IBM C1000-185 Exam : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with creating a prompt-tuned model using IBM watsonx.ai to enhance the quality of text generation for customer support. The goal is to fine-tune the model for improved context understanding based on specific customer queries.
Which of the following approaches would be the best method to initialize the prompt for tuning?

A. Use a manually crafted prompt tailored to the specific context of customer support queries
B. Construct a prompt using a large set of random tokens from the training corpus
C. Use a pre-trained general-purpose prompt with no domain-specific customization
D. Use a prompt with pre-defined output patterns to restrict the model's possible responses

Question 2

You are working on generating creative text responses using IBM watsonx's generative AI model. You need to adjust the output so that it is more diverse and creative without losing coherence.
Which of the following model parameter settings would best achieve this objective?

A. Set temperature to 2
B. Set temperature to 1.5
C. Set temperature to 0
D. Set temperature to 0.2

Question 3

Your team has developed an AI model that generates automated legal documents based on user inputs. The client, a large law firm, wants to deploy this model but has stringent security, compliance, and auditability requirements due to the sensitive nature of the data.
What is the most appropriate deployment strategy to meet these specific requirements?

A. Deploy the model using a serverless architecture to minimize operational overhead and maintain compliance.
B. Deploy the model on a public cloud with built-in encryption and use APIs to connect to the client's private data.
C. Use a private cloud with role-based access controls (RBAC) and ensure model activity is logged for auditing purposes.
D. Deploy the model on a hybrid cloud, with inference done on the client's on-premise servers and training done in the public cloud.

Question 4

You are tasked with explaining the outcomes produced by a Watsonx Generative AI model based on specific prompts.
Which of the following approaches is most effective in ensuring transparency and understanding of how the model arrives at its decisions?

A. Explaining the optimization process that minimized the model's loss function
B. Providing an interpretable

Question 5

A client needs a Generative AI solution to summarize large legal documents into concise briefs. The solution must capture the critical legal arguments while preserving the formal language required in legal contexts. Additionally, the client wants the model to identify key legal clauses and ensure their inclusion in the summaries. You have a pre-trained LLM that was trained on general text, and now you must design a generative solution to meet the client's needs.
What would be your next step in analyzing and designing the most effective solution?

A. Apply model quantization to optimize the LLM for handling long legal documents more efficiently.
B. Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.
C. Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.
D. Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.

Solutions: