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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. What is one primary advantage of using Prompt Lab in IBM Watsonx when evaluating prompt variations for a generative AI model?

A) Prompt Lab enables side-by-side comparisons of multiple prompt variations, allowing developers to evaluate different formulations in a systematic manner.
B) Prompt Lab allows users to lock the output for each prompt, ensuring deterministic results across different runs.
C) Prompt Lab automatically generates prompts based on the dataset, requiring minimal manual input from developers.
D) Prompt Lab automatically selects the best performing prompt based on predefined metrics without any user input.

2. You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A) Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
B) Use a comprehensive training dataset that includes diverse business domains to reduce biases.
C) Include fairness metrics in the model evaluation stage to monitor for biased outputs.
D) Implement a post-processing filter to remove any potentially offensive or legally sensitive content.

3. You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?

A) Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.
B) Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.
C) Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.
D) Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.

4. Which of the following best describes the effect of controlling model parameters during the decoding process in IBM Watsonx's generative AI models?

A) Setting model parameters guarantees that the model will generate the most grammatically correct response, regardless of context.
B) Model parameters control the model's ability to self-learn from previous outputs, improving its performance over time.
C) Controlling model parameters allows the model to generate only the shortest possible responses, ensuring concise outputs.
D) Adjusting model parameters helps control the randomness in the output generation process, enabling a balance between creativity and accuracy.

5. You are working as a generative AI engineer and have developed a custom large language model (LLM) optimized for a specific use case. You are tasked with deploying this model on the IBM Watsonx platform.
Which of the following steps is most essential to ensure the successful deployment of your custom model, given that the model uses a third-party transformer architecture?

A) Containerize the model using Docker or an equivalent containerization tool, ensuring that all required dependencies, such as transformers, tokenizers, and necessary packages, are included.
B) Modify the model to use IBM's proprietary transformer architecture, as third-party architectures are not supported by Watsonx.
C) Ensure that the model's training data is in a proprietary IBM format, as only Watsonx-specific formats are supported for custom model deployments.
D) Set up auto-scaling in the IBM Watsonx environment to handle large numbers of simultaneous model inference requests.

Solutions: