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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. 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 on a hybrid cloud, with inference done on the client's on-premise servers and training done in the public cloud.
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 using a serverless architecture to minimize operational overhead and maintain compliance.

2. In the context of analyzing prompt-tuning results, which statistical measure is most important to assess how well the tuned model generalizes to unseen data?

A) Training loss
B) Validation loss
C) Accuracy on the training dataset
D) Number of epochs completed

3. You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?

A) Decrease the temperature from 0.9 to 0.3
B) Set the temperature to 0.0
C) Decrease the temperature from 0.8 to 0.6
D) Increase the temperature from 0.5 to 1.5

4. You are developing a Retrieval-Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently.
Which of the following best describes the appropriate approach to text chunking for this RAG implementation?

A) Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.
B) Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.
C) Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.
D) Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.

5. During prompt engineering for IBM Watsonx, you need to understand how the decoding process works when generating responses.
Which of the following best describes a high-level overview of the decoding process in generative AI?

A) Decoding is the process where the model generates a response token-by-token, choosing each token based on the probability distribution over all possible tokens.
B) Decoding involves translating the input data into a format that the AI model can understand before generating an output.
C) Decoding occurs only in reinforcement learning, where the model refines its responses based on user feedback over multiple generations.
D) Decoding is the final step in training, where the AI model verifies the accuracy of its outputs against a predefined set of labels.

Solutions: