number of on-premise servers that can handle peak traffic, but run them at a lower capacity during low-demand periods.

Correct Answer: A

Question 3

You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?

A. Increase the number of positive sentiment examples in the dataset

B. Ensure an equal distribution of positive, negative, and neutral sentiment examples

C. Convert neutral examples into either positive or negative to simplify the task

D. Focus only on neutral and negative examples to challenge the model

Correct Answer: B

Question 4

In the context of avoiding abusive or profane content generation, which of the following prompt engineering techniques is most likely to reduce the risk of model misuse?

A. Add disclaimers to the prompt input, asking the model to avoid producing harmful or offensive content.

B. Ask users to label their own prompts as "safe" or "unsafe" before passing them through the model.

C. Utilize sentiment analysis on generated outputs to detect harmful language and re-prompt the model if necessary.

D. Enforce the use of strictly neutral or factual language in all prompts.

Correct Answer: C

Question 5

You are developing a Retrieval-Augmented Generation (RAG) system to enhance the responses of a legal chatbot by integrating it with a vast legal document repository. You are using LangChain to build the pipeline, Watson ML for model hosting, and Elasticsearch as your document store.
What would be the most appropriate approach for combining these components into a RAG pipeline?

A. Use LangChain to chain together query encoding, document retrieval from Elasticsearch, and Watson ML for response generation.

B. Use LangChain to pre-process documents -> Use Elasticsearch for model storage -> Use Watson ML to retrieve documents and generate responses.

C. Use Elasticsearch for document retrieval -> Use LangChain to encode the documents -> Generate the response using Watson ML.

D. Use Watson ML for document retrieval and response generation -> Use Elasticsearch to store model responses -> Use LangChain for chaining the responses together.

Correct Answer: A

Question 6

You are using InstructLab to fine-tune a large language model (LLM) for generating technical documentation. The model's output is inconsistent, sometimes too verbose and other times lacking critical details.
Which of the following actions within InstructLab will best help customize the model to consistently produce balanced, concise, yet informative outputs?

A. Lower the batch size during fine-tuning to force the model to focus on smaller chunks of data

B. Use prompt engineering to provide more explicit instructions for the model

C. Adjust the token length limit in the model's configuration

D. Increase the number of fine-tuning epochs to ensure the model converges

Correct Answer: B

Question 7

IBM Watsonx's Prompt Lab offers various options to refine prompts for generating more effective AI outputs.
Which of the following is an accurate description of an editing option available in Prompt Lab?

A. Prompt Lab allows users to experiment with prompt structures, such as adjusting token limits or adding contextual instructions, to improve responses.

B. Users can apply real-time machine learning to modify the underlying model parameters within Prompt Lab.

C. Users can use Prompt Lab to train the AI model on new datasets and retrain it based on prompt performance.

D. Users can disable the model's access to certain pre-trained knowledge domains within Prompt Lab to focus its output on specific areas.

Correct Answer: A

Question 8

After prompt-tuning a generative AI model, you review its performance on multiple evaluation metrics. The metrics include accuracy, perplexity, and latency.
Which combination of these metrics would most effectively allow you to optimize the model for both user experience and content quality?

A. High accuracy, low perplexity, low latency

B. High accuracy, high perplexity, high latency

C. Low accuracy, high perplexity, low latency

D. High accuracy, low perplexity, high latency

Correct Answer: A

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IBM watsonx Generative AI Engineer - Associate (C1000-185) Free Practice Test